The Long-Run Determinants of Indian - IA de Investigación especializada en el MIT

The Long-Run Determinants of Indian
Government Bond Yields
Tanweer Akram and Anupam Das∗

This paper investigates the long-term determinants of the nominal yields
of Indian government bonds (IGBs). It examines whether John Maynard
Keynes’ supposition that the short-term interest rate is the key driver of the
long-term government bond yield holds over the long run, after controlling for
key economic factors. It also appraises if the government ﬁscal variable has
an adverse effect on government bond yields over the long run. The models
estimated in this paper show that in India the short-term interest rate is the
key driver of the long-term government bond yield over the long run. Sin embargo,
the government debt ratio does not have any discernible adverse effect on IGB
yields over the long run. These ﬁndings will help policy makers to (i) usar
information on the current trend of the short-term interest rate and other
key macro variables to form their long-term outlook about IGB yields, y
(ii) understand the policy implications of the government’s ﬁscal stance.

Palabras clave: government bond yields, India, interest rates, monetary policy
JEL codes: E43, E50, E60, G10, O16

I. Introducción

John Maynard Keynes (1930) contends that the central bank’s monetary
policy is the most important driver of the long-term interest rate. He believes
that the central bank’s actions inﬂuence the long-term interest rate primarily
through the effect of policy rates on the short-term interest rate and other tools
of monetary policy. In The General Theory of Employment, Interest, and Money,
Keynes (2007 [1936]) reiterates the importance of the central bank’s inﬂuence
on the long-term interest rate, even though he acknowledges that interest rates
have psychological, social, and conventional foundations, and arise from investors’
liquidity preferences.

∗Tanweer Akram (Autor correspondiente): Director of Global Public Policy and Economics, Thrivent Financial,
Mineápolis, United States. Correo electrónico:
tanweer.akram@thrivent.com; Anupam Das: Professor, Department of
Ciencias económicas, Justicia, and Policy Studies at Mount Royal University, Alberta, Canada. Correo electrónico: adas@mtroyal.ca. El
authors’ institutional afﬁliations are provided for identiﬁcation purposes only. The views expressed are solely those
of the authors and are not necessarily those of Thrivent Financial, Thrivent Asset Management, or any of its afﬁliates.
This is for information purposes only and should not be construed as an offer to buy or sell any investment product
or service. The authors would like to thank the managing editor and two anonymous referees for helpful comments
and suggestions. The usual ADB disclaimer applies.

Asian Development Review, volumen. 36, No. 1, páginas. 168–205
https://doi.org/10.1162/adev_a_00127

© 2019 Asian Development Bank and
Asian Development Bank Institute.
Publicado bajo Creative Commons
Atribución 3.0 Internacional (CC POR 3.0) licencia.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 169

This paper examines whether Keynes’ supposition that the short-term interest
rate is the key driver of the long-term government bond yield holds in India over
the long run after controlling for various key economic factors, such as inﬂationary
pressure and measures of economic activity. It also appraises if government ﬁscal
variables, such as the ratio of government debt to nominal gross domestic product
(PIB), have an adverse long-run effect on government bond yields in India. Akram
and Das (2015a and 2015b) report that Keynes’ conjectures hold in India for the
short-run horizon. They also ﬁnd that government ﬁscal variables do not appear
to exert upward pressure on Indian government bond (IGB) yields. Sin embargo, ellos
do not examine if these results hold over a long-run horizon. This paper ﬁlls that
critical lacunae.

Understanding the determinants of government bond yields in India over the
long-run horizon is important not just for scholarly reasons but also for policy
purposes and policy modeling, particularly for discerning the effects of ﬁscal
and monetary policy on IGB yields. Understanding the drivers of government
bond yields in emerging markets such as India has crucial
implications for
the government’s ﬁscal and macroeconomic policy mix. It is also relevant for
ﬁxed income investment and portfolio allocation, as well as the management of
government debt.

India’s institutional features, its economic rise, and the evolution of its
ﬁnancial system make it worthwhile to examine the long-run trends in its
government bond market. Primero, India’s ﬁnancial markets are in the development
stage. While India has liberalized its economy and many aspects of its ﬁnancial
sistema, there are still various restrictions. Its bond market is not as deep as those
of advanced capitalist economies such as Japan, the United Kingdom, y el
United States (US). The country’s banking system is dominated by state-owned or
state-controlled ﬁnancial institutions, and its ﬁxed income investors in the local
currency bond market are largely conﬁned to investing in government securities
since the depth and liquidity of corporate bonds and other ﬁxed income securities
are limited. Es, hence, appropriate to inquire whether Keynes’ supposition
regarding the link between the short-term interest rate and the long-term interest
rate holds in the institutional and structural circumstances of emerging market
economies such as India. Segundo, whether the central bank’s setting of the policy
tasa(s) and other monetary policy actions inﬂuence the long-term interest rate over
the long run in India has meaningful policy implications for monetary transmission
mechanisms. If the evidence suggests that the central bank can decisively affect
the long-term interest rate, not just in the short run but also over the long run,
this would show that the Government of India has considerable policy space. If no
such relationship can be established, then this would mean that its policy space is
rather restrictive and narrow. Por eso, it is important to examine what conjectures are
empirically warranted in India and other emerging markets.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

170 Asian Development Review

The paper is organized as follows. Section II sets the foundation for the
empirical investigation. Primero, it discusses Keynes’ view on interest rates and
provides the theoretical framework. Segundo, it summarizes Keynes’ stance on the
loanable funds theory and explains why he rejects this theory. Tercero, it presents a
simple two-period model of government bond yields. Cuatro, it recounts the stylized
facts about government bond yields and government debt ratios. Quinto, it brieﬂy
reviews the relevant literature on government bond yields in emerging market
economías. Section III describes the data, the behavioral equations to be estimated,
and the econometric methodology applied here. Section IV reports the empirical
ﬁndings. Section V analyzes the policy implications of the results and concludes.
Apéndice 1 presents the details of the simple two-period model of government bond
yields used in the paper. Apéndice 2 presents additional regressions to examine the
effects of credit growth, global investors’ risk appetite, and the nominal effective
exchange rate on government bond yields.

II. Theoretical Framework, Modelo, Institutional Background, Stylized Facts,

and Brief Review of the Literature

The Keynesian Framework

This paper investigates the long-run determinants of IGB yields based on
Keynes’ (1930 y 2007 [1936]) puntos de vista. Keynes holds that the central bank’s actions
play the decisive role in setting the long-term interest rate on government bonds
(Kregel 2011). He argues against the classical view of interest rates based on the
loanable funds theory as represented in Cassel (1903), marshall (1890), Taussig
(1918), and the classical economists.

The central bank’s ability to inﬂuence the long-term interest rate arises from
its ability to set the policy rate and anchor the short-term interest rate around the
policy rates, and to use various other tools of monetary policy (Keynes 1930). Él
acknowledges that interest rates have a foundation based on human psychology,
social conventions, herd mentality, and liquidity preferences (Keynes 2007 [1936]).
Sin embargo, the most immediate and important driver of long-term government
bond yields are the central bank’s actions as manifested through its ability to
(i) inﬂuence the short-term interest rate by setting the policy rate, y (ii) use a wide
range of tools of monetary policy including expanding and contracting its balance
sheet as it deems appropriate. Keynes relies on Rieﬂer’s (1930) pioneering empirical
analysis of the behavior of interest rates on US government securities (Kregel 2011).
He also observes that current conditions and the investor’s near-term outlook affect
the investor’s long-term outlook. Keynes believes that since the investor does not
have a ﬁrm basis for estimating the mathematical expectations of the unknown and
uncertain future, the investor resorts to forming an outlook of the future based on

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 171

past and current conditions. Como resultado, the factors that affect the short-term interest
rate also affect the long-term interest rate.

Keynes’ view on the drivers of long-term government bond yields is
in contrast to that of conventional views in macroeconomics and ﬁnance. El
conventional view is that government debts and deﬁcits have a decisive effect on
government bond yields. Other things held constant, if government debts and/or
government deﬁcits (both as a share of nominal GDP) increase (decrease), entonces
government bond yields will rise (decline). This view relies on the loanable funds
theory of interest rates. For Keynes, liquidity preferences and the central bank’s
actions are largely responsible for interest rates as manifested in the yield curve
for gilt-edged (gobierno) securities and other ﬁxed income instruments in an
economía.

Among others, Ardagna, Caselli, and Lane (2007); Baldacci and Kumar
(2010); Gruber and Kamin (2012); Lam and Tokuoka (2013); Poghosysan (2014);
and Tokuoka (2012) represent the conventional view. A diferencia de, Akram (2014);
Akram and Das (2014a, 2014b, 2015a, 2015b, 2017a, 2017b, and 2017c); y
Akram and Li (2016, 2017a, and 2017b) have argued that the short-term interest
rate and pace of inﬂation are the key drivers of interest rates on government
bonds. Además, they argue that if other things are held constant, el gobierno
ﬁscal variable has hardly any inﬂuence on government bond yields. This view is
based on their interpretation of Keynes. It is supported with empirical work on the
determinants of government bond yields in the eurozone, India, Japón, and the US.
As mentioned earlier, Akram and Das’ (2015a and 2015b) empirical work on India
has merely explored the short-run dynamics. This paper examines whether the same
hypothesis holds true for India in the long run.

Keynes’ Stance on the Loanable Funds Theory of Interest Rates

Keynes rejected the loanable funds theory of interest rates. According to
the proponents of this theory, the interest rate is primarily determined by the
demand and supply of loanable funds. The loanable funds theory has a distinguished
pedigree. It is endorsed in classical economics such as Cassel (1903), Böhm-Bawerk
(1959), Hayek (1933 y 1935), marshall (1890), Pigou (1927), Ricardo (1817),
von Mises (1953), and Wicksell (1962 [1936]). Keynes rejects the loanable funds
theory because he believes it is insufﬁcient to determine interest rates solely on
the basis of knowledge of the demand for investment and the supply of savings.
He criticizes the loanable funds theory for neglecting the roles of national income,
the marginal propensity to consume, and liquidity preference in the determination
of interest rates. In his view, the “rate of interest is the reward for parting with
liquidity for a speciﬁed time” (Keynes 2007 [1936], pag. 167). It follows that the
interest rate is “a measure of the unwillingness of those who possess money to part
with their liquid control over it.” Liquidity preference is quite central to Keynes’

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

172 Asian Development Review

view on the interest rate. Liquidity preference arises from fundamental uncertainty
about future economic and ﬁnancial conditions, and the divergence among investors
about their outlook for the future. Interest rates have institutional and behavioral
foundations. Por eso, for Keynes, institutions like the central bank and investors’
psychology and social orientation, as manifested in herding and the formation of
long-term expectations, play decisive roles in the determination of the interest rate,
rather than just the demand and supply of loanable funds. The demand and the
supply of loanable funds are outcomes of income, the propensity to consume, y
liquidity preference, which occur within a context that consists of institutions, semejante
as the central bank, and amid investors’ psychology that is guided by animal spirits,
instincts, and social conventions.

A Simple Two-Period Model of Government Bond Yields

A simple model, based on Akram and Das’ (2014 y 2015) and Akram
and Li’s (2016 and 2017a) interpretations of Keynes’ views, is presented here to
show the connection between the current short-term interest rate and the long-term
interest rate.

To simplify the exposition, a two-period horizon is used. There are two
periods: t= 1, 2. The long-term interest rate on a government bond in period 1
is rLT ; the short-term interest rates on a Treasury bill in period 1 and period 2
son, respectivamente, r1 and r2; the expected short-term interest rate in period 2 is Er2;
the 1-year, 1-year forward rate is f1,1; the term premium is z; the current rate of
inﬂation in period 1 is π1; the actual rate of inﬂation in period 2 is π2; the expected
rate of inﬂation in period 2 is Eπ2; the current growth rate in period 1 is g1; el
actual growth rate in period 2 is g2; the expected growth rate in period 2 is Eg2; el
government ﬁscal variable in period 1 is ν1; the government ﬁscal variable in period
2 is ν2; and the expected government ﬁscal variable in period 2 is Ev2.

It can be shown that the long-term interest rate is a function of either (i) el
short-term interest rates in period 1 and period 2, and the growth rate and the rate
of inﬂation in period 2; o (ii) the short-term interest rates in period 1 and period
2, and the growth rate, the rate of inﬂation, and the government ﬁscal variable in
período 2. Por eso, the models of the determinants of the long-term bond yields take
the following forms:

rLT = F 7 (r1, r2, g2, π2)
rLT = F 8 (r1, r2, g2, π2, ν2)

(1)

(2)

A detailed derivation of the above models is presented in Appendix 1.

It is appropriate to incorporate the government ﬁscal variable in the model
of the long-term interest rate for several reasons. Primero, government ﬁscal variables

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 173

affect the long-term interest rate in the standard IS–LM Keynesian models. Segundo,
it is also included in the standard theoretical and empirical literature, incluido
Ardagna, Caselli, and Lane (2007); Baldacci and Kumar (2010); and other studies
cited in section II.A. Tercero, since the paper assesses whether Keynes’ conjecture
regarding the importance of the short-term interest rate in driving the long-term
interest rate is more warranted than that of the conventional view, it is necessary
to empirically estimate the effect of government ﬁscal variables on the long-term
interest rate. Ruling out, a priori, the role of the government ﬁscal variable on the
long-term interest rate would be arbitrary and could be regarded as an ad hoc and
unjustiﬁed maneuver. Undoubtedly, the empirical ﬁndings of this and other studies
that ﬁnd support for the Keynesian perspective can inﬂuence the choice of variables
in the construction of models of the long-term interest rate in the future.

Institutional Background

Akram and Das (2015a and 2015b) provide the institutional background
to the monetary policy framework, the government bond market, and monetary–
ﬁscal coordination in India. Yanamandra (2014) gives additional perspective on
monetary policy making in India in light of economic reforms, modernization,
and recent developments, while Chakraborty (2016) provides a detailed description
and analysis of the country’s monetary–ﬁscal policy mix and monetary–ﬁscal
coordination. Jácome et al.’s (2012) survey of global practices among central
banks in extending credit and coordinating with the national Treasury includes a
description of Indian laws, regulations, and practices related to its Treasury and
central bank.

India enjoys monetary sovereignty as deﬁned by Wray (2012). El
Government of India issues its own currency, the rupee. The country’s central
bank, the Reserve Bank of India (RBI), sets the policy rates and can use a wide
range of monetary policy tools. The RBI enjoys a wide range of authority and
control over the country’s ﬁnancial system. The Government of India has the
legal and political authority to collect taxes from households, negocios, ﬁnancial
institutions, and other organizations. The country’s sovereign debt is predominantly
issued in its own currency, the rupee. The multifaceted roles played by the RBI in the
payment system, monetary policy, ﬁnancial stability policy, and policy coordination
with the Treasury gives it the operational ability to inﬂuence government bonds’
nominal yields by setting and changing the short-term interest rate and using
other tools of monetary policy as it deems appropriate. RBI (2014) provides a
detailed institutional description of the IGB market, while RBI (various years)
Annual Reports give useful summaries of the central bank’s monetary policy and
fondo. El 2009 report presents a valuable perspective on the operational
aspects of monetary–ﬁscal coordination in India.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

174 Asian Development Review

Cifra 1. The Evolution of 10-Year Government Bond Yields in Selected Emerging
Market Economies

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

Fuente: Macrobond. Various years. Macrobond subscription services (consultado en septiembre 13, 2017).

mi.

Stylized Facts

A set of ﬁgures are presented in this section to highlight important stylized
facts related to IGBs and government ﬁnance. Cifra 1 compares the evolution of
10-year government bond yields in India with that of other major emerging markets,
such as Brazil, México, the People’s Republic of China, the Russian Federation, y
South Africa. It shows that since the global ﬁnancial crisis, government bond yields
in India have been generally higher than in the People’s Republic of China and
México, but lower than in Brazil. Government bond yields in the Russian Federation
and South Africa have been more volatile than those in India. En años recientes, como
commodity prices tumbled, ﬁnancial ﬂows to emerging markets weakened, and their
central bank policy rates increased, and government bond yields in the Russian
Federation and South Africa rose.

Cifra 2 shows the evolution of key government ﬁscal variables in India
como el (i)
ratio of gross government debt to nominal GDP, (ii) ratio of
government ﬁscal balance to GDP, y (iii) 10-year government bond yield. Él
shows that the government debt-to-GDP ratio rose from 70% to nearly 85% en el
early 2000s, but subsequently declined to around 70% as the country’s annual ﬁscal

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 175

Cifra 2. The Evolution of Key Government Fiscal Variables in India

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

GDP = gross domestic product.
Fuente: Macrobond. Various years. Macrobond subscription services (consultado en septiembre 13, 2017).

balance improved from a deﬁcit of around 11% of GDP in the early 2000s to a
deﬁcit of just 4% of GDP in the 2010s. Since the beginning of the 2010s, India’s
government debt ratio has been stable at around 70%, while its ﬁscal deﬁcit has
hovered around 7% of GDP. The ﬁgure also suggests that, prima facie, the evolution
of government bonds yields in India is not directly affected by government ﬁscal
condiciones.

Cifra 3 shows the evolution of the sector balances as a share of nominal
GDP in India. It uses annual ﬂow data to display (i) the government balance, (ii) el
private sector balance, y (iii) the current account balance. It visually shows that
the ﬂow of government dissaving is equal to private sector saving and the rest of the
world’s saving in Indian rupees.

Cifra 4 displays that the changing relationship between the credit default
swap (CDS) premium on IGBs and the spread between the nominal yields of
10-year IGBs and 10-year US Treasury notes since 2010. It shows that the
correlation can change drastically. Between 2010 y 2013, the CDS premium
and the yield spread were tightly correlated. Sin embargo, desde 2014, the correlation
between the CDS premium and the yield spread has been quite weak.

176 Asian Development Review

Cifra 3. The Evolution of Sector Balances in India

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

GDP = gross domestic product.
Fuente: Macrobond. Various years. Macrobond subscription services (consultado en septiembre 12, 2017).

Cifra 4. The Evolution of Credit Default Swap Premiums and Yield Spreads

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

CDS = credit default swap, IGB10Yr = 10-year Indian government bond yield, lhs = left-hand side, rhs = right-hand
lado, USD = United States (US) dollar, UST10Yr = 10-year US Treasury note yield.
Fuente: Macrobond. Various years. Macrobond subscription services (consultado en septiembre 12, 2017).

The Long-Run Determinants of Indian Government Bond Yields 177

A Brief Review of the Literature on Government Bond Yields

There is a substantial literature on government bonds yields, including on
the determinants of government bond yields in emerging markets such as India.
Sin embargo, the debate on the determinants of bond yields and the relative
importance of the key drivers is still unsettled.

We examine the ﬁndings of recent studies on government bond yields to
ascertain how relevant these are to the question that this paper addresses. Andritzky
(2012) provides a useful database on the investor base for government securities
and investigates the effect of the composition of the investor base on government
bond yields. Even though the study relies on G20 advanced economies and the
eurozone, a key ﬁnding appears to be relevant for emerging markets. An increase in
the share of bonds held by institutional or nonresidents by 10 puntos de porcentaje
is correlated with a decline in bond yields by about 25–40 basis points (bps).
Asonuma, Bakhache, and Hesse (2015) ﬁnd that an increase in domestic bank
holdings of government bonds reduces bond yields and provides ﬁscal space for the
sovereign authorities. Ebeke and Lu (2014) argue that the rise in foreign holdings
of local currency government bonds in emerging markets has led to a decline
in bond yields but a rise in their volatility, particularly since the global ﬁnancial
crisis. Acharya and Steffen (2015) provide an insightful analysis of the cause of the
divergence of bond yields between the core of the eurozone and its periphery. Ellos
also discuss the vital role played by the “carry trade” of eurozone banks in causing
the widening of the spread. The results of Ardagna, Caselli, and Lane (2007) are in
line with the conventional wisdom cited earlier in the introduction. They claim that
an increase of 1 percentage point in the ratio of the primary deﬁcit leads to (i) un
increase in the current long-term interest rate by 10 bps and (ii) cumulative increases
in the long-term interest rate by 150 bps after 10 años. These and other results in
the conventional literature on government bond yields are interesting. Sin embargo, el
conventional literature does not probe sufﬁciently the key role of the central bank
in inﬂuencing government bond yields in emerging markets. Por eso, a Keynesian
perspective may provide a more insightful analysis of the decisive factors and may
be more pertinent for understanding government bond yields in India.

This view is reinforced by the empirical literature on IGBs, which largely
refutes the conventional view that higher (más bajo) government debt or government
deﬁcits induce higher (más bajo) government bond yields. Chakraborty’s (2016)
detailed and careful institutional and empirical study ﬁnds that there is no evidence
of any link between ﬁscal deﬁcit and interest rates in India. Vinod, Chakraborty,
and Karun (2014) use the maximum entropy bootstrap method and report that the
government ﬁscal deﬁcit ratio is not signiﬁcant for interest rate determination in
India. Chakraborty (2012), applying asymmetrical vector autoregressive models,
ﬁnds that an increase in the ﬁscal deﬁcit ratio does not lead to a rise in interest rates.
Akram and Das (2015a and 2015b) show that changes in the short-term interest rate,

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

178 Asian Development Review

after controlling for other crucial variables such as changes in the rates of inﬂation
and economic activity, take a lead role in driving the changes of the nominal yields
of IGBs. Additional results show that higher ﬁscal deﬁcits do not appear to exert
upward pressures on government bond yields. Findings from Akram and Das (2015a
and 2015b) son, sin embargo, valid solely for the short run. One of the important goals
of the current paper is to examine if the ﬁndings from Akram and Das (2015a and
2015b) hold over the long-run horizon.

The next section introduces behavioral equations, time series data, y
econometric methods to examine the role of the short-term interest rate, the rate of
inﬂation, the government ﬁscal variable, and other key macroeconomics variables
to determine the nominal yields on IGBs over the long-run horizon.

III. Datos, Behavioral Equations, and Methods

Data1

For the purpose of econometric estimations, time series data on the nominal
yields of long-term IGBs, the short-term interest rate, the rate of inﬂation, el
growth of industrial production, and government ﬁscal variables are used.

Nominal yields on Indian Treasury bills with 3-month maturities are used
for the short-term interest rate, while the nominal yields on IGBs of various
tenors—including 2-year, 3-año, 5-año, 7-año, and 10-year maturities—are
used to represent long-term government bond yields. The RBI (2014) classiﬁes
government securities with a maturity of less than 1 year as short-term securities,
and those with a maturity of 1 year or more as long-term securities.

Cifra 5 shows the evolution of nominal yields of IGBs. Cifra 6 muestra
the evolution of the short-term interest rate along with the RBI’s policy rates (repo
rates and reverse repo rates). The rate of inﬂation is deﬁned as the year-on-year
percentage change in the total consumer price index for all items. Growth in
industrial production is the year-on-year percentage change in the index of industrial
activity in India. The ratio of government debt to nominal GDP is used here as
the government ﬁscal variable. The ratio of private sector credit (from all sectors)
to nominal GDP is used to measure credit growth. The Institute for International
Monetary Affairs’ index of the volatility in global bond markets is a proxy for global
investors’ risk appetite. An increase (decrease) in volatility in global bond markets
means that investors’ perception of and appetite for risk has risen (declined).
The nominal effective exchange rate, calculated by the Bank for International
Settlements, is the exchange rate used here. The data of all the variables are
collected from Macrobond’s (various years) data services. Mesa 1 provides a

1The dataset used in the empirical part of this paper is available upon request to bona ﬁde researchers for the

replication and veriﬁcation of the results.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 179

Cifra 5. The Evolution of Indian Government Bond Yields of Selected Tenors

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

Fuente: Macrobond. Various years. Macrobond subscription services (accessed July 12, 2017).

Cifra 6. The Evolution of Policy Rates and Short-Term Interest Rates in India

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Fuente: Macrobond. Various years. Macrobond subscription services (accessed July 12, 2017).

180 Asian Development Review

Mesa 1. Summary of the Data and Variables

Variable
Labels

Data Description, Date Range

Frecuencia

Fuentes

Indian Short-Term Interest Rates
TB3M;

TB3M_Q

Government benchmarks,
auction rate, 3-mes
% producir; Jan 1999–Oct 2015;
Q1 1999–Q3 2015

Daily; converted to

Reserve Bank of India;

mensual

Macrobond converted to
quarterly

Indian Government Bond Yields
IGB2YR;

Government bond, 2-año

IGB2YR_Q

% producir; Mar 2003–Oct 2015;
Q2 2003–Q3 2015

Daily; converted to

mensual

IGB3YR;

Government bond, 3-año

Daily; converted to

IGB3YR_Q

% producir; Mar 2003–Oct 2015;
Q2 2003–Q3 2015

mensual

IGB5YR;

Government bond, 5-año

IGB5YR_Q

% producir; Mar 2003–Oct 2015;
Q2 2003–Q3 2015

Daily; converted to

mensual; converted
to quarterly

Clearing Corporation of

India; Macrobond
converted to quarterly
Clearing Corporation of

India; Macrobond

IGB7YR;

Government bond, 7-año

Daily; converted to

Clearing Corporation of

IGB7YR_Q

% producir; Mar 2003–Oct 2015;
Q2 2003–Q2 2015

mensual; converted
to quarterly

India; Macrobond

IGB10YR;

Government bonds, 10-año

Daily; converted to

Clearing Corporation of

IGB10YR_Q

Inﬂation
TCPIYOY;

TCPIYOY_Q

% producir; Jan 1999–Oct 2015;
Q1 1999–Q2 2015

mensual; converted
to quarterly

India; Macrobond

India, consumer price index,

Monthly; converted

total, % cambiar, year on year;
Jan 2007–Oct 2015;
Q1 2007–Q2 2015

to quarterly

The Economist;
Macrobond

Economic Activity
IPIYOY;

Industrial production,

IPIYOY_Q

% cambiar, year on year;
Jan 1999–Oct 2015; Q1 1999–
Q2 2015

Government Fiscal Variable
DRATIO_Q

Government debt, % of nominal

PIB; Q1 1999–Q2 2015

Monthly; converted

Central Statistical

to quarterly

Organisation, India;
Macrobond

Quarterly

Indian Ministry of

Commerce and Industry;
Macrobond

Bank for International

Settlements; Macrobond

Credit Growth
CREDIT

Credit from all sectors to the
private sector, % of nominal
PIB; Jan 1999–Dec 2015

Quarterly; converted
to monthly using
cubic interpolation

Investors’ Risk Appetite
RISK

Global bond market volatility
índice; Jan 1999–Dec 2015

Daily; converted to

Institute for International

mensual

Monetary Affairs;
Macrobond

Exchange Rate
NEER

Nominal effective exchange rate

Monthly

índice, amplio; Jan 1999–
Dec 2015

Bank for International

Settlements; Macrobond

GDP = gross domestic product, Q = quarter.
Fuente: Authors’ compilation.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 181

summary of the data and detailed descriptions of the variables. The monthly
time series dataset runs from March 1999 to October 2015, while the quarterly
dataset includes time series variables from the third quarter of 2003 to the second
quarter of 2015.

Both monthly and quarterly data are used to examine the determinants of
nominal yields of long-term government bonds. Indian government ﬁscal data is
available only in quarterly form. Por eso, the debt-to-GDP ratio is included only in
the quarterly equations.

Behavioral Equations

A set of behavioral equations for monthly data and for quarterly data are
constructed in concordance with the model based on the Keynesian framework
presented earlier. These behavioral equations readily lend themselves to empirical
pruebas. The speciﬁc-to-general approach is deployed here. For the monthly dataset,
the long-term government bond yields are ﬁrst regressed individually with the
short-term interest rate, inﬂation, and the growth rate of industrial production.
The dependent variables are then regressed with the short-term interest rate and
inﬂation, and the short-term interest rate and growth rate. In the general form
of the behavioral equation, the long-term interest rate is determined by all three
explanatory variables including the short-term interest rate, rate of inﬂation, y
growth rate. The general equation takes the following form:

rLT = α1 + α2r1 + α3π1 + α4g1

(3)

The same approach is used when the quarterly dataset is employed to
examine the determinants of long-term bond yields in India. Sin embargo, to understand
the effects of the government ﬁscal variable on government bond yields, the ratio
of government debt to nominal GDP is included in the general equation of the
quarterly dataset. Por eso, the behavioral equation can be written in the following
manner:

rLT = z1 + z2r1 + z3π1 + z4g1 + z5v1

(4)

Econometric Methodology

The ﬁrst step is to examine the nature of the data. The presence of unit roots
in most macroeconomic variables is fairly common (Nelson and Plosser 1982).
Por eso, estimating the long-run relationships of stationary variables using standard
cointegration techniques (p.ej., Johansen cointegration) is inconsistent. Por lo tanto,
unit root tests on the variables used in this paper are imperative. Conventional
research has used both the Augmented Dickey–Fuller (ADF) (Dickey and Fuller
1979, 1981) and the Phillips–Perron (PÁGINAS) (Phillips and Perron 1988) tests to

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

182 Asian Development Review

Mesa 2. Unit Root Tests for Monthly Variables

Variable

DFGLS
−1.29
−1.76*
−1.26
−2.01**
−1.26
−2.44**
−1.27
−2.74***
−1.57
−2.15**
−1.63*
−9.47***
−1.92*
−0.97
0.30
−0.98
0.48
−0.79*
−4.93***
−0.97

ADF
−1.72
−11.57***
−1.81
−7.60***
−1.95
−7.87***
−2.06
−7.96***
−2.57
−17.09***
−1.89
−9.51***
−4.67***
−9.73***
−1.54
−2.48
−0.52
−11.21***
−4.93***
−17.18***

PÁGINAS
−1.86
IGB2YR
−11.57***
(cid:5)IGB2YR
−1.97
IGB3YR
(cid:5)IGB3YR
−11.54***
−2.03
IGB5YR
−11.38***
(cid:5)IGB5YR
−2.06
IGB7YR
−11.18***
(cid:5)IGB7YR
−2.58
TB3M
−17.13***
(cid:5)TB3M
−1.99
TCPIYOY
−9.48***
(cid:5)TCPIYOY
−13.66***
IPIYOY
−47.57***
(cid:5)IPIYOY
−1.64
CREDIT
−6.99***
(cid:5)CREDIT
−0.27
NEER
−11.04***
(cid:5)NEER
−4.86***
RISK
−19.01***
(cid:5)RISK
ADF = Augmented Dickey–Fuller, CREDIT = credit to the private sector as percentage of
PIB, DFGLS = Dickey–Fuller Generalized Least Squares, IGB2YR = 2-year government
bond yield, IGB3YR = 3-year government bond yield, IGB5YR = 5-year government bond
producir, IGB7YR = 7-year government bond yield, IPIYOY = year-on-year percentage change
in industrial production, NEER = nominal effective exchange rate, PP = Phillips–Perron,
RISK = global bond market volatility index, TB3M = 3-month government auction rate,
TCPIYOY = year-on-year percentage change in consumer price index.
Notas: ***, **, y * indicate statistical signiﬁcance at 1%, 5%, y 10% niveles, respectivamente.
The null hypothesis of all three tests is that the series contains unit roots.
Fuente: Authors’ calculations.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

identify the existence of unit roots. eliot, Rothenberg, and Stock (1996) propuesto
the Dickey–Fuller Generalized Least Square (DFGLS) prueba, which is a modiﬁed
version of the standard ADF test. According to the DFGLS procedure, the data are
detrended before testing for stationarity. Different versions of the ADF, PÁGINAS (con
no constant and trend, constant and no trend, and constant and trend), and DFGLS
pruebas (with constant but without trend, and constant and trend) are applied in this
paper. All of these versions produce similar results. Due to space constraints, solo
the results with constant but without trend are presented here. All remaining results
are available upon request.2 Unit root results for monthly variables are displayed
en mesa 2 and the results for quarterly variables are displayed in Table 3. Para el
monthly dataset, most variables are nonstationary at levels and stationary at the ﬁrst
diferencia. The year-on-year percentage change in consumer price index is found to
be nonstationary at levels and stationary at the ﬁrst difference by two out of three

2For additional results, the interested reader may want to consult the working paper version (Akram and Das

2017a) of this study and/or contact the authors.

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 183

Mesa 3. Unit Root Tests for Quarterly Variables

Variable

DFGLS
−1.51
−6.10***
−1.60
−6.36***
−1.72*
−6.58***
−1.81*
−6.77***
−1.59
−1.87*
−1.93*
−6.46***
−1.70*
−6.55***
−1.27
−0.87

ADF
−2.05
−7.47***
−2.27
−8.06***
−2.54
−8.51***
−2.72
−6.81***
−2.16
−8.52***
−2.36
−6.56***
−4.64***
−6.53***
−2.21
−2.60*

PÁGINAS
−2.05
IGB2YR_Q
−7.48***
(cid:5)IGB2YR_Q
−2.14
IGB3YR_Q
(cid:5)IGB3YR_Q
−8.36***
−2.30
IGB5YR_Q
−9.59***
(cid:5)IGB5YR_Q
−2.47
IGB7YR_Q
−10.14***
(cid:5)IGB7YR_Q
−2.57
TB3M_Q
−8.60***
(cid:5)TB3M_Q
−2.44
TCPIYOY_Q
−6.65***
(cid:5)TCPIYOY_Q
−4.58***
IPIYOY_Q
−14.18***
(cid:5)IPIYOY_Q
−4.00***
DRATIO_Q
−11.21***
(cid:5)DRATIO_Q
ADF = Augmented Dickey–Fuller, DFGLS = Dickey–Fuller Generalized Least Squares,
DRATIO_Q = government debt as percentage of nominal gross domestic product,
IGB2YR_Q = 2-year government bond yield, IGB3YR_Q = 3-year government bond yield,
IGB5YR_Q = 5-year government bond yield, IGB7YR_Q = 7-year government bond yield,
IPIYOY_Q = year-on-year percentage change in industrial production, PP = Phillips–Perron,
TB3M_Q = 3-month government auction rate, TCPIYOY_Q = year-on-year percentage
change in consumer price index.
Notas: ***, **, y * indicate statistical signiﬁcance at 1%, 5%, y 10% niveles, respectivamente.
The null hypothesis of all three tests is that the series contains unit roots.
Fuente: Authors’ calculations.

pruebas. The year-on-year percentage change in industrial production (IPIYOY) y
the global bond market volatility index are stationary at levels. De este modo, most variables
are integrated of order one, I(1). All three tests suggest that IPIYOY is stationary at
niveles; eso es, I(0). Similar results are found for the quarterly variables. Government
bond as a percentage of GDP is found to be stationary at levels by the PP test,
and nonstationary at levels by the ADF and DFGLS tests. Por lo tanto, all quarterly
variables are either I(0) or I(1).

Given the results from the unit root tests, it is appropriate to estimate
the long-run cointegrating relationships using the autoregressive distributive lag
(ARDL) proposed by Pesaran and Shin (1998) and Pesaran, espinilla, and Smith
(2001). The ARDL bounds test approach is based on the ordinary least squares
estimation of a conditional unrestricted error correction model for cointegration
análisis. The ARDL technique is more appealing than the Johansen cointegration
técnica (Johansen and Juselius 1990) because the latter requires that the variables
are integrated of the same order of I(1). Sin embargo, the ARDL approach is not
constrained by the outcomes of unit root tests. It is applicable irrespective of
whether the regressors in the model are purely I(0), purely I(1), or mutually
cointegrated. In the present case, most variables are I(1) with the exception
of IPIYOY and DRATIO_Q (es decir., government debt as percentage of nominal

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

184 Asian Development Review

Mesa 4. Autoregressive Distributive Lag Bounds Test Results for
IGB2YR (monthly data)

Ecuación
4.1) IGB2YR = β 0 + β 1TB3M
4.2) IGB2YR = β 2 + β 3TCPIYOY
4.3) IGB2YR = β 4 + β 5IPIYOY
4.4) IGB2YR = β 6 + β 7TB3M + β 8TCPIYOY
4.5) IGB2YR = β 9 + β 10TB3M + β 11IPIYOY
4.6) IGB2YR = β 12 + β 13TB3M + β 14TCPIYOY + β 15IPIYOY

F-statistic

3.93
2.97
1.46
6.52**
2.99
4.81*

Variable

TB3M

TCPIYOY

IPIYOY

Constant

Time period

Number of observations

Long-Run Relationships

Ecuación 4.4

Ecuación 4.6

0.51***
(0.04)
−0.01
(0.04)
—

3.60***
(0.48)
Dec 2006–
Oct 2015
107

0.51***
(0.05)
−0.00
(0.04)
−0.00
(0.01)
3.60***
(0.54)
Feb 2007–
Oct 2015
105

IGB2YR = 2-year government bond yield, IPIYOY = year-on-year percentage change in
industrial production, TB3M = 3-month government auction rate, TCPIYOY = year-on-year
percentage change in consumer price index.
Notas: ***, **, y * representar 1%, 5%, y 10% levels of signiﬁcance, respectivamente. Estándar
errors are in parentheses. Lower bound values are 6.84, 4.94, y 4.04 para 1%, 5%, y 10%
levels of signiﬁcance, respectivamente. Upper bound values are 7.84, 5.73, y 4.78 para 1%, 5%,
y 10% levels of signiﬁcance, respectivamente.
Fuente: Authors’ calculations.

PIB), which are I(0). Además, the ARDL technique allows different variables
to take different optimal numbers of lags, while this is not permitted in the
Johansen cointegration approach. Por lo tanto, the ARDL technique, que lo hará
accommodate both I(0) y yo(1) variables, is used in this paper to estimate the
long-run relationships between long-term government bond yields and other control
variables.

IV. Empirical Results

A. Monthly Results

The ARDL bounds test results generated from monthly variables are
presented in Tables 4–8. When the short-term interest rate is included with inﬂation,
in most cases the computed F-statistic based on a Wald test exceeds the upper
bound value at the 5% nivel. In the case of the 2-year government bond yield,
the computed F-statistic exceeds the upper bound value at the 10% level when the

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 185

Mesa 5. Autoregressive Distributive Lag Bounds Test Results for
IGB3YR (monthly data)

Ecuación
5.1) IGB3YR = β 16 + β 17TB3M
5.2) IGB3YR = β 18 + β 19TCPIYOY
5.3) IGB3YR = β 20 + β 21IPIYOY
5.4) IGB3YR = β 22 + β 23TB3M + β 24TCPIYOY
5.5) IGB3YR = β 25 + β 26TB3M + β 27IPIYOY
5.6) IGB3YR = β 28 + β 29TB3M + β 30TCPIYOY + β 31IPIYOY

F-statistic

4.60
2.64
2.03
8.37***
3.70
6.20**

Long-Run Relationships

Variable

Ecuación 5.4

Ecuación 5.6

TB3M

TCPIYOY

IPIYOY

Constant

Time period

Number of observations

0.39***
(0.04)
−0.01
(0.04)
—

4.74***
(0.47)
Dec 2006–
Oct 2015
107

0.38***
(0.05)
−0.01
(0.04)
−0.01
(0.01)
4.81***
(0.55)
Feb 2007–
Oct 2015
105

IGB3YR = 3-year government bond yield, IPIYOY = year-on-year percentage change in
industrial production, TB3M = 3-month government auction rate, TCPIYOY = year-on-year
percentage change in consumer price index.
Notas: *** y ** representar 1% y 5% levels of signiﬁcance, respectivamente. Los errores estándar son
entre paréntesis. Lower bound values are 6.84, 4.94, y 4.04 para 1%, 5%, y 10% levels of
signiﬁcance, respectivamente. Upper bound values are 7.84, 5.73, y 4.78 para 1%, 5%, y 10%
levels of signiﬁcance, respectivamente.
Fuente: Authors’ calculations.

short-term rate is included in the equation with both inﬂation and the industrial
production index (equation 4.6). The null hypothesis of no cointegration is rejected
whenever the F-statistic value is higher than the upper bound value. este análisis
conﬁrms the presence of a long-run relationship among long-term government bond
yields, the short-term interest rate, the rate of inﬂation, and the growth of industrial
producción. It enables the estimation of the long-run coefﬁcients of the short-term
interest rate and other control variables. The coefﬁcients of the short-term interest
rate are always positive and statistically signiﬁcant at the 1% nivel. The size of this
coefﬁcient tends to be smaller as the tenor of the government bond rises. Estos
results suggest that in the long run the short-term interest rate strongly inﬂuences
long-term government bond yields in India.

Quarterly Results

Estimated results using quarterly data are presented in Tables 9–13. Cuando
the short-term 3-month interest rate is included with inﬂation and the ratio of

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

186 Asian Development Review

Mesa 6. Autoregressive Distributive Lag Bounds Test Results for
IGB5YR (monthly data)

Ecuación
6.1) IGB5YR = β 32 + β 33TB3M
6.2) IGB5YR = β 34 + β 35TCPIYOY
6.3) IGB5YR = β 36 + β 37IPIYOY
6.4) IGB5YR = β 38 + β 39TB3M + β 40TCPIYOY
6.5) IGB5YR = β 41 + β 42TB3M + β 43IPIYOY
6.6) IGB5YR = β 44 + β 45TB3M + β 46TCPIYOY + β 47IPIYOY

F-statistic

3.84
3.65
2.37
10.56***
4.08
7.74**

Variable

TB3M

TCPIYOY

IPIYOY

Constant

Time period

Number of observations

Long-Run Relationships

Ecuación 6.4

Ecuación 6.6

0.26***
(0.04)
−0.00
(0.04)
—

5.86***
(0.43)
Dec 2006–
Oct 2015
107

0.25***
(0.04)
−0.00
(0.04)
−0.01
(0.01)
5.98***
(0.53)
Feb 2007–
Oct 2015
105

IGB5YR = 5-year government bond yield, IPIYOY = year-on-year percentage change in
industrial production, TB3M = 3-month government auction rate, TCPIYOY = year-on-year
percentage change in consumer price index.
Notas: *** y ** representar 1% y 5% levels of signiﬁcance, respectivamente. Los errores estándar son
entre paréntesis. Lower bound values are 6.84, 4.94, y 4.04 para 1%, 5%, y 10% levels of
signiﬁcance, respectivamente. Upper bound values are 7.84, 5.73, y 4.78 para 1%, 5%, y 10%
levels of signiﬁcance, respectivamente.
Fuente: Authors’ calculations.

government debt to nominal GDP, the computed F-statistic value is mostly higher
than the upper bound value. Long-run coefﬁcients of the short-term interest rate
are positive when signiﬁcant. The magnitude of this coefﬁcient lies between 0.13
y 0.53. The coefﬁcient of the ratio of government debt to nominal GDP is mostly
negative and signiﬁcant at the 1% nivel, suggesting that in the long run a higher debt
ratio tends to reduce the nominal yields of IGBs. This is contrary to the conventional
wisdom. Quarterly data allow the use of government ﬁscal variables but a clear
limitation is that these results are based on a smaller number of observations.

The Main Finding and Its Relevance

The main ﬁnding is that the short-term interest rate is a key driver of the
long-term interest rate on IGBs in both the short run and the long run. This ﬁnding
has important policy implications. Por ejemplo, it suggests that the RBI’s monetary
policy decisions not only have an immediate effect on the long-term interest
rate and the Treasury yield curve, but also on the direction and the level of the

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 187

Mesa 7. Autoregressive Distributive Lag Bounds Test Results for
IGB7YR (monthly data)

Ecuación
7.1) IGB7YR = β 48 + β 49TB3M
7.2) IGB7YR = β 50 + β 51TCPIYOY
7.3) IGB7YR = β 52 + β 53IPIYOY
7.4) IGB7YR = β 54 + β 55TB3M + β 56TCPIYOY
7.5) IGB7YR = β 57 + β 58TB3M + β 59IPIYOY
7.6) IGB7YR = β 60 + β 61TB3M + β 62TCPIYOY + β 63IPIYOY

F-statistic

4.02
5.63
2.59
10.60***
4.09
7.70**

Variable

TB3M

TCPIYOY

IPIYOY

Constant

Time period

Number of observations

Long-Run Relationships

Ecuación 7.2

Ecuación 7.4

Ecuación 7.6

—

0.03
(0.08)
—

7.71***
(0.62)
Dec 2006–
Oct 2015
107

0.19***
(0.03)
0.02
(0.04)
—

6.40***
(0.43)
Dec 2006–
Oct 2015
107

0.18***
(0.04)
0.01
(0.04)
−0.01
(0.01)
6.53***
(0.52)
Feb 2007–
Oct 2015
105

IGB7YR = 7-year government bond yield, IPIYOY = year-on-year percentage change in
industrial production, TB3M = 3-month government auction rate, TCPIYOY = year-on-year
percentage change in consumer price index.
Notas: *** y ** representar 1% y 5% levels of signiﬁcance, respectivamente. Los errores estándar son
entre paréntesis. Lower bound values are 6.84, 4.94, y 4.04 para 1%, 5%, y 10% levels of
signiﬁcance, respectivamente. Upper bound values are 7.84, 5.73, y 4.78 para 1%, 5%, y 10%
levels of signiﬁcance, respectivamente.
Fuente: Authors’ calculations.

long-term interest rate over a longer horizon. The results obtained are robust.
Additional regressions estimated in Appendix 2 show that the coefﬁcient of the
short-term interest rate is positive and statistically signiﬁcant, at least at the 5%
nivel, even after controlling for variables such as credit growth, global investors’
risk appetite, and the nominal effective exchange rate. Por lo tanto, the main ﬁnding
that the short-term interest rate is the most important determinant of long-term bond
yields does not change with adjustments to the speciﬁcations.

These results reinforce the ﬁndings in Akram and Das’ (2015a and 2015b)
recent studies on IGBs in which they report that changes in the short-term interest
rate are important determinants of changes in long-term government bond yields in
India. Whereas Akram and Das (2015a and 2015b) established the results for the
short run, the current study extends this for the long run.

V. Policy Implications and Conclusion

The empirical results reported here support Keynes’ conjecture that the
central bank’s actions, through its inﬂuence on the short-term interest rate and its use

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

188 Asian Development Review

Mesa 8. Autoregressive Distributive Lag Bounds Test Results for
IGB10YR (monthly data)

Ecuación
8.1) IGB10YR = β 64 + β 65TB3M
8.2) IGB10YR = β 66 + β 67TCPIYOY
8.3) IGB10YR = β 68 + β 69IPIYOY
8.4) IGB10YR = β 70 + β 71TB3M + β 72TCPIYOY
8.5) IGB10YR = β 73 + β 74TB3M + β 75IPIYOY
8.6) IGB10YR = β 76 + β 77TB3M + β 78TCPIYOY + β 79IPIYOY

F-statistic

4.73
7.51**
3.60
9.42***
3.07
6.83**

Variable

TB3M

TCPIYOY

IPIYOY

Constant

Time period

Number of observations

Long-Run Relationships

Ecuación 8.2

Ecuación 8.4

Ecuación 8.6

—

0.04
(0.05)
—

7.74***
(0.45)
Dec 2006–
Oct 2015
107

0.14***
(0.04)
0.03
(0.04)
—

6.87***
(0.44)
Dec 2006–
Oct 2015
107

0.13***
(0.04)
0.02
(0.04)
−0.01
(0.01)
6.99***
(0.53)
Feb 2007–
Oct 2015
105

IGB10YR = 10-year government bond yield, IPIYOY = year-on-year percentage change in
industrial production, TB3M = 3-month government auction rate, TCPIYOY = year-on-year
percentage change in consumer price index.
Notas: *** y ** representar 1% y 5% levels of signiﬁcance, respectivamente. Los errores estándar son
entre paréntesis. Lower bound values are 6.84, 4.94, y 4.04 para 1%, 5%, y 10% levels of
signiﬁcance, respectivamente. Upper bound values are 7.84, 5.73, y 4.78 para 1%, 5%, y 10%
levels of signiﬁcance, respectivamente.
Fuente: Authors’ calculations.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

Mesa 9. Autoregressive Distributive Lag Bounds Test Results for IGB2YR_Q
(quarterly data)

Ecuación
9.1) IGB2YR_Q = γ 0 + γ 1TB3M_Q + γ 2DRATIO_Q
9.2) IGB2YR_Q = γ 3 + γ 4TCPIYOY_Q + γ 5DRATIO_Q
9.3) IGB2YR_Q = γ 6 + γ 7IPIYOY_Q + γ 8DRATIO_Q
9.4) IGB2YR_Q = γ 9 + γ 10TB3M_Q + γ 11TCPIYOY_Q + γ 12DRATIO_Q
9.5) IGB2YR_Q = γ 13 + γ 14TB3M_Q + γ 15IPIYOY_Q + γ 16DRATIO_Q
9.6) IGB2YR_Q = γ 17 + γ 18TB3M_Q + γ 19TCPIYOY_Q + γ 20IPIYOY_Q

+ γ 21DRATIO_Q

F-statistic

2.67
1.68
2.21
1.16
2.03
1.01

DRATIO_Q = government debt as percentage of nominal gross domestic product, IGB2YR_Q = 2-year
government bond yield, IPIYOY_Q = year-on-year percentage change in industrial production, TB3M_Q
= 3-month government auction rate, TCPIYOY_Q = year-on-year percentage change in consumer price
índice.
Nota: Lower bound values are 6.84, 4.94, y 4.04 para 1%, 5%, y 10% levels of signiﬁcance,
respectivamente. Upper bound values are 7.84, 5.73, y 4.78 para 1%, 5%, y 10% levels of signiﬁcance,
respectivamente.
Fuente: Authors’ calculations.

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 189

Mesa 10. Autoregressive Distributive Lag Bounds Test Results for IGB3YR_Q
(quarterly data)

Ecuación
10.1) IGB3YR_Q = γ 22 + γ 23TB3M_Q + γ 24DRATIO_Q
10.2) IGB3YR_Q = γ 25 + γ 26TCPIYOY_Q + γ 27DRATIO_Q
10.3) IGB3YR_Q = γ 28 + γ 29IPIYOY_Q + γ 30DRATIO_Q
10.4) IGB3YR_Q = γ 31 + γ 32TB3M_Q + γ 33TCPIYOY_Q + γ 34DRATIO_Q
10.5) IGB3YR_Q = γ 35 + γ 36TB3M_Q + γ 37IPIYOY_Q + γ 38DRATIO_Q
10.6) IGB3YR_Q = γ 39 + γ 40TB3M_Q + γ 41TCPIYOY_Q + γ 42IPIYOY_Q

+ γ 43DRATIO_Q

F-statistic

5.51**
2.19
2.51
6.17**
2.21
1.09

Variable

TB3M_Q

TCPIYOY_Q

IPIYOY_Q
DRATIO_Q

Constant

Time period

Number of observations

Long-Run Relationships

Ecuación 10.1

Ecuación 10.4

0.53***
(0.07)
—

—
−2.39***
(0.82)
7.36***
(1.55)
Q3 2003–
Q2 2015
48

0.44***
(0.03)
0.00
(0.03)
—
0.69
(0.61)
3.21***
(0.85)
Q1 2007–
Q2 2015
34

DRATIO_Q = government debt as percentage of nominal gross domestic product, IGB3YR_Q = 3-year
government bond yield, IPIYOY_Q = year-on-year percentage change in industrial production, TB3M_Q =
3-month government auction rate, TCPIYOY_Q = year-on-year percentage change in consumer price index.
Notas: *** y ** representar 1% y 5% levels of signiﬁcance, respectivamente. Standard errors are in parentheses.
Lower bound values are 5.15, 3.79, y 3.17 para 1%, 5%, y 10% levels of signiﬁcance, respectivamente. Upper
bound values are 6.36, 5.52, y 4.14 para 1%, 5%, y 10% levels of signiﬁcance, respectivamente.
Fuente: Authors’ calculations.

of the tools of monetary policy, are the main drivers of the long-term interest rate.
In the case of India, the actions of the RBI affect the long-term interest rate. El
long-term interest rate on IGBs is positively associated with the short-term interest
rate on Indian Treasury bills after controlling for the relevant variables such as the
rate of inﬂation, growth of industrial production, and debt ratio. A higher (más bajo)
long-term interest rate on IGBs is associated with a higher (más bajo) short-term
interest rate, más alto (más bajo) rate of inﬂation, and faster (slower) pace of industrial
producción. The results show that a higher level of government indebtedness does
not have an adverse effect on IGBs’ nominal yields, contrary to the conventional
vista. These ﬁndings concur with the results obtained in Akram and Das’ (2015a
and 2015b) studies of the short-term dynamics of IGBs. The ﬁndings also align with
those obtained in studies by Chakraborty (2012 y 2016) and Vinod, Chakraborty,
and Karun (2014), which use quite different econometric and statistical methods.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

190 Asian Development Review

Mesa 11. Autoregressive Distributive Lag Bounds Test Results for IGB5YR_Q
(quarterly data)

Ecuación
11.1) IGB5YR_Q = γ 44 + γ 45TB3M_Q + γ 46DRATIO_Q
11.2) IGB5YR_Q = γ 47 + γ 48TCPIYOY_Q + γ 49DRATIO_Q
11.3) IGB5YR_Q = γ 50 + γ 51IPIYOY_Q + γ 52DRATIO_Q
11.4) IGB5YR_Q = γ 53 + γ 54TB3M_Q + γ 55TCPIYOY_Q + γ 56DRATIO_Q
11.5) IGB5YR_Q = γ 57 + γ 58TB3M_Q + γ 59IPIYOY_Q + γ 60DRATIO_Q
11.6) IGB5YR_Q = γ 61 + γ 62TB3M_Q + γ 63TCPIYOY_Q + γ 64IPIYOY

+ γ 65DRATIO_Q

F-statistic

5.13**
3.45
3.81
9.00***
3.97
6.63***

Variable

TB3M_Q

TCPIYOY_Q

IPIYOY_Q

DRATIO_Q

Constant

Time period

Number of observations

Long-Run Relationships

Ecuación 11.1

Ecuación 11.4

Ecuación 11.6

0.41***
(0.09)
—

—

−3.06***
(1.04)
9.52***
(1.98)
Q3 2003–
Q2 2015
48

0.26***
(0.04)
−0,03
(0.05)
—

1.54
(0.92)
3.73**
(1.36)
Q1 2007–
Q2 2015
34

0.21***
(0.07)
−0.11
(0.08)
−0,03
(0.02)
1.67
(1.08)
4.67**
(1.83)
Q1 2007–
Q2 2015
34

DRATIO_Q = government debt as percentage of nominal gross domestic product, IGB5YR_Q = 5-year
government bond yield, IPIYOY_Q = year-on-year percentage change in industrial production, TB3M_Q =
3-month government auction rate, TCPIYOY_Q = year-on-year percentage change in consumer price index.
Notas: *** y ** representar 1% y 5% levels of signiﬁcance, respectivamente. Standard errors are in parentheses.
Lower bound values are 5.15, 3.79, y 3.17 para 1%, 5%, y 10% levels of signiﬁcance, respectivamente. Upper
bound values are 6.36, 5.52, y 4.14 para 1%, 5%, y 10% levels of signiﬁcance, respectivamente.
Fuente: Authors’ calculations.

The ﬁndings reported in this paper have implications for policy debates in
India and other emerging markets with monetary sovereignty that issue government
debt mostly in their own currencies. The ﬁndings are also relevant for ongoing
debates over ﬁscal policy, the sustainability of government debt, monetary policy,
monetary–ﬁscal coordination and the policy mix during economic ﬂuctuations, y
macroeconomic and monetary theory (Bindseil 2004, Fullwiler 2008 y 2016,
Kregel 2011, Sims 2013a and 2013b, Tcherneva 2011, Woodford 2001, and Wray
2003 [1998] y 2012). Primero, the results show that the RBI can exert a strong
inﬂuence on IGB yields by affecting the short-term interest rates. The RBI can
affect the short-term interest rates on Indian Treasury bills through setting the
repo rate and the reverse repo rate (Cifra 6). These ﬁndings support Keynes’
conjecture about the inﬂuence of a sovereign central bank on long-term interest
tarifas. Segundo, the results also suggest that, contrary to the conventional wisdom,
higher government indebtedness does not raise IGBs’ nominal yields. While this

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 191

Mesa 12. Autoregressive Distributive Lag Bounds Test Results for IGB7YR_Q
(quarterly data)

Ecuación
12.1) IGB7YR_Q = γ 66 + γ 67TB3M_Q + γ 68DRATIO_Q
12.2) IGB7YR_Q = γ 69 + γ 70TCPIYOY_Q + γ 71DRATIO_Q
12.3) IGB7YR_Q = γ 72 + γ 73IPIYOY_Q + γ 74DRATIO_Q
12.4) IGB7YR_Q = γ 75 + γ 76TB3M_Q + γ 77TCPIYOY_Q + γ 78DRATIO_Q
12.5) IGB7YR_Q = γ 79 + γ 80TB3M_Q + γ 81IPIYOY_Q + γ 82DRATIO_Q
12.6) IGB7YR_Q = γ 83 + γ 84TB3M_Q + γ 85TCPIYOY_Q + γ 86IPIYOY_Q

+ γ 87DRATIO_Q

F-statistic

4.89**
4.50**
4.62**
10.04***
3.81
2.44

Variable

TB3M_Q

TCPIYOY_Q

IPIYOY_Q

DRATIO_Q

Constant

Time period

Number of observations

Long-Run Relationships

Ecuación 12.1

Ecuación 12.2

Ecuación 12.3

Ecuación 12.4

0.35***
(0.10)
—

—

−3.22***
(1.14)
10.40***
(2.17)
Q3 2003–
Q2 2015
48

—

0.02
(0.10)
—

1.67
(2.16)
5.18
(3.51)
Q1 2007–
Q2 2015
34

—

−0,02
(0.04)
−4.97***
(1.57)
15.71***
(2.53)
Q3 2003–
Q2 2015
48

0.18***
(0.05)
−0,04
(0.05)
—

1.71*
(0.98)
4.27***
(1.43)
Q1 2007–
Q2 2015
34

DRATIO_Q = government debt as percentage of nominal gross domestic product, IGB7YR_Q = 7-year
government bond yield, IPIYOY_Q = year-on-year percentage change in industrial production, TB3M_Q =
3-month government auction rate, TCPIYOY_Q = year-on-year percentage change in consumer price index.
Notas: *** y ** representar 1% y 5% levels of signiﬁcance, respectivamente. Standard errors are in parentheses.
Lower bound values are 5.15, 3.79, y 3.17 para 1%, 5%, y 10% levels of signiﬁcance, respectivamente. Upper
bound values are 6.36, 5.52, y 4.14 para 1%, 5%, y 10% levels of signiﬁcance, respectivamente.
Fuente: Authors’ calculations.

ﬁnding is contrary to the conventional view, which is derived from the loanable
funds perspective, it is fully in concordance with Keynes’ views and modern money
theory (Fullwiler 2008 y 2016, Kregel 2011, and Wray 2003 [1998] y 2012),
which holds that increased government expenditures result in rising central bank
reserves and banking deposits in the ﬁnancial system because the central bank
credits the banks in order to facilitate the government’s borrowing and expenditures.
Tercero, the results suggest that Indian policy makers can use appropriate models—
based on information on the current trend of short-term interest rates, gobierno
debt ratios, and other key macro variables—to form their long-term outlook about
IGBs’ nominal yields and understand the implications of the government’s ﬁscal
stance on the government bond market. Por supuesto, the use of such models requires
judgment and prudence, and carries with it model risks and limitations.

Keynes claims that the central bank has a decisive inﬂuence on long-term
interest rates. He believes that short-term interest rates and other monetary policy

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

192 Asian Development Review

Mesa 13. Autoregressive Distributive Lag Bounds Test Results for IGB10YR_Q
(quarterly data)

Ecuación
13.1) IGB10YR_Q = γ 88 + γ 89TB3M_Q + γ 90DRATIO_Q
13.2) IGB10YR_Q = γ 91 + γ 92TCPIYOY_Q + γ 93DRATIO_Q
13.3) IGB10YR_Q = γ 94 + γ 95IPIYOY_Q + γ 96DRATIO_Q
13.4) IGB10YR_Q = γ 97 + γ 98TB3M_Q + γ 99TCPIYOY + γ 100DRATIO_Q
13.5) IGB10YR_Q = γ 101 + γ 102TB3M_Q + γ 103IPIYOY_Q + γ 104DRATIO_Q
13.6) IGB10YR_Q = γ 105 + γ 106TB3M_Q + γ 107TCPIYOY_Q + γ 108IPIYOY_Q

+ γ 109DRATIO_Q

F-statistic

6.82***
5.51**
7.88***
10.66***
4.14
3.93

Variable

TB3M_Q

TCPIYOY_Q

IPIYOY_Q

DRATIO_Q

Constant

Time period

Number of observations

Long-Run Relationships

Ecuación 13.1

Ecuación 13.2

Ecuación 13.3

Ecuación 13.4

0.29
(0.20)
—

—

−5.41***
(2.18)
14.67***
(4.42)
Q3 1999–
Q2 2015
64

—

0.03
(0.08)
—

1.53
(1.78)
5.48*
(2.90)
Q1 2007–
Q2 2015
34

—

0.04
(0.07)
−7.52***
(2.16)
19.90***
(3.56)
Q3 1999–
Q2 2015
64

0.13**
(0.05)
−0.05
(0.06)
—

1.75*
(1.02)
4.85***
(1.48)
Q1 2007–
Q2 2015
34

DRATIO_Q = government debt as percentage of nominal gross domestic product, IGB10YR_Q = 10-year
government bond yield, IPIYOY_Q = year-on-year percentage change in industrial production, TB3M_Q = 3-month
government auction rate, TCPIYOY_Q = year-on-year percentage change in consumer price index.
Notas: *** y ** representar 1% y 5% levels of signiﬁcance, respectivamente. Standard errors are in parentheses. Lower
bound values are 5.15, 3.79, y 3.17 para 1%, 5%, y 10% levels of signiﬁcance, respectivamente. Upper bound values
son 6.36, 5.52, y 4.14 para 1%, 5%, y 10% levels of signiﬁcance, respectivamente.
Fuente: Authors’ calculations.

actions drive long-term interest rates and that an investor’s long-term outlook
is mostly shaped by the investor’s near-term outlook and assessment of current
condiciones. This paper shows that Keynes’ conjecture has empirical support in
India over the long-run horizon by extending Akram and Das’ (2015a and 2015b)
ﬁndings for the short-run horizon to the long-run horizon for the case of India. Él
contributes to the nascent literature—such as Akram (2014) and Akram and Das
(2014a and 2014b) on Japan; Akram and Das (2017b and 2017c) on the eurozone;
and Akram and Li (2016, 2017a, and 2017b) on the US—on this topic of examining
whether Keynes’ conjecture holds in various countries. Further research should
extend this to a wider range of countries—both advanced capitalist economies
and emerging markets and other developing areas—and apply a broad spectrum of
suitable econometric methods to establish whether these ﬁndings can be generalized
and determine under which institutional contexts they are warranted.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 193

Referencias

Acharya, Viral V., and Sascha Steffen. 2015. “The Greatest Carry Trade Ever? Comprensión

Eurozone Bank Risks.” Journal of Financial Economics 115 (2): 215–36.

Akram, Tanweer. 2014. “The Economics of Japan’s Stagnation.” Business Economics 49 (3):

156–75.

Akram, Tanweer, and Anupam Das. 2014a. “Understanding the Low Yields of the Long-Term

Japanese Sovereign Debt.” Journal of Economic Issues 48 (2): 331–40.

_____. 2014b. “The Determinants of Long-Term Japanese Government Bonds’ Low Nominal

Yields.” Levy Economics Institute Working Paper No. 818.

_____. 2015a. “Does Keynesian Theory Explain Indian Government Bond Yields?” Levy

Economics Institute Working Paper No. 834.

_____. 2015b. “A Keynesian Explanation of Indian Government Bond Yields.” Journal of Post

Keynesian Economics 38 (4): 565–87.

_____. 2017a. “The Long-Run Determinants of Indian Government Bond Yields.” Levy

Economics Institute Working Paper No. 881.

_____. 2017b. “The Dynamics of Government Bond Yields in the Eurozone.” Levy Economics

Institute Working Paper No. 889.

_____. 2017C. “The Dynamics of Government Bond Yields in the Eurozone.” Annals of Financial

Ciencias económicas 12 (3): 1750011-1–1750011-18.

Akram, Tanweer, and Huiqing Li. 2016. “The Empirics of Long-Term US Interest Rates.” Levy

Economics Institute Working Paper No. 863.

_____. 2017a. “What Keeps Long-Term US Interest Rates So Low?” Economic Modelling 60:

380–90.

_____. 2017b. “An Inquiry Concerning Long-Term US Interest Rates Using Monthly Data.” Levy

Economics Institute Working Paper No. 894.

Andritzky, Jochen R. 2012. “Government Bonds and Their Investors: What Are the Facts and Do

They Matter?” International Monetary Fund (IMF) Working Paper No. 12/158.

Ardagna, Silvia, Francesco Caselli, and Timothy Lane. 2007. “Fiscal Discipline and the Cost
of Public Debt Service: Some Estimates for OECD Countries.” The B.E. Diario de
Macroeconomics 7 (1): 1–35.

Arslanalp, Serkan, and Tigran Poghosyan. 2014. “Foreign Investor Flows and Sovereign Bond

Yields in Advanced Economies.” IMF Working Paper No. 14/27.

Asonuma, Tamon, Said Bakhache, and Heiko Hessee. 2015. “Is Banks’ Home Bias Good or Bad

for Public Debt Sustainability?” IMF Working Paper No. 15/44.

Baldacci, Emanuele, and Manmohan Kumar. 2010. “Fiscal Deﬁcits, Public Debt, and Sovereign

Bond Yields.” IMF Working Paper No. 10/184.

Bindseil, Ulrich. 2004. Monetary Policy Implementation: Teoría, Past, and Present. Oxford and

Nueva York: prensa de la Universidad de Oxford.

Böhm–Bawerk, Eugene von. 1959. Capital and Interest (three volumes). South Holland:

Libertarian Press.

Cassel, Gustav. 1903. Nature and Necessity of Interest. London and New York: The Macmillan

Compañía.

Chakraborty, Lekha S. 2012. “Empirical Evidence on Fiscal Deﬁcit–Interest Rate Linkages and

Financial Crowding Out.” Levy Economics Institute Working Paper No. 744.

_____. 2016. Fiscal Consolidation, Budget Deﬁcits, and the Macro Economy. Thousand Oaks:

Sage Publications Pvt. Limitado.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

194 Asian Development Review

clark, Todd E., and Troy Davig. 2008. “An Empirical Assessment of the Relationships among
Inﬂation and Short- and Long-Term Expectations.” Research Working Paper RWP 08-05.
Kansas City: Federal Reserve Bank of Kansas City.

Dickey, David A., and Wayne A. Batán. 1979. “Distribution of the Estimators for Autoregressive
Time Series with a Unit Root.” Journal of the American Statistical Association 74 (366):
427–31.

_____. 1981. “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root.”

Econometrica 49 (4): 1057–72.

Ebeke, cristiano, and Yinqiu Lu. 2014. “Emerging Market Local Currency Bond Yields and
Foreign Holdings in the Post-Lehman Period—A Fortune or Misfortune?” IMF Working
Paper No. 14/29.

eliot, graham, Tomas J.. Rothenberg, and James H. Existencias. 1996. “Efﬁcient Tests for an

Autoregressive Unit Root.” Econometrica 64 (4): 813–36.

Faust, Jon, and Jonathan H. Wright. 2013. “Forecasting Inﬂation.” In Handbook of Economic
Forecasting, volumen. 2A, edited by Graham Elliot and Allan Timmermann. Ámsterdam:
Elsevier.

Fullwiler, Scott T. 2008. “Modern Central Bank Operations: The General Principles.” Social

Science Research Network. http://ssrn.com/abstract=1658232.

_____. 2016. “The Debt Ratio and Sustainable Macroeconomic Policy.” World Economic Review

7: 12–42.

Gruber, Joseph W., and Steven B. Kamin. 2012. “Fiscal Positions and Government Bond
Yields in OECD Countries.” Journal of Money, Credit, and Banking 44 (8): 1563–
87.

Hayek, Friedrich August. 1933. Monetary Theory and the Trade Cycle. Nueva York: Sentry Press.
_____. 1935. Prices and Production, second edition. Nueva York: Augustus M. Kelly.
Jácome, Luis I., Simon Baker Townsend, Marcela Matamoros–Indorf, and Mrinalini Sharma.
2012. “Central Bank Credit to the Government: What Can We Learn from International
Practices?” IMF Working Paper No. 12/16.

Johansen, Søren, and Katarina Juselius. 1990. “Maximum Likelihood Estimation and Inference
on Cointegration—with Applications to the Demand for Money.” Oxford Bulletin of
Economics and Statistics 52 (2): 169–10.

Keynes, John M. 1930. A Treatise on Money, volumen. II: The Applied Theory of Money. Londres:

Macmillan.

_____. 2007 (1936). The General Theory of Employment, Interest, and Money. Nueva York:

Palgrave Macmillan.

Kregel, Ene. 2011. “Was Keynes’ Monetary Policy À Outrance in the Treatise, A Forerunner of
ZIRP and QE? Did He Change His Mind in the General Theory?” Levy Economics Institute
Policy Note No. 4.

Justicia, Waikei R., and Kiichi Tokuoka. 2013. “Assessing the Risks to the Japanese Government
Bond Market.” Journal of International Commercial and Economic Policy 4 (1): 1350002-
1–1350002-15.

Macrobond. Various years. Macrobond subscription services (consultado en septiembre 13, 2018).
marshall, Alfred. 1890. Principles of Economics. London and New York: The Macmillan

Compañía.

Mavroeidis, Sophocles, Mikkel Plagborg-Møller, and James H. Existencias. 2014. “Empirical Evidence
on Inﬂation Expectations in the New Keynesian Phillips Curve.” Journal of Economic
Literature 52 (1): 124–88.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 195

nelson, Charles R., and Charles R. Plosser. 1982. “Trends and Random Walks in Macroeconmic
Time Series: Some Evidence and Implications.” Journal of Monetary Economics 10 (2):
139–62.

Pesaran, METRO. Hashem, and Yongcheol Shin. 1998. “An Autoregressive Distributed-Lag Modelling
Approach to Cointegration Analysis.” Econometric Society Monographs 31: 371–
413.

Pesaran, METRO. Hashem, Yongcheol Shin, and Richard J. Herrero. 2001. “Bounds Testing Approaches
to the Analysis of Level Relationships.” Journal of Applied Econometrics 16 (3): 289–
326.

Phillips, Peter C. B., and Pierre Perron. 1988. “Testing for a Unit Root in Time Series Regression.”

Biometrika 75 (2): 335–46.

Pigou, Arthur Cecil. 1927. Industrial Fluctuations. Londres: Macmillan.
Poghosyan, Tigran. 2014. “Long-Run and Short-Run Determinants of Sovereign Bond Yields in

Advanced Economies.” Economic System 38 (1): 100–14.

Reserve Bank of India. 2009. Fiscal Monetary Coordination: Report on Currency and Finance

2009–12. Mumbai.

_____. 2014. Government Securities Market in India–A Primer. http://rbi.org.in/scripts/FAQView

.aspx?Id=79.

_____. Various years. Reserve Bank of India Annual Report. India.
Ricardo, David. 1817. On the Principles of Political Economy. Londres: John Murrary.
Rieﬂer, Winﬁeld W. 1930. Money Rates and Money Markets in the United States. New York and

Londres: Harper & Brothers.

Sims, Christopher A. 2013a. “Paper Money.” American Economic Association Presidential

Lecture. http://sims.princeton.edu/yftp/PaperMoney/PaperMoneySlides.pdf.

_____. 2013b. “Paper Money.” American Economic Review 103 (2): 563–84.
Taussig, Frank W. 1918. Principles of Economics, volumen 2, second revised edition. Nueva York:

The Macmillan Company.

Tcherneva, Pavlina R. 2011. “Bernanke’s Paradox: Can He Reconcile His Position on the
Federal Budget with His Recent Charge to Prevent Deﬂation?” Journal of Post Keynesian
Ciencias económicas 33 (3): 411–34.

Tokuoka, Kiichi. 2012. “Intergenerational Implications of Fiscal Consolidation in Japan.” IMF

Working Paper No. 12/197.

Vinod, Hrishikesh D., Lekha Chakraborty, and Honey Karun. 2014. “If Deﬁcits Are Not the
Culprit, What Determines Indian Interest Rates? An Evaluation Using the Maximum
Entropy Bootstrap Method.” Levy Economics Institute Working Paper No. 811.

Von Mises, Ludwig. 1953. The Theory of Money and Credit. nuevo refugio: Prensa de la Universidad de Yale.
Wicksell, Knut. 1962 (1936). Interest and Prices. Nueva York: Sentry Press.
Woodford, Miguel. 2001. “Fiscal Requirements for Price Stability.” Journal of Money, Credit

and Banking 33 (3): 669–28.

Wray, l. Randall. 2003 (1998). Understanding Modern Money: The Key to Full Employment and

Price Stability. Cheltenham and Northampton: Edward Elgar.

_____. 2012. Modern Money Theory: A Primer on Macroeconomics for Sovereign Monetary

Sistemas. Nueva York: Palgrave Macmillan.

Yamanadra, Srinivas. 2014. “Minsky, Monetary Policy and Mint Street: Challenges for the Art
of Monetary Policymaking in Emerging Economies.” Levy Economics Institute Working
Paper No. 820.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

196 Asian Development Review

Apéndice 1. Derivation of the Two-Period Model of Government Bond Yields

The long-term interest rate on the 2-year government bond depends on the
short-term interest rate on Treasury securities in period 1 and the 1-year, 1-año
forward rate (equation A1). The 1-year, 1-year forward rate is based on an investor’s
expectation of the short-term interest rate on Treasury securities in period 2 y el
term premium (equation A2). Sin embargo, the expected short-term interest rate on
Treasury securities in period 2 and the term premium is a function of the investor’s
expectation of growth and inﬂation in period 2 (equation A3). Por eso, the 1-year,
1-year forward rate is merely the sum of the expected short-term interest rate on the
Treasury bill in period 2 and a function of the expected growth rate and expected
inﬂation in the same period (equation A4). This implies that the forward rate is a
function of expected short-term interest rates on Treasury securities, the expected
growth rate, and expected rate of inﬂation in period 2 (equation A5). Desde el
long-term interest rate is a function of the short-term interest rate on the Treasury
securities in period 1 and the 1-year, 1-year forward rate (equation A6), it follows
that the long-term interest rate is a function of the short-term interest rate in period
1, and a function of the expected short-term interest rate, expected growth rate, y
expected rate of inﬂation in period 2 (equation A7).

Keynes’ view is that the investor resorts to the present and the past. El
investor relies on his view of the near-term future to form his conception of the
long-term future since it is not really possible to have a proper mathematical
expectation of the unknown and uncertain future. Por eso, for the investor, el
expected short-term interest rate in period 2 is based on the actual short-term
interest rate in period 1 (equation A8), the expected growth rate in period 2 es
based on the actual growth rate in period 1 (equation A9), and the expected rate of
inﬂation in period 2 is based on the actual rate of inﬂation in period 1 (equation
A10). Similarmente, the expected government ﬁscal variable in period 2 is based
on the government ﬁscal variable in period 1 (equation A11). These Keynesian
assumptions results in a model (equation A12) where the long-term interest rate is
a function of either (i) the current short-term interest rate, the current growth rate,
and current inﬂation (equation A13); o (ii) the current short-term interest rate, el
current growth rate, current inﬂation, and the current government ﬁscal variable
(equation A14).

The Keynesian view that an investor’s expectation of key economic variables
depends largely on current conditions or the investor’s assessment of current
conditions may appear intriguing and counterintuitive. Pero
if key economic
variables follow a Markov process (equation A15, equation A16, equation A17,
and equation A18), then the Keynesian view of the trajectory of expected values
of these variables is entirely reasonable. Empirical and behavioral studies of the
investor’s expectations of the interest rate and the rate of inﬂation show that Keynes’

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

The Long-Run Determinants of Indian Government Bond Yields 197

conjectures have considerable support (Clark and Davig 2008; Faust and Wright
2013; Mavroedis, Plagborg-Møller, and Stock 2014).

A diferencia de, under rational expectations where Lucasian assumptions of
perfect foresight hold, the investor’s expected short-term interest rates, esperado
growth rate, expected inﬂation, and expected government ﬁscal variable would
igual, respectivamente, the actual short-term interest rates, growth rate, rate of inﬂation,
and government ﬁscal variable in period 2 (equation A19, equation A20, equation
A21, and equation A22). This would result in the long-term interest rate being a
function of either (i) the current short-term interest rate, growth rate, and rate of
inﬂation in period 2 (equation A23); o (ii) the current short-term interest rate,
growth rate, rate of inﬂation, and government ﬁscal variable in period 2 (equation
A24).

The model is represented in the following system of equations:

(1 + rLT )2 = (1 + r1) (1 + f1,1)

f1,1 = Er2 + z
Er2 + z = F 1 (Eg2, Eπ2)

f1,1 = Er2 + F 2 (Eg2, Eπ2)
f1,1 = F 3 (Er2, Eg2, Eπ2)
rLT = F 4 (r1, f1,1)
(cid:2)
r1, F 3 (Er2, Eg2, Eπ2)
rLT = F 4

(cid:3)

The Keynesian assumptions imply that the following hold:

Er2 = r1
Eg2 = g1
Eπ2 = π1
Eν2 = ν1

Incorporating Keynesian assumptions into the model leads to the following:

(cid:2)

r1, F 3 (r1, g1, π1)

rLT = F 4
rLT = F 5 (r1, g1, π1)

(cid:3)

(A1)

(A2)

(A3)

(A4)

(A5)

(A6)

(A7)

(A8)

(A9)

(A10)

(A11)

(A12)

(A13)

Extending the model to include the government ﬁscal variable results in the
following:
rLT = F 6 (r1, g1, π1, v1)

(A14)

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

F
/

3
6
1
1
6
8
1
6
4
4
2
2
4
a
d
mi
v
_
a
_
0
0
1
2
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

198 Asian Development Review

If the variables in period 2 are to follow a simple Markov process, these variables
can be modeled in the following terms:

r2 = (cid:8)1 + (cid:8)2r1
g2 = (cid:8)3 + (cid:8)4g1
π2 = (cid:8)5 + (cid:8)6π1
v2 = (cid:8)7 + (cid:8)8v1

(A15)

(A16)

(A17)

(A18)

In the above equations,
0 < (cid:8)2 < 1, 0 < (cid:8)4 < 1, 0 < (cid:8)6 < 1, and 0 < (cid:8)8 < 1. the restrictions on the parameters are as follows: It is useful to contrast the Keynesian model with a Lucasian (rational expectations) model. Under rational expectations: Er2 = r2 Eg2 = g2 Eπ2 = π2 Ev2 = v2 (A19) (A20) (A21) (A22) Under Lucasian assumptions, the long-term rates are modeled, respectively, without and with government ﬁscal variable, as follows: rLT = F 7 (r1, r2, g2, π2) rLT = F 8 (r1, r2, g2, π2, v2) (A23) (A24) Appendix 2. The Effects of Credit Growth, Global Risk Appetite, and the Nominal Effective Exchange Rate on Indian Government Bond Yields While this paper is based on a Keynesian perspective on government bond yields, it can be worthwhile to examine the view that a number of other macroeconomic variables—such as credit growth, global investors’ risk appetite, the index of the nominal effective exchange rate, and ﬁnancial ﬂows—could have marked effects on government bond yields. Increased (decreased) access to credit should lead to higher (lower) demand for government bonds and hence would cause bond prices to rise (fall) and bond yields to decline (increase). The appreciation (depreciation) of the Indian rupee should lead to lower (higher) bond yields because investors, particularly foreign investors, are compensated for the increase l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 6 8 1 6 4 4 2 2 4 a d e v _ a _ 0 0 1 2 7 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 The Long-Run Determinants of Indian Government Bond Yields 199 Figure A2.1. The Evolution of Credit to the Private Sector in India l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . GDP = gross domestic product. Source: Macrobond. Various years. Macrobond subscription services (accessed September 13, 2018). (reduction) in the value of the currency. Increased (decreased) perception of risk, as measured by higher (lower) volatility in global bond markets, should lead to higher (lower) government bond yields in India. This appendix examines whether any of these variables have a discernable inﬂuence on government bond yields as posited. The hypothesis that credit growth, global investors’ risk appetite, and the exchange rate matter is supported in some of the ﬁndings reported in the recent empirical literature on the determinants of government bond yields. Arslanalp and Poghosyan (2014) show that an increase in the share of government debt held by foreign investors can explain a reduction in long-term government bond yields. Ebeke and Lu (2014) report that foreign holdings of local currency government bonds in emerging markets exert downward pressure on government bond yields, though they note that an increase in such holdings is associated with somewhat increased yield volatility in the post-Lehman period. Other researchers have explored the effects of overall credit growth and the exchange rate on government bond yields in emerging markets. The evolution of some of these additional variables for India is shown in the ﬁgures below. Figure A2.1 shows that the ratio of overall credit to nominal gross domestic product steadily increased for many years before stabilizing in recent years. Figure A2.2 depicts the evolution of volatility in global bond markets. Volatility in government bond markets rose sharply during both the global ﬁnancial crisis and the eurozone debt crisis. Such volatility is a good proxy for global investors’ risk appetite. Figure A2.3 displays the evolution of the nominal effective exchange rate for the Indian rupee. The Indian rupee depreciated steadily versus / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 6 8 1 6 4 4 2 2 4 a d e v _ a _ 0 0 1 2 7 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 200 Asian Development Review Figure A2.2. The Evolution of Risk as Measured by the Global Market Volatility Index Source: Macrobond. Various years. Macrobond subscription services (accessed September 13, 2018). Figure A2.3. The Evolution of the Nominal Effective Exchange Rate of the Indian Rupee l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 6 8 1 6 4 4 2 2 4 a d e v _ a _ 0 0 1 2 7 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Source: Macrobond. Various years. Macrobond subscription services (accessed September 13, 2018). the United States dollar between 2000 and 2014. Since 2014, it has appreciated modestly and has been fairly stable. After controlling for the short-term interest rate, rate of inﬂation, growth of industrial production, and debt ratio, the effects of credit growth, global risk The Long-Run Determinants of Indian Government Bond Yields 201 Table A2.1. Autoregressive Distributive Lag Bounds Test Results for IGB2YR (monthly data) Equation B1.1) IGB2YR = β 0 + β 1TB3M + β 2CREDIT + β 3NEER + β 4RISK B1.2) IGB2YR = β 5 + β 6TCPIYOY + β 7CREDIT + β 8NEER + β 9RISK B1.3) IGB2YR = β 10 + β 11IPIYOY + β 12CREDIT + β 13NEER + β 14RISK B1.4) IGB2YR = β 15 + β 16TB3M + β 17TCPIYOY + β 18CREDIT + β 19NEER B1.5) IGB2YR = β 21 + β 22TB3M + β 23IPIYOY + β 24CREDIT + β 24NEER + β 20RISK + β 27RISK B1.6) IGB2YR = β 28 + β 29TB3M + β 30TCPIYOY + β 31IPIYOY + β 32CREDIT + β 33NEER + β 34RISK Long-Run Relationships Equation B1.1 Equation B1.2 Equation B1.3 Equation B1.4 Equation B1.5 0.46*** (0.04) — — 0.07*** (0.01) 0.01*** (0.01) −1.07*** (0.19) −0.28 (0.99) — 0.05 (0.15) — 0.10 (0.27) 0.02 (0.06) −3.26*** (0.75) 2.82 (19.83) — — 0.00 (0.05) 0.16*** (0.04) 0.03 (0.03) −4.07*** (1.11) −0.61 (4.49) 0.47*** (0.03) −0.20 (0.03) — 0.06 (0.05) 0.01 (0.01) −0.89*** (0.18) 0.30 (3.54) May 2003– Mar 2007– Oct 2015 Oct 2015 104 150 Aug 2009– Mar 2007– Oct 2015 Oct 2015 107 147 0.47*** (0.04) — −0.02 (0.02) 0.08*** (0.01) 0.02** (0.01) −1.28*** (0.26) −1.44 (1.24) Jul 2003– Oct 2015 148 Variable TB3M TCPIYOY IPIYOY CREDIT NEER RISK Constant Time period Number of observations F-statistic 9.86 5.79 8.03 7.58 8.58 5.99 Equation B1.6 0.45*** (0.04) −0.04 (0.04) −0.01 (0.02) 0.07 (0.06) 0.01 (0.01) −1.04*** (0.27) 0.08 (4.14) Feb 2007– Oct 2015 105 CREDIT = credit to the private sector as percentage of gross domestic product, IGB2YR = 2-year government bond yield, IPIYOY = year-on-year percentage change in industrial production, NEER = nominal effective exchange rate, RISK = global bond market volatility index, TB3M = 3-month government auction rate, TCPIYOY = year-on-year percentage change in consumer price index. Notes: *** represents 1% level of signiﬁcance. Standard errors are in parentheses. Source: Authors’ calculations. appetite, and the nominal effective exchange rate on the nominal yields of Indian government bonds (IGBs) of various tenors are examined using monthly data. Autoregressive distributive lag bounds test results are obtained. When the computed F-statistic value is higher than the upper bound value, the long-run relationships are estimated. The results of the empirical investigation are presented in Tables A2.1– A2.5. An increase in the ratio of credit to nominal GDP leads to slightly higher IGB yields rather than lower yields. The coefﬁcient for the index of the nominal l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 6 8 1 6 4 4 2 2 4 a d e v _ a _ 0 0 1 2 7 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 202 Asian Development Review Table A2.2. Autoregressive Distributive Lag Bounds Test Results for IGB3YR (monthly data) Equation B2.1) IGB3YR = β 35 + β 36TB3M + β 37CREDIT + β 38NEER + β 39RISK B2.2) IGB3YR = β 40 + β 41TCPIYOY + β 42CREDIT + β 43NEER + β 44RISK B2.3) IGB3YR = β 45 + β 46IPIYOY + β 47CREDIT + β 48NEER + β 49RISK B2.4) IGB3YR = β 50 + β 51TB3M + β 52TCPIYOY + β 53CREDIT + β 54NEER B2.5) IGB3YR = β 56 + β 57TB3M + β 58IPIYOY + β 59CREDIT + β 60NEER + β 55RISK + β 61RISK B2.6) IGB3YR = β 62 + β 63TB3M + β 64TCPIYOY + β 65IPIYOY + β 66CREDIT + β 67NEER + β 68RISK Long-Run Relationships Equation B2.1 Equation B2.2 Equation B2.3 Equation B2.4 Equation B2.5 0.35*** (0.04) — — 0.09*** (0.01) 0.01*** (0.01) −0.97*** (0.18) −0.58 (0.99) May 2003– Oct 2015 150 — 0.06 (0.09) — — — 0.36*** (0.04) −0.02 (0.03) — −0.06 (0.14) −0.02 (0.03) −2.78*** (0.64) 15.39 (1.014) −0.01 (0.04) 0.14*** (0.03) 0.03 (0.02) −3.06*** (0.75) −0.24 (3.27) Jan 2007– Aug 2003– Dec 2006– Oct 2015 Oct 2015 Oct 2015 107 147 106 0.04 (0.05) 0.01 (0.01) −0.66*** (0.17) 2.46 (4.00) 0.35*** (0.04) – –0.01 (0.02) 0.09*** (0.01) 0.02** (0.01) –1.07*** (0.22) –1.02 (1.22) Jul 2003– Oct 2015 148 Variable TB3M TCPIYOY IPIYOY CREDIT NEER RISK Constant Time period Number of Observations F–statistic 8.73 5.96 8.04 6.35 7.82 5.02 Equation B2.6 0.34*** (0.04) −0.01 (0.04) −0.01 (0.02) −0.02 (0.05) −0.01 (0.01) −0.73*** (0.23) 7.35* (3.92) Feb 2007– Oct 2015 105 CREDIT = credit to the private sector as percentage of gross domestic product, IGB3YR = 3-year government bond yield, IPIYOY = year-on-year percentage change in industrial production, NEER = nominal effective exchange rate, RISK = global bond market volatility index, TB3M = 3-month government auction rate, TCPIYOY = year-on-year percentage change in consumer price index. Notes: *** represents 1% level of signiﬁcance. Standard errors are in parentheses. Source: Authors’ calculations. effective exchange rate is positive. This implies that as the Indian rupee appreciates (depreciates), IGB yields rise (fall). The estimated coefﬁcient on risk shows that as risk (as measured by global bond market volatility) rises (falls), IGB yields decline (increase). The results from the additional regressions estimated in this Appendix suggest that the ratio of credit to nominal GDP, nominal effective exchange rate, and investors’ risk appetite (volatility) in global bond markets are not important drivers of IGB yields in India. However, the short-term interest rate is always found to be l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 6 8 1 6 4 4 2 2 4 a d e v _ a _ 0 0 1 2 7 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 The Long-Run Determinants of Indian Government Bond Yields 203 Table A2.3. Autoregressive Distributive Lag Bounds Test Results for IGB5YR (monthly data) Equation B3.1) IGB5YR = β 69 + β 70TB3M + β 71CREDIT + β 72NEER + β 73RISK B3.2) IGB5YR = β 74 + β 75TCPIYOY + β 76CREDIT + β 77NEER + β 78RISK B3.3) IGB5YR = β 79 + β 80IPIYOY + β 81CREDIT + β 82NEER + β 83RISK B3.4) IGB5YR = β 84 + β 85TB3M + β 86TCPIYOY + β 87CREDIT + β 88NEER B3.5) IGB5YR = β 90 + β 91TB3M + β 92IPIYOY + β 93CREDIT + β 94NEER + β 89RISK + β 95RISK B3.6) IGB5YR = β 96 + β 97TB3M + β 98TCPIYOY + β 99IPIYOY + β 100CREDIT + β 101NEER + β 102RISK Long-Run Relationships Equation B3.1 Equation B3.2 Equation B3.3 Equation B3.4 Equation B3.5 0.21*** (0.05) — — 0.09*** (0.01) 0.01* (0.01) −0.78*** (0.22) 0.31 (1.36) — 0.02 (0.06) — −0.02 (0.09) −0.01 (0.02) −1.79*** (0.40) 11.65* (6.85) — — −0.01 (0.03) 0.13*** (0.02) 0.02 (0.02) −2.05*** (0.51) 0.20 (2.45) 0.23*** (0.05) 0.01 (0.04) — −0.05 (0.06) −0.01 (0.01) −0.34 (0.22) 10.30** (4.22) Jan 2007– Dec 2006– Aug 2003– Dec 2006– Oct 2015 Oct 2015 107 150 Oct 2015 147 Oct 2015 107 0.21*** (0.05) — −0.02 (0.02) 0.10 (0.01) 0.02* (0.01) −0.89*** (0.27) −0.68 (1.70) Jul 2003– Oct 2015 148 Variable TB3M TCPIYOY IPIYOY CREDIT NEER RISK Constant Time period Number of observations F–statistic 6.60 5.60 6.51 4.02 5.88 5.23 Equation B3.6 0.21*** (0.04) 0.01 (0.04) −0.02 (0.02) −0.03 (0.05) −0.01 (0.01) −0.73*** (0.23) 9.54** (3.91) Feb 2007– Oct 2015 105 CREDIT = credit to the private sector as percentage of gross domestic product, IGB5YR = 5-year government bond yield, IPIYOY = year-on-year percentage change in industrial production, NEER = nominal effective exchange rate, RISK = global bond market volatility index, TB3M = 3-month government auction rate, TCPIYOY = year-on-year percentage change in consumer price index. Notes: ***, **, and * represent 1%, 5%, and 10% levels of signiﬁcance, respectively. Standard errors are in parentheses. Source: Authors’ calculations. positive and statistically signiﬁcant, irrespective of the equations used to estimate the determinants of long-term government bond yields. This particular result is robust and insensitive to any changes in the speciﬁcation. This result supports Keynes’ contention in the case of India. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 6 8 1 6 4 4 2 2 4 a d e v _ a _ 0 0 1 2 7 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 204 Asian Development Review Table A2.4. Autoregressive Distributive Lag Bounds Test Results for IGB7YR (monthly data) Equation B4.1) IGB7YR = β 103 + β 104TB3M + β 105CREDIT + β 106NEER + β 107RISK B4.2) IGB7YR = β 108 + β 109TCPIYOY + β 110CREDIT + β 111NEER + β 112RISK B4.3) IGB7YR = β 113 + β 114IPIYOY + β 115CREDIT + β 116NEER + β 117RISK B4.4) IGB7YR = β 118 + β 119TB3M + β 120TCPIYOY + β 121CREDIT + β 122NEER B4.5) IGB7YR = β 124 + β 125TB3M + β 126IPIYOY + β 127CREDIT + β 128NEER + β 123RISK + β 129RISK B4.6) IGB7YR = β 130 + β 131TB3M + β 132TCPIYOY + β 133IPIYOY + β 134CREDIT + β 135NEER + β 136RISK Variable TB3M TCPIYOY IPIYOY CREDIT NEER RISK Constant Time period Number of Observations Long-Run Relationships Equation B4.1 Equation B4.3 Equation B4.4 Equation B4.5 0.22*** (0.08) — — — 0.18*** (0.05) 0.03 (0.04) — — 0.09*** (0.02) 0.02* (0.01) −0.19 (0.41) −0.17 (1.90) −0.02 (2.28) 0.13*** (0.02) 0.02 (0.02) −1.69*** (0.45) −0.02 (0.03) Jan 2007– Aug 2003– Dec 2006– Oct 2015 Oct 2015 Oct 2015 107 147 150 −0.07 (0.06) −0.02 (0.01) −0.15 (0.24) 11.89*** (4.41) 0.15** (0.06) – –0.02 (0.02) 0.10*** (0.02) 0.02* (0.01) –0.83*** (0.30) –0.40 (1.86) Jul 2003– Oct 2015 148 F–statistic 3.17 2.90 5.91 4.28 4.97 3.69 Equation B4.6 0.18*** (0.05) 0.03 (0.05) −0.02 (0.02) −0.08 (0.06) −0.01 (0.01) −0.28 (0.31) 12.39*** (4.59) Feb 2007– Oct 2015 105 CREDIT = credit to the private sector as percentage of gross domestic product, IGB7YR = 7-year government bond yield, IPIYOY = year-on-year percentage change in industrial production, NEER = nominal effective exchange rate, RISK = global bond market volatility index, TB3M = 3-month government auction rate, TCPIYOY = year-on-year percentage change in consumer price index. Notes: ***, **, and * represent 1%, 5%, and 10% levels of signiﬁcance, respectively. Standard errors are in parentheses. Source: Authors’ calculations. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 6 8 1 6 4 4 2 2 4 a d e v _ a _ 0 0 1 2 7 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 The Long-Run Determinants of Indian Government Bond Yields 205 Table A2.5. Autoregressive Distributive Lag Bounds Test Results for IGB10YR (monthly data) Equation B5.1) IGB10YR = β 137 + β 138TB3M + β 139CREDIT + β 140NEER + β 141RISK B5.2) IGB10YR = β 142 + β 143TCPIYOY + β 144CREDIT + β 145NEER + β 146RISK B5.3) IGB10YR = β 147 + β 148IPIYOY + β 149CREDIT + β 150NEER + β 151RISK B5.4) IGB10YR = β 152 + β 153TB3M + β 154TCPIYOY + β 155CREDIT + β 156NEER B5.5) IGB10YR = β 158 + β 159TB3M + β 160IPIYOY + β 161CREDIT + β 162NEER + β 157RISK + β 163RISK B5.6) IGB10YR = β 164 + β 165TB3M + β 166TCPIYOY + β 167IPIYOY + β 168CREDIT + β 169NEER + β 170RISK Long-Run Relationships Variable TB3M TCPIYOY IPIYOY CREDIT NEER RISK Constant Time period Number of observations Equation B5.1 0.66*** (0.15) — — 0.04 (0.04) 0.04 (0.03) 2.04* (1.08) −4.69 (4.83) Mar 1999– Oct 2015 199 Equation B5.4 0.17*** (0.05) 0.07* (0.04) — −0.09 (0.06) −0.01 (0.01) −0.00 (0.24) 12.88*** (4.51) Dec 2006– Oct 2015 107 Equation B5.5 0.66*** (0.15) — −0.05 (0.07) 0.05 (0.05) 0.06 (0.04) 1.72 (1.17) −6.74 (6.36) Jun 1999– Oct 2015 197 F-statistic 3.51 2.89 2.83 4.19 3.08 3.59 Equation B5.6 0.17*** (0.05) 0.07 (0.04) −0.01 (0.02) −0.10 (0.06) −0.01 (0.01) 0.08 (0.31) 13.17*** (4.66) Feb 2007– Oct 2015 105 CREDIT = credit to the private sector as percentage of gross domestic product, IGB10YR = 10-year government bond yield, IPIYOY = year-on-year percentage change in industrial production, NEER = nominal effective exchange rate, RISK = global bond market volatility index, TB3M = 3-month government auction rate, TCPIYOY = year-on- year percentage change in consumer price index. Notes: ***, **, and * represent 1%, 5%, and 10% levels of signiﬁcance, respectively. Standard errors are in parentheses. Source: Authors’ calculations. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 1 1 6 8 1 6 4 4 2 2 4 a d e v _ a _ 0 0 1 2 7 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 The Long-Run Determinants of Indian image

Descargar PDF