Examining Monetary Policy Transmission in the - Am MIT spezialisierte KI-Forschung

Examining Monetary Policy Transmission in the
People’s Republic of China–Structural Change
Models with a Monetary Policy Index+
∗
PAUL G. EGAN AND ANTHONY J. LEDDIN

This paper estimates augmented versions of the Investment–Saving curve for the
People’s Republic of China in an attempt to examine the relationship between
monetary policy and the real economy. It endeavors to account for any structural
break, nonlinearity, or asymmetry in the transmission process by estimating
a breakpoint model and a Markov switching model. The Investment–Saving
curve equations are estimated using a Monetary Policy Index, which has been
calculated using the Kalman ﬁlter. This index will account for the various
monetary policy tools, both quantitative and qualitative, that the People’s Bank
of China has used over the period 1991–2014. The results of this paper suggest
that monetary policy has an asymmetric affect depending on the level of output
in relation to potential, and that the People’s Republic of China’s exchange rate
policy has restricted the effectiveness of the People’s Bank of China’s monetary
policy response.

Schlüsselwörter: IS curve, Kalman ﬁlter, monetary policy, People’s Bank of China,
structural change
JEL-Codes: E12, E42, E58

ICH. Einführung

The dynamics of monetary policy transmission is arguably the most
comprehensive and yet rapidly expanding research area in the discipline of
macroeconomics. Taylor (1995) describes the monetary policy transmission channel
as the process by which a central bank’s monetary policy instruments exert inﬂuence
on macroeconomic variables such as prices, output, and employment. In most
advanced economies, the operating target for the conduct of monetary policy is
the interest rate. Zum Beispiel, Die Vereinigten Staaten (US) Federal Reserve has the Fed
Funds rate, the European Central Bank has the main reﬁnancing rate, and the Bank

+ADB recognizes “China” as the People’s Republic of China.
∗Paul G. Egan (Korrespondierender Autor): School of Economics and Finance, University of St. Andrews, Großbritannien
and Kemmy Business School, University of Limerick, Ireland. Email: paul.egan@ul.ie; Anthony J. Leddin: Kemmy
Business School, University of Limerick, Ireland. Email: anthony.leddin@ul.ie. The authors would like to thank the
managing editor and two anonymous referees for helpful comments and suggestions. The ﬁnancial support of the
Irish Research Council and The Paul Tansey Economics Postgraduate Research Scholarship is greatly appreciated.
Es gilt der übliche Haftungsausschluss.

Asiatischer Entwicklungsbericht, Bd. 33, NEIN. 1, S. 74–110

C(cid:3) 2016 Asiatische Entwicklungsbank
und Institut der Asiatischen Entwicklungsbank

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 75

of England has the base rate. It has been argued, Jedoch, that the People’s Republic
of China’s (VR China) central bank, the People’s Bank of China (PBOC), uses a variety
of policy instruments, both quantitative and qualitative, and so the use of a single
interest rate variable may not give an accurate representation of the PBOC’s monetary
Haltung (sehen, Zum Beispiel, Geiger 2008, He and Pauwels 2008, Ma 2014). While some
researchers suggest that continued liberalization in the ﬁnance sector has improved
its effectiveness (Fernald, Hsu, and Spiegel 2014), most studies have found that the
interest rate channel in the PRC has been largely ineffective. Since the reforms in
1978, the foundations of the PRC’s monetary policy have instead been built on a ﬁxed
exchange rate, strict controls on capital ﬂows, and a wide selection of administrative
and qualitative policy tools. In der Vergangenheit 30 Jahre, the PRC’s macroeconomic
dynamics have been characterized mainly by high gross domestic product (BIP)
growth accompanied by erratic business cycle ﬂuctuations. Despite average annual
growth of almost 10% per annum over the last 3 decades, the PRC’s output volatility
has remained consistently high. An IMF (2011) paper states that the PRC’s output
volatility is twice as high as that of the US. In recovering from the global ﬁnancial
crisis of 2008–2009, the PRC faced serious credit-fueled inﬂationary concerns. Der
PRC’s monetary authorities addressed this by raising banks’ reserve requirement
ratios. Jedoch, in the pursuit of higher ﬁnancial openness and exchange rate
Stabilität, the PRC is facing the crucial trade-off of having to give up monetary
policy independence. This is a perfect example of the trilemma, or impossible
trinity problem.1

With this is mind, understanding the PBOC’s instruments of monetary policy
is important in examining how the transition mechanisms affect the real economy.
To fully analyze what drives business cycle behavior in the PRC, it is important
to carry out a robust study of the relationship between monetary policy, the credit
Markt, and the real economy while allowing for PRC-speciﬁc characteristics. Es ist
also crucial to examine if these relationships have changed during the estimation
Zeitraum (1991–2014), given the large number of reforms and institutional changes
that typiﬁed this period. This paper will attempt to do so by estimating different
variations of an Investment–Saving (IS) curve—both a traditional interest rate IS
curve and a model estimated using a composite policy index that has been calculated
using a Kalman ﬁlter in a State Space Model (SSM) bilden. This index may give a more
accurate representation of the instruments at the disposal of the PBOC. This paper
will also test, and account for where appropriate, structural breaks, nonlinearity,
and asymmetry in the time series to determine if the response or effect of monetary
policy has changed or switched in any signiﬁcant way.

1A fundamental contribution of the Mundell-Fleming framework, the impossibly trinity states that an economy
may choose two but not all three of the policy goals of monetary policy independence, a ﬁxed exchange rate, and full
capital mobility.

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76 ASIAN DEVELOPMENT REVIEW

The paper presents three main ﬁndings. First of all, unlike the majority of the
literature in this area, our results ﬁnd that a standard IS curve equation using a simple
PBOC lending rate has a statistically signiﬁcant impact on the real economy, albeit a
small one. Zweitens, a composite measure of the monetary policy instruments would
seem to give a better explanation of the monetary policy transmission channel once
structural breaks, asymmetry, and nonlinearity are accounted for. The breakpoint
model ﬁnds that the PRC’s monetary policy reaction has declined since 1995, Und
we suggest that this is the result of the adoption of the dollar peg exchange rate
regime in 1994. Endlich, the results of the Markov switching (MS) model indicate
that the PBOC’s monetary policy instruments have a stronger effect on the real
economy when output is operating at or above potential (positive output gap), Aber
has less of an effect when output is operating below potential (negative output gap).
The paper is structured as follows. Section II gives an overview of the literature on
monetary policy transmission in the PRC during the reform period (1991–2014).
Section III outlines the methodology used in estimating our IS curve. Section IV
outlines the data used in our estimations and gives a detailed description of the
estimated Monetary Policy Index (MPI). Section V estimates the various IS models
and comments on the results of each. Section VI performs robustness tests on our
results. Section VII concludes.

II. Literature Review

A number of seminal papers have been written relating the level of aggregate
demand to monetary policy. Bernanke and Blinder (1992), Blanchard (1990), Und
Friedman (1995) are all good examples of the theory that in advanced economies
the level of real output is highly responsive to monetary policy. There is, Jedoch,
a separate branch of research that suggests monetary policy has had little or
diminishing impact on the real economy (sehen, Zum Beispiel, Goodhart and Hofmann
2005). The New Keynesian model of monetary policy has become the standard tool
for the analysis of the monetary policy transmission channel. This model consists
of a Phillips curve, an IS curve, and a monetary policy rule. According to Goodhart
and Hofmann (2005), the IS curve represents the intertemporal Euler consumption
equation. It relates the output gap to the expected future output gap and the real
interest rate: the higher the interest rate, the lower the output. A great deal of
research on this topic in the PRC has focused on understanding the impact of
interest rate changes on investment, which accounts for a particularly large share of
GDP and growth in the PRC and is an important driver of business cycle volatility
(Conway et al. 2010, Liu and Zhang 2010, Kuijs 2006). The PRC’s authorities
have traditionally relied mainly on administrative instruments and an array of both
qualitative and quantitative measures in conducting monetary policy, with interest
rates playing a less prominent role (Koivu 2005). So far, the majority of the literature

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 77

has supported this argument as macroeconomic evidence of a signiﬁcant negative
relationship between interest rate changes and capital formation in the PRC has
been weak. Geiger (2006) argues that changes in interest rates have limited impact
on aggregate macro variables and that the transmission of monetary policy via the
interest rate channel is distorted. Laurens and Maino (2007) argue that there is no
signiﬁcant link between the PRC’s short-term interest rates and movements in GDP.
Mehrotra (2007) examines the role of interest rate channels in the PRC; Hongkong,
China; and Japan using a structural vector autoregression model and ﬁnds that while
there is strong evidence of the interest rate channel as a monetary policy tool for
both Japan and Hong Kong, China, the same cannot be said for the PRC. The limited
importance of the interest rate channel in the PRC is attributed to the implementation
of interest rates by administrative measures rather than market-determined interest
Tarife.

The majority of studies analyzing aggregate demand in the PRC have used
standard linear models and found little or no evidence of a relationship between
output and monetary policy. Koivu (2009) argues that the reforms and structural
breaks during 1998–2007 prevented the estimation of a stable credit demand equation
for the PRC. To remedy this, the author estimates the model across two subsample
periods, accounting for these structural breaks and reforms. The results seem to
support the ﬁndings of previous studies that the link from interest rates to the
real economy is still quite weak in the PRC. The author did, Jedoch, ﬁnd that
the link had strengthened toward the end of the estimation period, suggesting that
interest rates have increased in importance along with continued reform in the
PRC’s ﬁnance sector. Qin et al. (2005) ﬁnd that the overall impact of monetary
policy on the real sector of the macroeconomy is small and insubstantial, vorschlagen
that the PRC’s monetary policy instruments are not effective tools for controlling
output, investment, or employment. In contrast to this, many authors have found that
there is a negative link between interest rates and macroeconomic aggregates in the
VR China. Girardin and Liu (2007) use a vector autoregression model to investigate the
relationship between interest rates and output in the PRC and ﬁnd that a negative
relationship does exist, particularly in the latter half of the sample period 1997–2005.
While Conway et al. (2010) argue that an IS equation for the PRC is difﬁcult to
estimate, the authors’ estimation for 2000–2007 found that both the interest rate and
the exchange rate have a statistically signiﬁcant impact on the real economy in the
VR China, even if this impact is relatively small.

There has been very little agreement in the mainstream literature regarding
the asymmetric effect of monetary policy; das ist, whether monetary policy has a
greater effect across different stages of the business cycle. Using US data, Ravn
and Sola (2004) and Weise (1999) ﬁnd that the transmission of monetary policy
is very much symmetric. In a more recent paper, Tenreyro and Thwaites (2013)
also suggest that monetary policy transmission has asymmetric effects, mit dem
authors ﬁnding a greater effect on output (and inﬂation) in an expansion. Dolado,

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78 ASIAN DEVELOPMENT REVIEW

Maria-Dolores, and Ruge-Murcia (2005); Peersman and Smets (2001); Arag´on and
Portugal (2009); Und, more recently, Barigozzi et al. (2014) have also investigated the
topic of asymmetric monetary policy in eurozone economies. Despite these studies,
across advanced economies, no real consensus has been reached in this area of
Forschung. Infolge, the topic and its policy implications have been largely ignored
in the mainstream monetary policy literature.2 This paper will add to the body of
research by testing and accounting for asymmetry in the PRC’s economy over the
last 25 Jahre. Jedoch, the huge difference between the PRC’s economy and that
of the US and the eurozone requires innovative and perhaps unconventional tools
to investigate the monetary policy transmission channel. The presence of structural
breaks, changes, nonlinearity, and asymmetry in the transmission channel may be
even more prominent in the PRC. There are many reasons to make this inference. Das
paper examines the monetary policy reactions of the PBOC since late 1991, welche
is often regarded as the start of the PRC’s second reform era. Während dieser Zeit, Die
PBOC endeavored to pursue a more market-oriented monetary policy framework,
which included greater use of indirect instruments. The period also coincided with
other institutional reforms and changes that may have greatly affected the monetary
policy transmission channel over time.

While there has been a great deal of literature chronicling the PRC’s economic
Politik, in particular the effect of changes in exchange rate policy, less attention has
been paid to estimating an indicator for the monetary policy stance and very few
studies have accounted for the asymmetric affect that these policies have had on the
level of output. Xiong (2012) computes a monetary policy index using an ordered
probit model, but stops short of differentiating between asymmetric responses to the
PBOC’s actions due to changes in the state of the economy, stating that this warrants
further investigation.3 Girardin, Lunven, and Ma (2014) build on the work of He
and Pauwels (2008) and Xiong (2012) by constructing an aggregate measure of the
PRC’s monetary policy stance using price, quantitative, and administrative measures.
Endlich, Petreski and Jovanovic (2013) create their own MPI using a weighted
average of quantitative and qualitative instruments, which is in turn included in
the model instead of the interest rate. The estimation of the PRC’s monetary policy
instruments in this paper is based on this work as it also uses a Kalman ﬁlter to
extract the qualitative variables. While these papers may have focused on ﬁnding an
appropriate measurement of monetary policy in the PRC, one oversight in this area
of the literature has been the failure to account for structural breaks, asymmetry,
and nonlinearity in the transmission process. As has been discussed, this could be
particularly relevant to an economy like the PRC’s, which has undergone signiﬁcant
change and reform. To our knowledge, this research is the ﬁrst to apply both a

2This argument was made by Tenreyro and Thwaites (2013), who pointed to examples such as Christiano,

Eichenbaum, and Evans (2005); and Woodford (2003).

3An ordered probit model in this case assigns a number depending on the type of policy that is observed or

believed to have been carried out: –1 is contractionary, 0 is neutral, Und 1 is expansionary.

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 79

linear model with structural breaks and a nonlinear technique (MS model) to the
transmission process, as well as a composite index of the monetary policy stance.

III. Methodik

The traditional IS curve takes the form of Equation 1:

¯yt = Et ( ¯yt+1) − c[it − Et (πt+1)] + vt

(1)

where ¯yt is the output gap, (it − Et (πt+1)) is the real interest rate, vt is a demand-side
shock, and c is the response of output to changes in the real interest rate.

Gleichung 1 is a purely forward-looking equation and relates the output gap to
the expected future output gap and the real interest rate. In empirical applications,
Jedoch, purely forward-looking models have been found to be inconsistent with
the dynamics of aggregate demand (Estrella and Fuhrer 2002). daher, A
backward-looking speciﬁcation is often preferred in order to match the lagged
and persistent responses of output to monetary policy measures that are found in the
Daten (Rudebusch 2002). Backward-looking speciﬁcations have been used in many
empirical studies, including Fuhrer and Moore (1995), Rudebusch and Svensson
(1998), and Goodhart and Hofmann (2005). We can therefore rewrite the equation
als

¯yt = a + B ( ¯yt−1) − c [(it−1) − (πt−1)] + dvt + εt

(2)

This purely backward-looking speciﬁcation of the PRC’s IS equation can
be used in our estimations to obtain dynamics that match those of available
economic data most consistently. Macroeconomic data usually shows a high degree
of persistence in both inﬂation and output (Estrella and Fuhrer 2002). According to
Ball (1999, 128), the advantage of the backward-looking speciﬁcation is that it “is
similar in spirit to the more complicated macro econometric models of many central
banks.”

Since the PBOC has adopted a wide range of monetary policy instruments
over the last 3 decades, the use of a single variable to adequately capture a monetary
policy stance may not be appropriate. A good measure of the monetary policy stance
should be able to indicate, either qualitatively or quantitatively, whether policy
is becoming contractionary, expansionary, or remaining unchanged (Xiong 2012).
Most studies in this area focus on the movement of a single policy variable such as the
lending or the deposit interest rate (Xie and Xiong 2003, Conway et al. 2010) oder der
M2 money supply (Burdekin and Silkos 2008, Koivu 2008). It is commonly accepted
that monetary policy in the PRC consists of both quantitative instruments (interest
Tarife, deposit rates, reserve requirement) and qualitative instruments. Qualitative
instruments include persuasion and speciﬁc directives such as telling banks which

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80 ASIAN DEVELOPMENT REVIEW

companies to lend to, a practice that is often referred to as window guidance. Das
policy uses benevolent compulsion to persuade banks and other ﬁnancial institutions
to stick to ofﬁcial guidelines. Central banks put moral pressure on ﬁnancial players
to make them operate consistently with national needs (Geiger 2008). This usually
involves inﬂuencing market participants through announcements rather than a set of
strict rules. Many authors—including Goodfriend and Prasad (2006); Bell and Feng
(2013); and Girardin, Lunven, and Ma (2014)—have emphasized the importance
of these qualitative instruments with regard to the conduct of the PRC’s monetary
Politik, but the problem from a modeling point of view is that there is no data
available for such instruments. How can one model or quantify whether the PBOC
informs a particular industry or company to follow their instructions? daher,
this qualitative instrument variable must be calculated. Once predicted, this series
can be used to create an index composed of both the changes in quantitative and
qualitative instruments that would more accurately represent the monetary policy
stance of the PBOC. The technique of building an index for monetary policy using a
variety of techniques has been carried out by Gerlach (2007); Petreski and Jovanovic
(2013); and Girardin, Lunven, and Ma (2014).

IV. Data

Interest Rate and Demand Shock

All variables used in the IS curve estimations are plotted in Figure 1. Seasonal
factors have been adjusted for where appropriate. The time period of Q1 1991–Q3
2014 corresponds with the start of the second reform era and was chosen to capture
the dynamics of this period of structural changes and institutional reforms. Tisch 1
reports the unit root tests for all the variables used in our estimations. The results
conﬁrm that all variables pass the test for integration of order zero (I ∼ [0]) and are
therefore stationary.

The real interest rate is calculated as (it − πt ) where it

is the lending
interest rate and πt
is the annual change in quarterly Consumer Price Index.
Both of these series are available in the International Monetary Fund’s (IMF)
International Financial Statistics and the PRC’s National Bureau of Statistics. Für
the demand shock, the PRC’s seasonally adjusted export data, also found in the
IMF’s International Financial Statistics, is used. The huge importance of the PRC’s
exports to its growth model over the last 2 decades has been discussed extensively
in the literature (Liu, Burridge, and Sinclair 2002; Guo and N’Diaye 2009; Amiti
and Freund 2010), and therefore this is the most logical and appropriate demand
shock for the PRC’s economy. As has been mentioned, the PBOC relies on a basket
of different policy tools in the conduct of monetary policy. daher, an MPI is
required to accurately examine the stance of the PBOC. As no dataset for such
an index exists, it will be calculated using the Kalman ﬁlter technique. Given the

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 81

Figur 1. IS Curve Estimation Variables, Q1 1991–Q3 2014

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IS = Investment–Saving.
Sources: National Bureau of Statistics. http://www.stats.gov.cn/english/; International Monetary Fund. International
Financial
and Oxford
Economics. Global Economic Databank. http://www.oxfordeconomics.com/forecasts-and-models/cities/china-cities
-and-regional-forecasts/overview (all accessed January 2015); and authors’ calculations.

http://data.imf.org/?sk=5DABAFF2-C5AD-4D27-A175-1253419C02D1;

Statistics.

Tisch 1. IS Curve Unit Root Test (augmented Dickey-Fuller)

Variable

Output gap, ¯yt

Real interest rate, it − Et (πt+1)

Demand shock ((cid:4) Exports), vt

Monetary Policy Index, MPIt

2 lags
−3.23∗
(0.04)
−3.14∗∗
(0.03)

3 lags
−3.50∗∗
(0.03)
−2.99∗∗
(0.05)

5 lags
4 lags
1 Verzögerung
−3.05∗∗
−5.76∗∗
−2.60∗
(0.04)
(0.04)
(0.09)
−2.73∗∗
−2.60∗
−2.19
(0.05)
(0.09)
(0.14)
−3.19∗∗
−4.54∗∗∗ −4.82∗∗∗ −5.69∗∗∗ −3.92∗∗
(0.00)
(0.05)
(0.02)
(0.00)
−5.07∗∗∗ −4.35∗∗∗ −4.84∗∗∗ −3.76∗∗∗ −2.87∗
(0.09)
(0.00)
(0.00)

(0.00)

IS = Investment–Saving.
Notes: Rejection of the unit root hypothesis at the 10%, 5%, Und 1% level is indicated with ∗, ∗∗, and ∗∗∗,
jeweils. P-values are in parentheses. Critical values for test with constant are −2.5, −2.8, and −3.4.
Quelle: Berechnungen der Autoren.

importance of this variable for the analysis of the PRC’s monetary policy, section
IV.C describes the theory, rationale, and calculations behind the MPI. Endlich, für
the excess demand variable, the output gap is used. As the output gap variable is key
to this paper and is the dependent variable for almost all econometric estimations,
the next section discusses its calculation and interpretation.

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82 ASIAN DEVELOPMENT REVIEW

The People’s Republic of China’s Output Gap

The IMF (2015) deﬁnes the output gap as the deviation of actual from potential
output as a percentage of potential. In the equation below, y denotes actual output
(measured by real GDP) and y∗ represents potential output, which is deﬁned as
the output an economy could produce if all factors of production were operating at
their full employment rates of capacity. The output gap can then be represented as
¯y = y−y∗

y∗ × 100.
Gerlach and Peng (2006) identify two broad approaches to estimating

potential output, and thus the output gap, for the PRC:4

• a production function approach, which makes use of information regarding
the sources of growth (d.h., factor accumulation and the state of total factor
productivity); Und

• by identifying the trend in real GDP with potential output and using time

series techniques, such as ﬁltering, to estimate it.

We will examine these two techniques for our output gap data for the PRC.

Production Function

The main advantage of the production function approach is that it provides
an understanding of the sources of growth. Jedoch, to estimate a level of potential
output in this way requires high-quality data on the capital stock and labor force. Der
reasons that this may be an issue for the PRC have been well documented (sehen, für
Beispiel, Holz 2014). Scheibe (2003) devotes an entire paper to the calculation of
the PRC’s output gap. The author points out the many issues in estimating potential
output for the PRC, ranging from the limited number of postreform observations,
badly measured data, absence of proxies for capacity utilization or hours worked,
no reliable inventory data, and signiﬁcant structural changes.

This paper considers a production function output gap calculated by
the Oxford Economics Global Economic Databank (Figur 2), given that this
organization has access to data that are not widely available. This variable is
estimated as follows: “We construct our measure of potential output bottom-up
by looking at the inputs into the production function ([Arbeit] supply, capital
accumulation, and the components of [total factor productivity]). Subsequently,
we benchmark this against actual GDP to a period where we feel the economy was

4More recently Zhang and Murasawa (2011, 2012) and Zhang et al. (2013) have estimated a measure of the

output gap for the PRC based on a multivariate dynamic model.

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 83

Figur 2. Production Function Output Gap

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Quelle: Oxford Economics. Global Economic Databank. http://www.oxfordeconomics.com/forecasts-and-models
/cities/china-cities-and-regional-forecasts/overview (abgerufen im Januar 2015).

operating at potential to ensure the level of actual and potential GDP at that moment
is equal and the output gap is zero.”5

Filtering

A frequently used tool in macroeconomics is the Hodrick–Prescott (HP) ﬁlter,
which decomposes actual output into a long-run trend and cyclical components. Das
statistical method does not use any information regarding the determinants of each
of the components, but provides a useful approximation of potential output growth.
While the time series approach is easy to implement, it suffers from the drawback
that it provides no economic understanding of the sources of growth. Daher, Es
is arguably best seen as a complement to the more rigorous production function
Ansatz (Gerlach and Peng 2006). daher, we will calculate an HP ﬁlter output
gap using GDP data from the PRC’s National Bureau of Statistics to compare to
Oxford Economics’ estimations.

To get a complete comparison for as long a period as possible, we use the
earliest available data and calculate an HP ﬁlter output gap from 1987 Zu 2014. Der
PRC’s quarterly GDP data can be seen in Figure 3(A) and the HP ﬁlter applied to
this series in Figure 3(B). The calculated output gap is presented in Figure 4.

5This includes data on wages; Arbeit; primary, secondary, and tertiary activities; employment; income and
consumer spending; retail sales; and other series of importance. Oxford Economics, Global Economic Databank.
http://www.oxfordeconomics.com/forecasts-and-models/cities/china-cities-and-regional-forecasts/overview
(abgerufen im Januar 2015).

84 ASIAN DEVELOPMENT REVIEW

Figur 3. Hodrick-Prescott Filter of the PRC’s GDP Data

BIP = Bruttoinlandsprodukt, HP = Hodrick-Prescott, VR China = Volksrepublik China.
Sources: National Bureau of Statistics; and authors’ calculations.

Figur 4. Hodrick-Prescott Filter Output Gap

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Sources: National Bureau of Statistics; and authors’ calculations.

Comparison of Both Series

Both the production function output gap and the HP ﬁlter output gap are
plotted together in Figure 5. There is a noticeable difference between the two in the
1987–1989 period: the HP ﬁlter estimation shows a highly positive output gap, while
the production function gap shows a negative value. The signiﬁcant difference in
the HP ﬁlter estimation in this period may be explained by the arguments of Giorno,

EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 85

Figur 5. Comparison of the PRC’s Output Gaps, 1987–2014

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HP = Hodrick-Prescott, VR China = Volksrepublik China.
Sources: Oxford Economics. Global Economic Databank. http://www.oxfordeconomics.com/forecasts-and-models
/cities/china-cities-and-regional-forecasts/overview (abgerufen im Januar 2015); and authors’ calculations.

Roseveare, and Van Den Noord (1995), who suggest the HP ﬁlter method often
falls victim to an endpoint problem. In part, this reﬂects the ﬁtting of a trend line
symmetrically through the data. If the beginning and the end of the dataset do not
reﬂect similar points in the cycle, then the trend will be pulled upward or downward
toward the path of actual output for the ﬁrst and last few observations. Zum Beispiel,
for those economies that have been slower to emerge from a recession, an HP ﬁlter
will tend to underestimate trend output growth for the current period. Other than
this discrepancy, the two series seem to follow a similar pattern of output operating
above or below potential in similar periods.

What is striking about both series is that despite GDP growth, which has
averaged almost 10% seit 1987, the PRC’s output gap has remained negative for
the majority of the review period. This point has also been made by the IMF (2012).
This problem of excess capacity can be highlighted by examining both estimates
of the PRC’s output gap.6 It is indicative of a growth model that has relied on high
levels of investment and exports combined with surplus labor. In the past, die VR China
high levels of investment created capacity beyond its ability to consume. The excess
capacity has often been absorbed outside its borders by the exceptionally strong
global demand for the PRC’s exports (IMF 2014). As the 2008–2009 global ﬁnancial

6Several factors are believed to contribute to the problem of excess capacity in the PRC. These include very
high rates of saving and investment, a massive transfer of unskilled labor from the agriculture to urban nonagriculture
sectors, cheap labor costs, low levels of education, and low levels of technical innovation (Wang, Fan, and Liu 2008).
Zusätzlich, there is the PRC’s institutional and political environment, which often results in central and regional
governments propping up largely inefﬁcient state-owned enterprises.

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86 ASIAN DEVELOPMENT REVIEW

crisis highlighted, Jedoch, the PRC can no longer rely on the same blistering level
of demand for its exports that it enjoyed in the early and mid-2000s.

The similarity of these two techniques using two different datasets adds
robustness to the use of the PRC’s output gap in our empirical estimations. Gegeben
that the production function is often seen as the optimal methodology in estimating
an economy’s potential output, we use the production function estimate of potential
output measure as calculated by Oxford Economics Global Economic Databank.

A Monetary Policy Index for the People’s Republic of China

Unobserved Components Model

Quantifying unobserved variables is a common problem in empirical research.
Often in macroeconomics, we come across variables that play an important role in
theoretical models but which we cannot observe. Unobserved component models
have been used in economics research in a variety of problems when a variable that
is supposed to play some relevant economic role is not directly observable. Während
a particular variable may not be directly observable, the unobserved component
model using a Kalman ﬁlter allows researchers to predict how this unobserved
variable might be behaving.7 For example, unobserved components have been used
in modeling agents’ reaction to permanent or transitory changes in the price level
(Lucas 1976), modeling the credibility of the monetary authority (Weber 1992), Und
measuring the persistence (long-term effects) of economic shocks (Cochrane 1988).
The statistical treatment of an unobserved components model is based on the SSM
bilden. In the SSM, the unobserved components, which depend on the state vector,
are related to the observations by a measurement equation. A transition equation
then models the dynamics of the unobserved variables or states. While linear
regression models use exogenous variables to distinguish the explained variation
from the unexplained variation, SSMs rely on the dynamics of the state variables
and the linkage between the observed variables and state variables to draw statistical
inference about the unobserved state. This allows us to estimate the unknown
parameters of the model. The Kalman ﬁlter is the basic recursion for estimating the
state, and hence the unobserved components, in a linear SSM (Harvey, Koopman,
and Sheppard 2004). The useful thing about the unobserved components model
is that if the unobserved variable is closely linked with an observed variable, es ist
possible to predict the value of that variable from the observed values. The purpose of
using this technique in this paper is to make inferences about the unobservable policy
instruments that the PBOC carry out given a set of observable policy instruments.

7Additional information on this technique can be found in Cuthbertson, Hall, and Taylor (1992); Kim and

Nelson (1999); and Commandeur and Koopman (2007).

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 87

We can categorize the monetary policy tools of the PBOC as either

quantitative or qualitative:

• Quantitative monetary policy tools, often known as general tools, are the
instruments used most often by central banks and monetary authorities in
advanced economies. These include bank lending and deposit rates, reserve
requirements, and open market operations. The quantitative instruments
used in this paper were chosen based on information from various PBOC
ofﬁcial publications. Zum Beispiel, „[Die] monetary policy instruments
applied by the PBOC include reserve requirement ratio, central bank base
interest rate, rediscounting, central bank lending and deposit rate, offen
market operations, and other policy instruments speciﬁed by the State
Council.”8

• Qualitative monetary policy tools, described as selective tools, often
involve direct administrative pressure on ﬁnancial players to make them
operate consistently with national needs (Geiger 2008). This style of
institutional coercion is one of the PBOC’s unique characteristics and
it reﬂects the PRC’s hierarchical order. It also makes the monetary policy
reactions of the PBOC very difﬁcult to quantify and model accurately. Der
most well-known of these instruments is window guidance, also known as
moral suasion or jawboning.9 Despite the word “guidance,” which implies
a voluntary aspect in the system, the PBOC has had a major inﬂuence on the
lending decisions of ﬁnancial institutions, especially the four state-owned
commercial banks (Ikeya 2002).

A key consideration of this paper is how to quantify the latter of these two
monetary policy tools; das ist, how to link the unobserved variables (qualitativ) to the
observed variables (quantitative)? Let us suppose that the PRC’s money supply (M2)
changes in a way that would be consistent with a certain monetary policy response.10
Let us also assume, Jedoch, that none of the standard quantitative policy instruments
(z.B., interest rates, open market operations, and reserve requirement ratios) that we
would expect to inﬂuence M2 cannot be held accountable for the deviations. Es ist,
daher, logical to assume that some unobserved qualitative variables might be
responsible for changes in M2. Natürlich, this does not mean that all changes in
M2 not explained by the measurement equation variables will be explained by this

8PBOC. Monetary

Policy

Instruments.

http://www.pbc.gov.cn:8080/publish/english/979/index.html

(abgerufen im Januar 2015).

9There are several other direct control instruments that a central bank can use. These include credit controls
(lending ceilings and ﬂoors) and prudential guidelines (informing commercial banks to exercise particular care in
their operations in order that speciﬁed outcomes are realized).

10M2 is chosen because qualitative instruments are likely to be reﬂected onto the broad money supply (Petreski

and Jovanovic 2013).

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88 ASIAN DEVELOPMENT REVIEW

unobserved variable as there is probably a lot of noise in the M2 data. There is,
Jedoch, likely to be very useful signal or noise-free data. The Kalman ﬁlter is,
daher, used to separate the best signal from the noise.

Setup of the Unobserved Components Model

First of all, we need to specify the quantitative instruments that will inﬂuence
M2. The main quantitative policy instruments used by the PBOC are the base
(discount) rate, reserve requirement ratios, and open market operations. Zweitens,
we include instruments based on the nature of the PRC’s ﬁnancial system. Since the
PRC’s banking and ﬁnancial institutions are dominated by state-owned banks, any
rate changes can be treated as a monetary policy response and so we include both the
lending and deposit rates of these institutions. Endlich, we need to include any other
variable that will have a major inﬂuence on the level of M2. daher, we include
changes in the level of real GDP as this will obviously affect M2 growth. Endlich, Wir
include the nominal effective exchange rate as the exchange rate is heavily managed
and any deviation in its level will also affect growth in M2. While the renminbi
was pegged to the US dollar without any movement between 1994 Und 2005, Die
variable used, the nominal effective exchange rate, varies throughout the estimation
Zeitraum. daher, we can account for the effect that these nominal appreciations
and depreciations, caused by changes in the exchange rate policy or regime, had on
the growth of M2.

Gleichung 3 Und 4 represent the measurement and transition equations,
jeweils. The quarterly change in M2 is chosen as the dependent variable in
the measurement equation because, as mentioned, qualitative instruments are likely
to be reﬂected onto broad money. (cid:4)M2 is then expressed as a function of both the
quantitative and the qualitative monetary policy instruments used by the PBOC.
The transition equation then models the unobservable qualitative instruments as a
ﬁrst-order autoregressive process (AR[1]). The qualitative instrument series is
obtained by a Kalman ﬁlter estimation of this money demand function. The two
equations are written in the following forms:

Measurement equation
(cid:4)M2 = β1 + β2 exchange rate + β3 base rate + β4 reserve requirement
+ β5 lending rate + β6 deposit rate + β7 GDP + β8 Qual + (cid:6)t1

Transition equation
Qual = β9 Qual(−1) + (cid:6)t2

(3)

(4)

The measurement equation links the quantitative variables (β3 base rate +
β4 reserve requirement + β5 lending rate + β6 deposit rate) and changes in the
exchange rate and GDP (β2 exchange rate + β7 GDP) to an unobserved state variable

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 89

(β8 Qual).11 The transition equation then describes the dynamics of this qualitative
instrument.12 This Qual variable in both Equations 3 Und 4 is the vector of the
unobserved variables and describes how these variables evolve over time. The error
terms et1 and et2 are the monetary policy shock and shocks to the qualitative
Instrumente, jeweils. The setup of this unobserved component model assumes
that the only variable affecting the quarterly growth rate of M2 that can have an
AR(1) structure is the unobserved variable and treats all other factors as shocks.
While using this assumption to deﬁne our series for the qualitative variable may at
ﬁrst seem slightly naive, it is justiﬁed for the simple reason that the key variables
that may have an AR(1) structure and still effect changes in M2 have already been
included in the measurement equation. daher, it is logical to assume that the only
important variable that remains for the quarterly change in M2 is this qualitative
Variable. The qualitative variable is intended to capture PBOC actions such as
window guidance, bank directives, credit guidance, and other instructions that are
widely regarded to be very important to the PRC’s banking sector. We expect that the
qualitative variable would inﬂuence M2 as it involves the central bank persuading
commercial banks to take certain steps without the PBOC making any changes to
benchmark rates.

Estimating the Qualitative Variable

The results of the estimations are as follows:

Measurement equation
(cid:4)M2 = 7.5∗∗∗ − 0.04 exchange rate + 0.32 base rate + 0.10 reserve requirement
− 1.4∗ lending rate + 0.95∗∗∗ deposit rate + 0.05 BIP + Qual13

(5)

Transition equation
Qual = −0.02 Qual(−1)

(6)

The measurement equation results show that while the GDP growth rate
and changes to the exchange rate are correctly signed, their coefﬁcients are not
signiﬁcant. The base rate and the reserve requirement ratio are insigniﬁcant and also
incorrectly signed. Tatsächlich, of all the monetary policy tools included in the equation,
only the deposit rate is correctly signed and signiﬁcant. This suggests that for the

11Note the omission of the open market operations variables. The variable for the open market operations
variable moved almost exactly with the base (discount) rate. They deviated at the same periods and by the same
magnitude; daher, all the dynamics are already captured by the base rate.

12The starting values for the parameters in the measurement equation were chosen from an ordinary least

squares (OLS) regression, which is the standard procedure for an estimation of this type.

13∗∗∗, ∗∗, and ∗ denote signiﬁcance at the 1%, 5%, Und 10% level of signiﬁcance, jeweils.

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90 ASIAN DEVELOPMENT REVIEW

Figur 6. PBOC Monetary Policy Index Variables

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PBOC = People’s Bank of China, RRR = reserve requirement ratio.
Sources: National Bureau of Statistics. http://www.stats.gov.cn/english/; International Monetary Fund. International
Financial Statistics. http://data.imf.org/?sk=5DABAFF2-C5AD-4D27-A175-1253419C02D1 (both accessed January
2015); and authors’ calculations.

most part the quantitative variables have had a limited impact on the PRC’s money
supply. This equation obviously suffers from multicollinearity problems, Jedoch,
and so the interpretation of its results must be treated with caution.

The transition equation on the other hand will give the prediction of the
qualitative instruments used by the PBOC. Technically speaking, the transition
equation identiﬁes the latent AR(1) that affects growth in M2. The predicted
series calculated from the estimation can be seen in the bottom center panel of
Figur 6 (Changes in Qualitative Instruments). This series should, broadly speaking,

EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 91

Tisch 2. Coefﬁcient of Variance of Policy Instruments

Index

Deposit Lending Base RRR Qualitative

0.20

0.09

MPIt
0.43
MPI = Monetary Policy Index, RRR = reserve requirement ratio.
Notiz: The coefﬁcients have been normalized.
Quelle: Berechnungen der Autoren.

0.16

0.12

correspond to the selective monetary policy actions of the PBOC. As a simple
Beispiel, the marked increase and decline in the 1992–1995 period may be accredited
to Deng Xiaoping’s southern tour. The spike in the 2008–2009 period may have
captured the stimulus package the PBOC undertook to mitigate the domestic impacts
of the global ﬁnancial crisis. From a simple observation of the series, it would
appear that our qualitative variable measure has succeeded in capturing some of the
important “unobservable” in the PRC’s monetary policy movements.

Calculating the Index

Having obtained an estimated series of the qualitative variable, the monetary
policy index can then be constructed. zuerst, the coefﬁcient of variance of the ﬁve
Instrumente, both qualitative and quantitative, is calculated and their sum normalized
to unity.14 The coefﬁcient of variance is a statistical measure of the dispersion of
data points in a data series around the mean. It is a useful statistic for comparing the
degree of variation from one data series to another even if the means are drastically
different from each other. This technique allows us to examine and compare the
degree of variation of the ﬁve series. The coefﬁcient of variance for the ﬁve variables
can be seen in Table 2. We can see that the main monetary policy tools mentioned
by the PBOC—deposit rate, lending rate, discount rate, and reserve requirement
ratio—play a relatively minor role and seem to change infrequently when compared
to our qualitative instrument. The addition of the qualitative instrument variable
clearly shows the importance of its role as a monetary policy tool.

This is conﬁrmed by examining the changes in all policy variables (Figur 6),
which show that the qualitative instrument variable changes far more frequently than
the other four quantitative variables. The ﬁnal MPI is then calculated as a weighted
average of the changes in the ﬁve policy instruments, using the coefﬁcient of variance
values as weights (Tisch 2). Figur 7 plots the ﬁnal MPI that will be used in the
estimations that follow. An increase in this index corresponds to an expansionary
monetary policy stance and a decrease to a contractionary stance. This is due to the
setup of the weightings of each of the variables. daher, we would expect to see a
positive sign on the monetary policy reaction coefﬁcient in the IS curve estimation.

14The ﬁve instruments are the four quantitative variables (deposit rate, lending rate, base rate, and reserve

requirement ratio) and the estimated qualitative variable.

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Figur 7. Calculated Monetary Policy Index

Quelle: Berechnungen der Autoren.

V. Estimations and Results

Standard Ordinary Least Squares Estimation

Tisch 3 reports the results of the traditional IS curve equation for the PRC
that uses a standard single policy variable (lending rate) as a measure of the
monetary policy stance of the central bank. Both a forward-looking (Gleichung 1) Und
backward-looking (Gleichung 2) IS curve are estimated. While these are the
speciﬁcations most often used to examine monetary policy transmission in advanced
economies, the literature has suggested that they may not provide an accurate
representation of the monetary policy transmission channel in the PRC. The left-hand
column of Table 3 provides the results of the forward-looking IS curve. The ﬁndings
seem to support the arguments made in section III that forward-looking transmission
equations of this kind perform poorly when tested empirically. First of all, the real
interest variable, it − Et (πt ), is incorrectly signed and only signiﬁcant at the 10%
Ebene. The demand shock, represented as a change in exports, is also insigniﬁcant and
incorrectly signed. Endlich, the estimations fail to satisfy tests for both autocorrelation
and stability.15 The center column of Table 3 provides the results of the
backward-looking IS curve (Gleichung 2). This speciﬁcation seems to provide a
better ﬁt for the data. The lag of the output gap is large and highly signiﬁcant,
indicating that shocks to the output gap are quite persistent. The real interest rate
is correctly signed and highly signiﬁcant, which suggests that the lending rate set

15This can be seen by examining the LM F Statistic and the SupF Statistic.

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 93

Tisch 3. IS Curve Estimation—OLS Estimation, Q2 1991–Q3 2014

Monetary

Variable

Forward-Looking Backward-Looking Policy Index
IS Curve (7)

IS Curve (2)

IS Curve (1)

Dependent Variable: PRC Output Gap, ¯yt

Constant
A
Output gap lag
¯yt−1
Output gap expect.
¯yt+1
Real interest rate (Lending)
it − Et (πt )
Monetary Policy Index
MPIt
Demand shock (Exports)
vt
R2
LM F-Stat
SupF-Stat

−0.01
(0.01)
—

1.02∗∗∗
(0.03)
0.03∗
(0.02)
—

−0.01
(0.11)
0.93
4.46∗∗
28.12∗∗∗
(Q4 1994)

0.01∗∗∗
(0.00)
0.90∗∗∗
(0.03)
—

−0.06∗∗∗
(0.01)
—

−0.01
(0.10)
0.94
2.26
14.51
(no break)

−0.01
(0.01)
0.98∗∗∗
(0.03)
—

—

0.12∗∗
(0.04)
0.01∗∗∗
(0.01)
0.94
2.11
17.39∗∗∗
(Q4 1994)

HAC = heteroscedasticity and autocorrelation-consistent, IS = Investment–Saving, OLS = ordinary
least squares, VR China = Volksrepublik China.
Notes: ∗∗∗ = 10% level of statistical signiﬁcance, ∗∗ = 5% level of statistical signiﬁcance, ∗ = 1%
level of statistical signiﬁcance. HAC standard errors are in parentheses.
Quelle: Berechnungen der Autoren.

by the PBOC did have an effect on the real economy. While the coefﬁcient of 0.06
indicates that the magnitude of this effect is quite small, it can be examined further
by estimating the standard deviation (SD) of variables.16 This will give a more
precise indication of the magnitude of the relationship between monetary policy and
the real economy. A 1% SD in the interest rate results in a 0.12% change in the
output gap. The effect of a shock to demand, represented in this paper as a change in
exports, has no signiﬁcant effect on the output gap during the estimation period. Der
presence of structural breaks can also be tested by applying the Quandt–Andrews
SupF statistic (Quandt 1960 and Andrews 1993). Tisch 3 shows that the model
passes the tests for structural breaks, which suggests that this standard IS curve for
the PRC is characterized by a stable and linear relationship. This is an interesting
ﬁnding and is at odds with the majority of research in the area. The result suggests
that a standard IS curve may be an appropriate model for examining the monetary
policy transmission channel in the PRC. There are explanations one can offer as to
why the magnitude of this relationship is quite small. The underdevelopment of the
banking system, market segmentation, and the ineffectiveness of the credit channel,

16These are calculated throughout the paper using the summary statistics that are provided in the Appendix.
The SD is calculated by multiplying the coefﬁcient given for the independent variable by the SD of that variable
divided by the SD of the dependent variable.

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94 ASIAN DEVELOPMENT REVIEW

as well as the fact that the PBOC has also relied on many different tools in the
conduct of monetary policy, are all possible explanations.

As the PBOC has been known to use a variety of different policy instruments,
an index representing these may give a better and more accurate representation of
the PRC’s monetary policy stance. The augmented IS curve model, estimated using
the MPI that was estimated in section IV.C, then takes the following form:

¯yt = a + B( ¯yt−1) + C(MPIt ) + dvt + εt

(7)

The estimation of this augmented IS curve can also be seen in Table 3 (right-hand
column).

Wieder, the persistence of a shock to the output gap is very high. The results,
a coefﬁcient of 0.12, seem to indicate that changes in this combined index have a
stronger effect on the real economy than the interest rate. The positive sign suggests
that an increase in the index corresponds to looser monetary policy and a decrease to
tighter monetary policy. As before, we can examine more rigorously the relationship
between the output gap and the MPI by examining the SD of the variables. A 1% SD
in the MPI leads to only a 0.08% change in the output gap, a change that is actually
smaller than that calculated by the interest rate IS curve. The validity of the results
Sind, Jedoch, compromised by the presence of a structural break. This is observed
by the highly signiﬁcant value of the SupF test in Table 3. In an attempt to remedy
dieses Problem, the next section of the paper estimates the MPI–IS curve using models
that allow for structural breaks and switching between different states or regimes.

Breakpoint Model

The standard linear regression model assumes that the parameters of the
model do not vary across observations. Despite this assumption, structural change,
which is the changing of parameters at dates in the sample periods, plays an
empirically relevant role in applied time series analysis. This is particularly true of
economies such as the PRC that have experienced reform and institutional change.
With this in mind, a linear regression model that is subject to structural change is
estimated. There has been a large volume of work targeted at developing testing
and estimating methodologies for regression models that allow for change. Der
seminal work of Chow (1960) and Quandt (1960) developed the testing procedure
for structural changes in a time series at a single speciﬁed (known) break date. Bai
and Perron (1998, 2003) developed this technique further and attempted to develop
methods that allow for estimation and testing of structural change at unknown break
dates.17 An important feature of this test is that it allows us to test for multiple breaks

17Details of the multiple breakpoint regression model were sourced from Bai (1997); Bai and Perron (1998);

Demers (2003); and Carlson, Craig, and Schwarz (2000).

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 95

Tisch 4. Monetary Policy Index IS Curve with Multiple
Breakpoints, Q2 1991–Q3 2014

Variable

Period 1
(Q2 1991–Q4 1994)

Period 2
(Q1 1995–Q3 2014)

Dependent Variable: PRC’s Output Gap, ¯yt (breakdate: Q4 1994)
−0.01
(0.01)
0.99∗∗∗
(0.04)
0.24∗∗∗
(0.08)
0.04∗
(0.04)

Constant
A
Output gap lag
¯yt−1
Monetary Policy Index
MPIt
Demand shock (Exports)
vt
HAC = heteroscedasticity and autocorrelation-consistent, IS = Investment–Saving,
VR China = Volksrepublik China.
Notes: ∗∗∗ = 10% level of statistical signiﬁcance, ∗∗ = 5% level of statistical
signiﬁcance, ∗ = 1% level of statistical signiﬁcance. HAC standard errors are in
parentheses.
Quelle: Berechnungen der Autoren.

0.01∗
(0.01)
0.96∗∗
(0.03)
0.07∗
(0.04)
0.01
(0.02)

at unknown dates. It is well documented that the effects of ﬁnancial liberalization
and economic reform are difﬁcult to model using standard ordinary least squares
(OLS) regressions, often due to the structural breaks that such events can cause in the
time series (Blangiewicz and Charemza 1999). The Bai–Perron procedure is useful
in such a case as it allows the user to ﬁnd the number of breaks implied by the data
and estimate the timing of the breaks and the parameters of the processes between
breaks. The methodology can be used to estimate multiple structural changes in
a linear model estimated by OLS. It treats the number of breakpoints and their
locations as unknown.

Applying this procedure to the augmented IS curve with the calculated policy

index, this gives us the following IS curve equation with m breaks:

¯yt = a1 + b1 ¯yt−1 + c1 MPIt + d1vt + εt
…
¯yt = am + bm ¯yt−1 + cm MPIt + dmvt + εt

t = 1, . . . . . , T1

t = Tm+1, . . . . . , T

(8)

(9)

where the breakpoints (T1, . . . . . . Tm+1) are treated as unknown. The Bai–Perron
estimation is based upon OLS estimates of ai , bi , ci , und in .

The results of the multiple breakpoint regression, which can be seen in Table 4,
conﬁrm the presence of a structural break in Q4 1994. In Q2 1991–Q4 1994 (Period
1), the lag of the output gap is very high at 0.99 and signiﬁcant at the 1% Ebene.
This indicates that shocks to the output gap are very persistent in this period. Der
index representing the monetary policy changes of the PBOC is highly signiﬁcant in
Period 1 with a coefﬁcient of 0.24. A 1% SD in the policy index results in a 0.15%

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96 ASIAN DEVELOPMENT REVIEW

change in the output gap. Endlich, the demand shock is signiﬁcant (if only at the 10%
Ebene) in this period, but has a small magnitude with a coefﬁcient of 0.04. Examining
the SD, A 1% shock to demand in Period 1 shows it causes a 0.13% change in the
output gap.

In Q1 1995–Q3 2014 (Period 2), the lagged coefﬁcient of the output gap is
again highly signiﬁcant at 0.96. The MPI is only signiﬁcant at the 10% level with a
coefﬁcient value of 0.07. Wieder, we can evaluate the magnitude of the relationship
by examining the effect of a 1% SD on the output gap. A 1% change in the policy
index in this period leads to only a 0.05% deviation in the output gap. There is no
signiﬁcant effect of the demand shock on the output gap in Period 2.

C. Markov Switching Model

While the estimations in section V.B provide a better insight into the monetary
policy transmission process in the PRC than the standard linear model, the breakpoint
regression is limited in that it does not allow us to switch between different regimes
or states. Many economic time series occasionally exhibit dramatic breaks in their
behavior that are associated with events such as ﬁnancial crises or abrupt changes
in government policy (Hamilton 2005). The PRC, insbesondere, has experienced
tremendous structural change in recent decades associated with the gradual opening
of the economy. Prices have been liberalized, trade has increased extensively,
companies have been privatized, and the economy has been transformed from one
that was centrally planned prior to 1978 to a market economy (Brandt and Rawski
2008). The PRC has also experienced several economic shocks, some of which
were related to policy measures to liberalize the economy (Gerlach and Peng 2006).
The breaks in the time series associated with these events make linear models
inappropriate for analyzing macroeconomic variables over time. To fully capture
nonlinearity, the PRC’s monetary policy transmission channel is examined using
the MS model of Hamilton (1989, 1990, 1994). This technique has been used
extensively to examine monetary policy transmission in advanced economies such
as the United Kingdom, the US, and the eurozone. Dolado, Maria-Dolores, Und
Ruge-Murcia (2005); Peersman and Smets (2001); and Arag´on and Portugal (2009)
have all carried out similar studies for advanced economies, but the technique has
seldom been applied to the PRC or other emerging market economies. This gives
us a unique opportunity to examine any asymmetry or nonlinearity in the PRC’s
monetary policy transmission channel. The MS model is so called because the
switching mechanism is controlled by an unobserved state variable, st , that follows
a ﬁrst order Markov chain process. An interesting feature of the MS model is that
the ﬁltered probabilities can be interpreted as the agent’s belief that the economy
is in one of the possible states that describe the economy. It is also a very useful
technique as the unobserved or latent state variable can be linked (or at least possibly
linked) to an observable event, Politik, or characteristic. Another key point is that the

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 97

MS model is relatively easy to implement because it does not assume any a priori
knowledge of an arbitrary time period or event. Stattdessen, the regime classiﬁcation in
this model is probabilistic and determined by the data (Kuan 2002).

By ﬁtting the linear IS curve equation to the MS framework, we get the

following:18

¯yt = ast + bst ( ¯yt−1) + cst (MPIt ) + dvtst

(10)

where et ∼ i.i.d. N (0, σ 2
e,st ) and with unobserved state st , which is assumed to follow
a Markov chain of order 1 with transition probabilities pi j . The transition probability
pi j gives the probability that state i will be followed by state j.

Pi j = Pr[st = j | st−1 = i],

M(cid:2)

i−1

pi j = 1,

∀i, j = 1, . . . . .M

(11)

This is often then written in an (M x M) matrix P, called a transition matrix:

⎡

⎢
⎢
⎢
⎣

p11
p12
…
pM M

· · ·
. . .

p21
p22
…
· · ·
p2M · · ·

⎤

⎥
⎥
⎥
⎦

pM1
pM2
…
pM M

P =

(12)

The row i, column j element of P is the transition probability pi j . To demonstrate, In
the above matrix (12), the row 2 column 1 element gives the probability that State 1
will be followed by State 2. Zum Beispiel, at time t the state of the economy st is
classiﬁed as having either a positive output gap (st = 1) or a negative output gap
(st = 2). In our estimation, we assume that the model gives us a probability of 95%
of being p11 and 5% of being p21. What these values tell us is that if the economy is
in a state of a negative output gap in the previous period, it tends to stay in this state
at a very high probability of 95%. Andererseits, the probability of being in a
negative output gap state in the previous period and switching to a positive output
gap state is just 5%.

The estimation of

the model depends on maximum likelihood. Der
maximization of likelihood function of the model requires an iterative estimation
technique to obtain estimates of the parameters of the model and the transition
probabilities.19 With the parameters identiﬁed, it is then possible to estimate the
probability that the variable of interest is following a particular regime. It is also

18In the interest of robustness, estimations were carried out using both the MS estimation function in Eviews

8 and the MS_Regress_Fit package developed by Perlin (2012) in Matlab. The results were very similar.

19For more detail on this technique and the maximum likelihood see Hamilton (1989, 1994) and Kim and

Nelson (1999).

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98 ASIAN DEVELOPMENT REVIEW

Tisch 5. Monetary Policy Index IS Curve with
Markov Switching Model, Q2 1991–Q3 2014

Variable

Zustand 1

Zustand 2

Dependent Variable: PRC’s Output Gap, ¯yt

0.01∗∗∗
(0.01)
0.70∗∗∗
(0.04)
0.25∗∗∗
(0.06)
0.04∗∗∗
(0.01)

−0.01∗∗∗
(0.01)
0.91∗∗∗
(0.03)
0.05
(0.05)
0.01∗∗∗
(0.01)
0.96
0.04

Constant
A
Output gap lag
¯yt−1
Monetary Policy Index
MPIt
Demand shock (Exports)
vt
p11
p12
0.11
p21
0.89
p22
Duration of state
8.8 quarters
HAC = heteroscedasticity and autocorrelation-consistent, IS =
Investment–Saving, VR China = Volksrepublik China.
Notes: ∗∗∗ = 10% level of statistical signiﬁcance, ∗∗ = 5% Ebene
of statistical signiﬁcance, ∗ = 1% level of statistical signiﬁcance.
HAC standard errors are in parentheses.
Quelle: Berechnungen der Autoren.

32.9 quarters

possible to derive the smoothed state probabilities that indicate the probability of
being in a particular regime or state. Zum Beispiel, the MS model may highlight
the effectiveness of the PBOC’s monetary policy depending on whether the PRC’s
economy is operating above or below potential (positive or negative output gap).
Before estimating the MS–IS curve, the number of states or regimes to be included in
the model must be chosen. As there are often relatively few transitions among states,
it is difﬁcult to estimate strictly exogenous explanatory variables accurately. Dafür
reason, most applications assume only two or three states (Hamilton 2005). Tests
for both a two-state and three-state MS–IS curves were carried out. The three-state
speciﬁcation was rejected against the two-state speciﬁcation since the data points
were detected only in the ﬁrst and second states.

The results of the MS estimation of the IS curve can be seen in Table 5 Und

the two identiﬁed states plotted against the output gap can be seen in Figure 8.

In State 1, the autoregressive coefﬁcient of the output gap is high at 0.91.
This indicates that shocks to the output gap are quite persistent (d.h., output will
increase if output was high in the previous period). The MPI has no signiﬁcant
effect on the output gap in this state. The effect of the demand shock (change in
exports), while signiﬁcant, is very small. We can examine the relationship of the
signiﬁcant independent variables to the output gap further by examining a 1% SD
in the demand shock, which has only a 0.09% Wirkung. By examining both Figure 8
and the summary statistics in the Appendix we can see that State 1 can be mostly

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 99

Figur 8. Output Gap with Regime Classiﬁcation

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Sources: Oxford Economics. Global Economic Databank. http://www.oxfordeconomics.com/forecasts-and-models
/cities/china-cities-and-regional-forecasts/overview (abgerufen im Januar 2015); and authors’ calculations.

characterized by a period when output was operating below potential (d.h., negative
output gap).20

In State 2, the persistence of shocks to the output gap is again high but has
decreased from State 1. The MPI is correctly signed and highly signiﬁcant with a
coefﬁcient of 0.25. This indicates that changes to the various policy instruments
used by the PBOC had a signiﬁcant effect on the real economy. The demand shock
(change in exports) is also highly signiﬁcant with a coefﬁcient of 0.04. A 1% SD in
the MPI leads to a 0.33% change in the output gap. The effect of a demand shock
is much stronger in State 2 than in State 1. A 1% SD in the PRC’s exports leads
to a 0.44% change in the output gap. Figur 8 and the summary statistics in the
Appendix indicate that State 2 can be described as a period when output was mostly
operating at or above potential (d.h., neutral or positive output gap).

Summary of Results

From the results of the various estimations in this section, several important
characteristics of the monetary policy transmission channel in the PRC have been
identiﬁed. Contrary to the majority of the literature in the area, the results ﬁnd that a
single policy instrument—the lending rate set by the PBOC—can have a signiﬁcant
effect on the real economy. Jedoch, the magnitude of this relationship between the
interest rate and the output gap is small. The results also found that the traditional

20As mentioned, this seems unusual for an economy that has grown at 9% per year. The issue of the PRC’s

excess capacity is discussed in detail in IMF (2012).

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100 ASIAN DEVELOPMENT REVIEW

IS curve speciﬁcation was linear and stable over the estimation period. An IS curve
estimated using a composite index of the policy tools employed by the PBOC,
including a measure of qualitative instruments, does not improve on the standard
Modell. Jedoch, this speciﬁcation was found to contain structural breaks.

An IS curve using the breakpoint model of Bai and Perron (1998, 2003)
and an MPI consisting of both quantitative and qualitative policy instruments
was then estimated. The results suggested that there was a breakpoint in Q4
1994. This breakpoint corresponds to key institutional changes and reforms in
the PRC’s economy. These include reforms to the banking sector; reforms regarding
price liberalization; Und, crucially, the adoption of the dollar peg exchange rate
regime. These events therefore seem to have had a distinct effect on the PRC’s
monetary policy transmission channel. During the period prior to this breakpoint
(Period 1: Q2 1991–Q4 1994), the MPI exerted a signiﬁcant inﬂuence on the real
economy. In the period after the breakpoint (Period 2: Q1 1995–Q3 2014), the same
instruments played a less signiﬁcant role in changes to the level of output. Das
is an important ﬁnding as Period 2 included continued reform of the banking and
ﬁnance sector which, in theory, should have improved both the transmission channel
and mechanisms of monetary policy, as well as the inﬂuence and autonomy of the
PBOC. A possible explanation for this counterintuitive ﬁnding is the adoption of
the dollar peg.21 It is possible that the maintenance of this quasiﬁxed exchange rate
regime has served to hinder the ability of the PBOC to inﬂuence the real economy
through the monetary policy transmission channel.

The ﬁnal model estimated was an MS–IS curve. This model differs from the
breakpoint model in that it allows switching between states. It classiﬁed the PRC’s
economy into two states. The majority of the estimation period is characterized by
Zustand 1, in which the MPI had no signiﬁcant effect on the output gap. This state
is mostly characterized by periods of below potential output (d.h., negative output
gap). Zustand 2, andererseits, is characterized by mostly positive output gaps. Der
index for PBOC monetary policy changes was highly signiﬁcant in this state. Diese
results suggest that monetary policy in the PRC is much more effective when output
is stronger relative to potential (operating at or above potential) than when it is
weaker relative to potential (operating below potential). This ﬁnding has similarities
to the “pushing on a string” hypothesis, which states that when the economy is
weak (operating below its potential level), a central bank can do little to remedy
the situation.22 The high signiﬁcance of the PRC’s growth in exports in affecting
whether the economy is operating above potential is also an important ﬁnding. Für

21While the dollar peg, introduced in 1994, was ofﬁcially abandoned in 2005, Morrison (2012) argues that
the PRC’s exchange rate mechanism remains, in practice, a tightly managed currency peg against the US dollar. In
Juli 2015, the Financial Times reported that despite reform progress since 2005, intervention remains a daily reality
(Wildau 2015).

22This metaphor, attributed to John Maynard Keynes, maintains that using monetary policy to ﬁght a severe

recession is like pushing on a piece of string (Blyth 2012).

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 101

many years, the PRC’s high levels of investment created capacity beyond its ability
to consume. This excess capacity was often absorbed outside its borders given the
exceptionally strong global demand for the PRC’s exports (IMF 2014). daher,
our results would seem to emphasize the reliance of the PRC on exports in closing
the gap between potential and actual output.

VI. Robustness Tests

In diesem Abschnitt, we undertake robustness tests to add reliability and credence

to the ﬁndings of both our breakpoint model and our MS model.

Breakpoint Model

The results of our breakpoint model found that prior to 1994, the index
calculated to model changes in the PBOC’s monetary policy exerted a signiﬁcant
inﬂuence on the real economy. Jedoch, in the period after the breakpoint date
(Q1 1995–Q4 2014), the same instruments played less of a signiﬁcant role in
changes to the level of output. This result is surprising as the post-1994 period
was characterized by historic monetary reforms toward a more modernized and
ﬁnancially liberated monetary system. We suggested that the PRC’s controversial
exchange rate regime may have served to hinder the ability of the PBOC to inﬂuence
the real economy through the monetary policy transmission channel. This theory can
be tested empirically by estimating a simple monetary policy reaction function.23
Along with the standard monetary policy rule variables of the inﬂation and output
gap, we include the nominal effective exchange rate as a target variable. Der
monetary policy reaction function, daher, takes the following form:

MPIt = a − b(πt−1 − π ∗

T ) − c( ¯yt−1) − d(cid:4)neert + εt

(13)

As before, MPIt is our calculated monetary policy index, πt−1 − π ∗
is the inﬂation
T
gap (inﬂation rate minus the inﬂation target), ¯yt is the output gap, Und (cid:4)neert is
the change in the nominal effective exchange rate.24 We then estimate this reaction

23As in our IS curve estimations, the interest rate is replaced with our MPI.
24All variables in Equation 13 were found to be I(0) and available by request. For the inﬂation gap variable
(πt−1 − π ∗
T ), the Consumer Price Index inﬂation rate is available from the PRC’s National Bureau of Statistics. Ein
ofﬁcial target is also available from various publications at http://english.gov.cn/archive/. For the exchange rate target,
changes in the nominal effect exchange rate are used. The nominal effective exchange rate is deﬁned in foreign
currency unit per renminbi (d.h., an increase in this variable corresponds to an appreciation of the renminbi). Diese
data are available in the IMF’s International Financial Statistics. As mentioned in section IV, the setup of the MPI
implies that an increase corresponds to an expansionary monetary policy action. This changed the sign of the monetary
policy response coefﬁcient from minus to plus in our IS curve equation. Ähnlich, an increase in inﬂation or output
over its target, Potenzial, or natural level will lead to a contractionary monetary policy reaction. daher, we would
expect to see negative signs for both the inﬂation and output gap coefﬁcients.

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102 ASIAN DEVELOPMENT REVIEW

Tisch 6. Two-Period OLS Monetary Policy Reaction
Function

Variable

Period 1
(Q2 1991–Q4 1994)

Period 2
(Q1 1995–Q3 2014)

Dependent Variable: Monetary Policy Index, MPIt

−0.03
(0.01)
0.29
(0.19)
−0.67∗
(0.35)
−0.01
(0.06)
0.28
2.10

−0.01
(0.01)
0.04
(0.06)
−0.10
(0.08)
−0.18∗∗∗
(0.07)
0.14
1.70

Constant
A
Inﬂation gap
πt−1 − π ∗
T
Output gap
¯yt−1
Exchange rate
(cid:4)neert
R2
DW-Stat
HAC = heteroscedasticity and autocorrelation-consistent, OLS =
ordinary least squares.
Notes: ∗∗∗ = 10% level of statistical signiﬁcance, ∗∗ = 5% level of
statistical signiﬁcance, ∗ = 1% level of statistical signiﬁcance. HAC
standard errors are in parentheses.
Quelle: Berechnungen der Autoren.

function before and after the breakpoint in 1994. The results, which are presented
in Table 6, are interesting.

First of all, the inﬂation gap is incorrectly signed and insigniﬁcant across
both time periods, suggesting that it does not appear to be an important factor in
the monetary policy response of the PBOC across the entire estimation period. Das
is in line with Mehrotra and S´anchez-Fung (2010), who ﬁnd the same result over a
similar period (1994–2008). The authors of this paper argue that as the inﬂation gap
was mostly negative in their estimation period, with the exception of brief periods
In 1994/1995 Und 2008, inﬂationary pressures may not have been a major concern
for the PBOC. What is perhaps most interesting is that the output gap is correctly
signed and signiﬁcant in Period 1, if only at the 10% Ebene, while the exchange rate is
not signiﬁcant. In Period 2, Jedoch, the coefﬁcient of the output gap has decreased
substantially and is no longer signiﬁcant, while changes in the exchange rate have
now become highly signiﬁcant. This switch from a policy index that was responsive
to deviations in output from its natural level to deviations in the level of the renminbi
exchange rate seems to verify the results and interpretation of our breakpoint model.
Mit anderen Worten, the PBOC’s exchange rate policy of maintaining the renminbi at a
desired level may have hindered the appropriate response to deviations in the level
of output.

B. Markov Switching Model

We can also test the results of our MS model. The main ﬁnding of our model
was that the PRC’s monetary policy is more effective when output is operating above

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 103

Tisch 7. Monetary Policy Transmission with Positive and
Negative Output Gaps, Q2 1991–Q3 2014

Negative Output Positive Output

Gap (14)

Gap (15)

Dependent Variable: PRC’s Output Gap

Variable

Constant

Output gap lag

Monetary Policy Index

Demand shock (Exports)

−0.01
(0.01)
0.98∗∗∗
(0.05)
0.13∗∗∗
(0.05)
0.01
(0.01)

0.01
(0.01)
0.88∗∗∗
(0.03)
0.11∗∗
(0.06)
0.01∗∗
(0.01)

HAC = heteroscedasticity and autocorrelation-consistent, PRC = People’s
Republic of China.
Notes: ∗∗∗ = 10% level of statistical signiﬁcance, ∗∗ = 5% level of statistical
signiﬁcance, ∗ = 1% level of statistical signiﬁcance. HAC standard errors
are in parentheses.
Quelle: Berechnungen der Autoren.

potential and less effective when operating below potential. The MS estimations
detected two states, which we labeled State 1 and State 2. While these states were
roughly deﬁned as mostly operating below (Zustand 1) or above (Zustand 2) Potenzial,
it is obvious from Figure 8 that not all positive and negative periods correspond
exactly to the states detected by the model. daher, we can undertake a much
more simple, if not arbitrary, examination of this dynamic. This is done by running
two separate linear regressions:

¯ytNEGATIVE = a + B( ¯yt−1) + C(MPIt ) + dvt + εt
¯ytPOSITIVE = a + B( ¯yt−1) + C(MPIt ) + dvt + εt

(14)

(15)

These correspond exactly to periods of positive and negative output gaps.25 While
this technique lacks many of the advantages of our MS estimation (section V.C),
it is nonetheless a useful robustness check of the validity of our ﬁndings and
interpretations. The results are presented in Table 7 below. The coefﬁcient of
the output gap does not differ greatly across the positive and negative output gap
periods, with coefﬁcients of 0.11 Und 0.13, jeweils. Tatsächlich, contrary to our MS
estimation, the reaction appears to be stronger, if only marginally, in the negative
output gap period. Jedoch, as mentioned in section V.A, it is important to examine
the relationship in terms of the SD of variables. Using the summary statistics in
the Appendix, we ﬁnd that a 1% SD in the MPI results in a 0.12% SD in ¯yt
during the negative output gap period. The same deviation in the policy index

25The negative output gap periods are Q1 1991–Q2 1993, Q2 1997, Q4 1997–Q1 2007, Q2 2009, and Q1
2012–Q3 2014. The positive output gap periods are Q3 1993–Q1 1997, Q3 1997, Q2 2007–Q1 2009, and Q3 2009–Q4
2011.

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104 ASIAN DEVELOPMENT REVIEW

results in a 0.23% deviation in ¯yt during the positive or neutral output gap period.
This indicates a stronger reaction of the output gap to changes in policy variables
during periods of a positive output gap, which is concurrent with the results of our
MS estimations. The coefﬁcients of the demand shock are also not signiﬁcantly
different over the two periods, with a coefﬁcient of 0.012 Und 0.010 for the positive
and negative output gap periods, jeweils. Jedoch, A 1% SD in the demand
shocks leads to a 0.05% change in ¯yt in the negative gap period and a 0.24% ändern
in the positive gap period. This conﬁrms the relative importance of exports in the
closing of the PRC’s output gap. This also concurs with our ﬁndings in section V.C.
From the results of these simple robustness tests, it appears that the results

and the ﬁndings from our Bai–Perron and MS estimations are fairly robust.

VII. Abschluss

In examining the link between monetary policy and the real economy in
the PRC using different variations of the IS equation, this paper has made several
interesting ﬁndings. The results of the traditional OLS model indicate that there
is a signiﬁcant and stable link between the lending interest rate set by the PBOC
and aggregate demand in the PRC’s economy. This is at odds with the majority
of studies on the topic that suggest it is difﬁcult to estimate a stable and robust
aggregate demand equation for the PRC. Es ist wichtig zu beachten, Jedoch, that the
size of the effect is small. This can be attributed to the underdevelopment of the
banking system, market segmentation, the ineffectiveness of the credit channel, Und
the fact that the PBOC relies heavily on many different tools in the conduct of
monetary policy. Given these ﬁndings, we then estimated an IS curve with an MPI
comprising the tools used by the PBOC between 1991 Und 2014. This index is a
composite measure of the relevant variables observed to be at the disposal of the
PBOC, both quantitative and qualitative, and therefore was expected to give a much
better representation of the monetary policy stance of the PRC’s central bank. Der
presence of structural breaks indicates that there is asymmetry between monetary
policy action and output depending on the state of the economy and the time period.
A monetary policy index IS curve using a breakpoint model was estimated.
The results conﬁrmed the presence of a break in late 1994. The results of this model
suggest that the monetary policy instruments of the PBOC have had less of an effect
on the level of output since this breakpoint. While this seems counterintuitive, fällig
to increased reforms in the ﬁnance sector, as well as measures that have promoted
greater PBOC independence, the result is attributed to the adoption of the US dollar
peg exchange rate regime in 1994.

Endlich, an MS–IS curve using our MPI was estimated. This technique
provided us with a different perspective on the monetary policy transmission channel
as it allowed for switching between different regimes or states. This nonlinear

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EXAMINING MONETARY POLICY TRANSMISSION IN THE PRC 105

technique allows us to examine asymmetry in the monetary policy transmission
channel. Testing for this type of asymmetry is important due to the underdeveloped
nature of the PRC’s ﬁnancial system and the huge amount of reform and structural
change that the economy has experienced. Our results suggest that there is a
signiﬁcant link between the monetary policy tools used by the PBOC and the
real economy in State 2 of our model, when output is mostly operating at or above
its potential level. This relationship breaks down when the economy switches to
periods that are characterized by output that is mostly below potential. Endlich, unser
MS model seems to suggest that demand shocks have a much greater effect in State
2 (mostly positive output gap) than in State 1 (mostly negative output gap). Das
points to the importance of exports in closing the gap between potential and actual
output in the PRC.

The results of the paper seem to suggest that the PRC’s exchange rate policy
has restricted the effectiveness of the PBOC’s monetary policy response. Während
there have been some signiﬁcant developments in recent years, further liberalization
of the exchange rate regime would facilitate greater monetary policy independence
and effectiveness. If monetary policy is less effective, or even ineffective, Wann
output is operating below potential, as our results suggest, then the PBOC will
need to resort to alternative monetary policy tools to continue to achieve its goal of
maintaining economic growth. This could be aided by further reform of the ﬁnance
and banking sectors to help reduce output volatility and allow for greater symmetry
in the transmission of monetary policy.

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110 ASIAN DEVELOPMENT REVIEW

APPENDIX: Summary Statistics

OLS
Estimations

Breakpoint Model

Markov
Switching
Modell

Robustness Test

Period 1
Period 2
(Q2 1991– (Q1 1995–
Q4 1994) Q3 2014) Zustand 1 Zustand 2 Gap
−1.5%
3.4%

−0.8% −1.4% 0.7%
2.6% 1.5%

1.6%

Gap
1.1% −2.1%
1.5%
0.8%

Positive Negative
Output Output

—

0.5%

2.1%

17.4%

10.9%

—

−0.3% −0.3% 0.4% −0.4% −0.1%

1.3%

1.1% 2.0%

1.5%

1.4%

11.6%

13.6% 9.4% 10.3% 13.8%

12.2%

10.4% 16.6% 15.2%

9.8%

Q2 1991–
Q3 2014
−1.0%
2.1%

2.8%

4.3%

−0.2%

1.5%

12.5%

12.1%

Mean of output gap
Standard deviation of

output gap

Mean of real interest

rate

Standard deviation of
real interest rate
Mean of Monetary
Policy Index
Standard deviation
Monetary Policy
Index

Mean of demand
shock (exports)
Standard deviation of
demand shock
(exports)

OLS = ordinary least squares.
Quelle: Berechnungen der Autoren.

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