Entrepreneurial Impulse, Investment Behavior,
and Economic Fluctuations–A VAR
Analysis with Indian Data
∗
PANCHANAN DAS
This study analyzes the observed behavior of growth cycles and the dynamics of
economic fluctuations in terms of entrepreneurial impulse with Indian macroe-
conomic time series data for more than 60 年. The fluctuations of investment
are explained in terms of persistence, volatility, and comovements of the cyclical
成分. The study observes that the growth cycles of private investment
were much more volatile than the growth cycles of public investment and gross
domestic product. Investments in India were procyclical, but their growth cy-
cles became acyclical. This study observes that the fluctuations of growth cycle
frequencies of private investment appeared even when there were no shocks
to gross domestic product. Such investment behavior may be because of the
self-fulfilling beliefs of investors. Private investment in India has not been badly
affected by the severe balance of payments crises. 相当, a cyclical downturn
was seen as an opportunity to invest by the large business houses.
关键词: animal spirits, growth cycles, Indian economy, time series decompo-
位置
JEL codes: C32, E32, N10
我. 介绍
This study analyzes the observed behavior of investment and output dynamics
in explaining growth cycles in India. The existence of highly volatile and persistent
cycles affecting overall economic activity is an inherent feature of a market-based
modern economy. A large number of competing theoretical models provide the es-
sential causes for economic fluctuations, but there has been a discrepancy between
them in explaining what actually happens in the real world. The empirical literature
on business cycles has indeed shown that, at the aggregate level, investment is con-
siderably more volatile than output, and fluctuations of both output and investment
are highly synchronized. 此外, at the micro level, firms’ investment behavior
appears to be lumpy and strongly affected by a firm’s financial structure. Whether
∗Panchanan Das: Associate Professor of Economics, 经济系, University of Calcutta, 印度. 电子邮件:
daspanchanan@ymail.com. The author would like to thank the anonymous referees and the Managing Editor for their
comments and suggestions, and to Amiya Kumar Bagchi for valuable comments on earlier versions of this paper. 这
usual disclaimer applies.
Asian Development Review, 卷. 32, 不. 2, PP. 1–17
C(西德:3) 2015 Asian Development Bank
and Asian Development Bank Institute
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2 ASIAN DEVELOPMENT REVIEW
the growth cycles follow optimal responses by rational agents to erratic changes in
技术, or the cycles are influenced by the “animal spirits” of investors is an
important issue in the context of recent financial crises and economic slowdowns
that have appeared in both the developed and the developing world. If economic
fluctuations are really guided by the outcome of Arrow–Debreu types of general
equilibrium models, then allocations will be Pareto optimal and there will be no
cause of concern, at least from regulators’ point of view. But if growth cycles are
independent of market fundamentals, then there may be an important role for policy
makers in designing regimes that can reduce fluctuations and increase economic
福利.
John Maynard Keynes’ animal spirits explanations were used globally to look
into the behavior of economic fluctuations until the late 1970s. 随后, 它是
thought that the animal spirits hypothesis, like sunspots, appeared as a theoretical
curiosity that did not have much to add to modern theories of the business cycle based
on the rational expectations hypotheses. 然而, some scholars, 特别是
post-Keynesians, believe that crises, in the form of a severe cyclical downturn in the
advanced capitalist world such as the recent global financial crisis, occur because
the expectations of economic agents, particularly the entrepreneur, are guided not by
rationality alone. Farmer and Guo (1994) observed that fluctuations in business cycle
frequencies appeared in the United States’ (我们) economy not by the shocks to the
fundamentals of the economy, but by the self-fulfilling beliefs of investors. Investors
become overly optimistic as the economy grows, but disappointed when profits
fall short of their inflated forecasts. 实际上, because of this kind of entrepreneurial
行为, every expansion sows the seeds of recession. In the process of fluctuations,
while the upswing is slow and steady, the collapse is sudden and steep (哈维
2010).
在此背景下, the objective of this paper is to analyze the dynamics
of economic fluctuations in terms of entrepreneurial impulse with Indian macroe-
conomic time series data for more than 60 年. The theoretical background of the
study relies primarily on Chapters 12 和 22 of Keynes’ The General Theory of Em-
ployment, Interest, and Money. During the Great Depression, Keynes attributed the
business cycle to alternating waves of optimism and pessimism, which he termed
animal spirits.1 Keynes (1921), in A Treatise on Probability, argued that, 作为
rational quantitative calculation alone cannot justify action under uncertainty, 在-
vestment becomes inadequate and the economy settles into collapse without animal
spirits. The fluctuations in aggregate economic activity might be driven at least
in part by the waves of optimism or pessimism, and not by “the outcome of a
weighted average of quantitative benefits multiplied by quantitative probabilities.”2
According to Keynes (1936), the formation of entrepreneurial expectations on
1See Barens (2011) for detailed sources of Keynes’ use of the term “animal spirits.”
2Keynes, J. 中号. 1921. A Treatise on Probability. 伦敦: Macmillan.
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ENTREPRENEURSHIP, INVESTMENT BEHAVIOR, AND ECONOMIC FLUCTUATIONS IN INDIA 3
investment in an uncertain environment depends largely on conventional judgments
and animal spirits. He argued that, given fundamental uncertainty, rationality alone
is insufficient to justify action. Animal spirits are neither rational nor irrational
in indicating the psychological urge to action which explained decisions being
taken in spite of uncertainty (Dow and Dow 2011). When there is no basis for
rational belief, investment behavior is dictated by psychological motivations and
nonrational forces. Reasons and evidence can provide only a partial justification for
决定.
The questions of how economic fluctuations and investment decisions are
interrelated and how the animal spirits hypothesis is relevant in analyzing growth
cycles have been extensively debated in the literature. Samuelson’s (1939) multiplier-
accelerator model and Harrod’s (1939) business cycle model were accounted as
contributions of the early Keynesian literature to this debate. Kalecki (1937) 和
Goodwin (1951) studied the endogenous formation of business cycles by bringing in
the heterogeneity and distributional aspects. While the underlying theoretical model
implied by Keynes has proven to be rather complex, many studies have attempted
to assess the Keynesian belief in relation to the psychological factors (Azariadis
1981; Crotty 1992; Howitt and McAfee 1992; Lawson 1981, 1985; 罗宾逊 1979;
Shleifer 1986). Dequech (1999) examined how animal spirits influence both ex-
pectations and confidence by demonstrating that animal spirits are interrelated with
认识. In a dynamic setting, Kiyotaki (1988) and Weil (1989) explored the role
of animal spirits in the formation of business cycles in the presence of investment ex-
ternalities. The inherent interaction of financial markets and investment has caused
a wave of new research (看, 例如, Fazzari 1988, Fazzari and Petersen 1993).
In such studies it is observed that limited access to finance, higher transactions costs,
and asymmetric information adversely affect investment. The underlying close as-
sociation of the fluctuations of internal finance, profits, and business cycles suggest
that the cost of external credit increases during recessions. 再次, 在很多情况下,
the investment behavior has been affected more by fiscal policy parameters than by
the cost of capital (Fazzari 1993).
Most of the research in this area, 然而, is theoretical or quantitative
in nature and based mainly on the developed industrial world. The relevance of
animal spirits in understanding investment behavior and economic fluctuations in
a developing economy like India has not been examined empirically as such by
学者. There has also been some research on business cycles with Indian data.
例如, Hatekar (1994) studied the historical paths and comovements of annual
time series data in the Indian economy for the period 1951–1985 after detrending
the series. The Reserve Bank of India (2002), via the Working Group on Economic
指标, examined business cycles in India with quarterly time series data of
nonagricultural gross domestic product (GDP). The empirical analysis in our study
is based on growth cycles of GDP, public investment, and private investment over
the period 1950–2013.
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4 ASIAN DEVELOPMENT REVIEW
This study sets out to provide empirical estimations of growth cycles of
output and investments in India to look into the investment behavior of the economy
by decomposing the observed time series of real GDP, total investment, and private
investment into trend and cyclical components. Private investment in India has not
been badly affected by the severe balance of payments crises. 相当, a cyclical
downturn was seen as an opportunity to invest by the large business houses. 这
type of entrepreneurial decision can hopefully be analyzed with rising animal spirits.
Keynes’ fundamental insight that animal spirits play an important role in affecting
in analyzing fluctuations in economic
investment decisions may be relevant
activities and investment behavior in India not only during the recent global financial
crisis, but also during different phases of state control. 在本文中, we examine
the interrelations between waves of optimism and pessimism, and the subsequent
economic fluctuations around the turning points.
The empirical work of this paper is based on Keynes’ theory of trade cycles,
where instability in investment has been the major cause of economic fluctuations.
Keynesian theories of business cycles are inherently endogenous because animal
spirits originate investment instability, which in turn causes output fluctuations.
Following Keynes (1936) on trade cycles, we assume that inescapable market
uncertainty and individual expectations play a key role in shaping investment
dynamics and triggering fluctuations in overall economic activity. In this framework,
代理人, both firms and workers, are heterogeneous, rationally bounded, and endowed
with adaptive expectations. As in the case of Dosi, Marengo, and Fagiolo (2004),
agents interact
in an endogenously changing environment characterized by
substantial uncertainty. The microeconomic dynamics of production and investment
induce macroeconomic dynamics for aggregate investment and output.
We have examined the stochastic behavior of growth cycles of the
macroeconomic time series of output, private investment, and total investment in
India from 1950 直到 2013. The cyclical variation in investment is examined in terms
of persistence, volatility, and comovements of the cycles of GDP. The fluctuations
of GDP in real terms obviously indicate the overall economic fluctuations that may
affect the behavior of aggregate investment. In our analysis, we have concentrated
mainly on the reverse causality—that is, how growth cycles of investment are
causally related to the growth cycles of output. According to the real business cycle
假设, the entrepreneur’s impulse in taking investment decisions is affected
by economic fluctuations. If the real business cycle hypothesis is actually effective
in an economy, there is no role for animal spirits in analyzing investment behavior.
然而, self-fulfilling expectations may also be the driver of business cycles in
the presence of expectational indeterminacy in investment in a real business cycle
模型. This study concentrates on the real sector of the economy to carry out
an empirical exercise in investigating the growth cycles as observed in India in a
partial equilibrium framework. We hypothesize that entrepreneurial activity toward
investments is affected largely by some nonrational factors in given political, 社会的,
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ENTREPRENEURSHIP, INVESTMENT BEHAVIOR, AND ECONOMIC FLUCTUATIONS IN INDIA 5
and economic situations. Entrepreneurs do not have access to a full information set
and they are not capable of describing fully the statistical distribution of economic
shocks, but they try to forecast by following adaptive types of expectations.
This paper observes that the fluctuations of growth cycle frequencies of
private investment appeared even when there were no shocks to GDP, the major
macroeconomic fundamental. This type of investment behavior may be because of
the self-fulfilling beliefs of investors. The insignificant relationship between growth
cycles of output and investment may imply the role of animal spirits in analyzing
investment behavior of an economy, at least in a statistical sense. The paper is
organized in the following manner. Section II describes the data. Section III analyzes
the stochastic behavior of trend and growth cycles of GDP, public investment, 和
private investment in India. Volatility, persistence, and comovements of the growth
cycles are investigated in Section IV. Section V deals with the dynamic relationship
between the cycles of investment and GDP. Section VI concludes.
二. 数据
The Central Statistical Office (公民社会组织) of the Ministry of Statistics and Pro-
gramme Implementation and the Reserve Bank of India are the sources of the data
used in this study. The estimates of India’s national income have been revised by the
National Accounts Division of the CSO in preparing National Accounts Statistics
(NAS) from time to time. Base years have been revised periodically by the CSO
in the past, starting from 1948–1949. The 2004–2005 series widens the database
for different sectors by the inclusion of several items not previously covered. GDP
at constant 2004–2005 prices, the most important macroeconomic aggregate of na-
tional accounts, is used as an output variable. Real gross capital formation is taken
as a proxy for investment. In NAS, gross capital formation has two components:
gross fixed capital formation (GFCF) and change in stocks. GFCF is the gross value
of goods that is added to the fixed domestic capital stock in a year. The change in
stock is the difference between market values of the stocks at the beginning and end
of the period. We have treated GFCF as total investment. NAS also provides GFCF
by the household sector, the private corporate sector, and the public sector. 我们有
used data for GFCF by the private corporate sector as private investment. All data
used in this study are reproduced in the Handbook of Statistics on Indian Economy,
2013, published annually by the Reserve Bank of India.
三、. Trend and Cycles of Outputs and Investments
Identification of trends and cycles of a macroeconomic variable is often
an important empirical issue in macroeconomic analysis, particularly in analyzing
growth behavior in an economy. Different methodologies have been suggested in
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6 ASIAN DEVELOPMENT REVIEW
the empirical literature in identifying business cycles or growth cycles. While the
National Bureau of Economic Research methodology is simple to understand, 我们
do not follow it in this study because the trend–cycle decomposition is needed to
identify the importance of economic growth and productivity.3 Moreover, empirical
investigation of the behavior of fluctuations of the macroeconomic time series around
its trend assumes significance in order to identify the possible sources of economic
不稳定. We define the fluctuations of a variable around its trend as the growth
cycle and it differs in stochastic character from the conventional business cycle
as defined in the National Bureau of Economic Research literature. 在多数情况下,
然而, growth cycles and business cycles are not distinguishable (Canova 1999).
It is very unlikely that any given type of linear deterministic trend would
persist over long stretches of time. 为此原因, the trend stationary approach
to view growth and fluctuations as a sum of deterministic trends and stochastic
cycles may not be a proper methodology. In the difference stationary process, 趋势
are stochastic because of interactions with shorter fluctuations as well as structural
breaks. They have no tendency to return to linear trends (Nelson and Plosser 1982).
The components of stochastic trends are purely unpredictable and there is little
to be done about the unpredictable shocks, their hypothetical long-term effects,
and the stochastic variations in economic activity with this approach (Diebold and
Rudebusch 1999).
The trending behavior has been examined by carrying out Augmented
Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests. Perron (1989) 阿尔-
gued that the evidence in favor of unit roots has been overstated, as standard tests
have low power against trend stationary alternatives with structural breaks in trend
level or growth rate. He resolved this problem by modifying the ADF test with
dummy variables to account for a single structural break. Zivot and Andrews (1992)
extend this methodology to an endogenous estimation of the break date. In this study,
a unit root test is performed after incorporating the major break, 如果有的话, in the series.
Tables 1 和 2 display the break points estimated on the basis of the likelihood ratio
test and estimated statistics for testing unit root, 分别.
A visual inspection of the data, 如图 1, shows that GDP, 民众
投资, and private investment in India appear to have experienced growth
with some short-run fluctuations as normally observed in any economy, 但是
growth of private investment contained severe fluctuations. The time series of GDP
3In the classical cycle approach, followed particularly in the studies of the National Bureau of Economic
研究, the expansions and contractions in the level series could be analyzed without considering the trend
adjustment process. 但, the empirical studies conducted recently on business cycles provide serious attention to
trend adjustment. The trend adjustments, 然而, reduce the variations of cyclical behavior both across series
and within series over time (Burns and Mitchell 1946). 有效, the time series decomposition of macroeconomic
variables associated with growth cycles is difficult because trends and cycles interact with and influence each other
(Baxter and King 1999). A step function linking the average levels of a variable in successive business cycles was
effectively the trend representation complementing the cyclical measures. This approach formalized the concept and
estimation of the phase average trend for analyzing fluctuations in detrended variables.
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ENTREPRENEURSHIP, INVESTMENT BEHAVIOR, AND ECONOMIC FLUCTUATIONS IN INDIA 7
桌子 1. Test Statistics for Structural Break
Series
Break Point
QLR Supremum
统计数据
12.588
12.535
11.545
11.030
GDP
Growth cycle of GDP from HP filter
Growth cycle of GDP from BK filter
Private investment
Growth cycle of private investment from HP filter
Growth cycle of private investment from BP filter
Public investment
Growth cycle of public investment from HP filter
Growth cycle of public investment from BP filter
BK = Baxter–King, BP = band-pass, GDP = gross domestic product, HP = Hodrick–Prescott,
QLR = Quandt Likelihood Ratio.
来源: Author’s estimation with data from Reserve Bank of India. 2014. Handbook of Statistics on
Indian Economy. Mumbai.
2003
1964
1964
2003
No break
1959
No break
1961
1958
13.159
11.628
31.154
桌子 2. Estimated Statistics for Testing Unit Root
Series
GDP
(西德:2) GDP
Private investment
(西德:2) Private investment
Public investment
(西德:2) Public investment
ADF Statistics
3.432
−5.056
−0.519
−7.685
1.054
−5.752
Growth cycles from HP filter
GDP
Private investment
Public investment
−7.174
−8.441
−6.51
Growth cycles from BK filter
PP Statistics
4.215
−7.463
−0.296
−8.95
1.291
−8.865
−7.634
−7.722
−8.394
−6.852
−6.818
−6.447
GDP
Private investment
Public investment
BK = Baxter–King, GDP = gross domestic product, HP =
Hodrick–Prescott.
来源: Author’s estimation with data from Reserve Bank of India.
2014. Handbook of Statistics on Indian Economy. Mumbai.
−7.84
−12.796
−7.75
and investments, both public and private, are integrated of order 1, implying the
presence of a stochastic trend (桌子 2). A major significant break appeared in the
trends of both GDP and private investment in 2003, but there was no significant
break in public investment during the period 1950–2013 (桌子 1). The apparently
observed smoothness in the time series typically hides severe cyclical turbulences
affecting economic aggregates. If we isolate the cyclical components, output and
investments exhibit a completely different pattern.
We decompose the time series of GDP, public investment, and private in-
vestment into a cyclical and a trend element by using Hodrick–Prescott (HP) filters
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8 ASIAN DEVELOPMENT REVIEW
数字 1. Trends in GDP and Investments in India
GDP = gross domestic product.
笔记: All variables are in logarithmic form.
来源: Author’s estimation with data from Reserve Bank of India. 2014. Handbook of Statistics on Indian Economy.
Mumbai.
(Hodrick and Prescott 1997) and the Baxter–King (BK) version of a band-pass filter
(Baxter and King 1999).
A seasonally adjusted time series can be viewed as the sum of a trend com-
波南特 (yg
t ) and a cyclical component (yc
t ):
yt = yg
t
+ yc
t
(1)
The deviation from trend of a time series is commonly referred to as the
t ) from a time
growth cycle component. The HP filter removes a smooth trend (yg
series yt by solving the following minimization problem:
min
时间(西德:2)
t−1
{(yt − yg
t )2 + λ((yg
t
− yg
t−1) - (yg
t−1
− yg
t−2))2}, with respect to yg
t
The first component is the squared cyclical part and the second is the squared
second difference of the trend component. The sum of the second part is exactly
zero for a linear trend, but differs from zero if the slope of the flexible trend is not
持续的. The weight λ is used to adjust the relative importance of these two criteria.
The larger is λ, the more tightly the HP trend will be constrained to be linear. 这
smaller the value of λ, the more fluctuations will be admitted into the trend. If λ =
0, then we would minimize only the first summation and would do so by setting
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ENTREPRENEURSHIP, INVESTMENT BEHAVIOR, AND ECONOMIC FLUCTUATIONS IN INDIA 9
yt = yg
t for all t. The trend series is just the series itself and there is no linear trend
in the traditional sense at all. The other extreme, λ → ∞, implies that the linearity
constraint becomes perfectly binding. 在这种情况下, the HP trend is identical to the
linear trend estimated with a standard regression. For intermediate values of λ, 我们
get a trend series that is, in smoothness, somewhere between the perfectly smooth
linear trend and the perfectly unsmooth series itself. As annual data are used in this
学习, we have chosen the value of λ = 100.4
The HP filter removes unit root components from the data. 更远, the filter is
symmetric, so there is no phase shift. The cyclical component of the HP filter places
zero weight on the zero frequency (King and Rebelo 1993). The HP filter allows only
the components of stochastic cycles at or above a specified frequency to pass through,
and removes the components corresponding to the lower-frequency stochastic cycles.
The BK version of the band-pass filter is also a symmetric approximation, 和
no phase shifts in the resulting filtered series. It allows the components in the
specified range of frequencies (6 到 32 quarters) to pass through and eliminates
all the other components. The BK filter provides stationary cycles by carrying out
moving averages based on 3 years of past data and 3 years of future data as well as
the current observation if the underlying time series is integrated of order 1 或者 2:5
y∗
t
=
K(西德:2)
k=−K
ak yt−k, 和
K(西德:2)
k=−K
ak = 0
(2)
The Indian economy frequently exhibited significant cyclical variations of
distinct pattern and origin comprising a boom and a recession. 数字 2 presents
the HP-filtered and BK-filtered cycles of real GDP, public investment, and private
投资. There is a very close correspondence between the growth cycles isolated
by the HP filter and those generated by the BK filter. In this study, the HP filter is
a reasonable approximation of the BK filter. There was a significant break in the
growth cycles of GDP (obtained from both the HP and BK filters) 在 1964. The HP
cycle of public investment exhibited a major break in 1961, while the major break
in the BK cycle appeared in 1958. In the case of private investment, the BK cycle
experienced a break in 1959 and there was no significant break in the HP cycle. 这
estimated statistics as shown in Table 2 suggest that the growth cycles of GDP and
投资, both public and private, and obtained either from the HP or BK filter,
are integrated of order 0 as expected.
Volatility in GDP was higher during the period between the mid-1960s and
20世纪70年代末. The rate of volatility, 然而, declined beginning in the early 1980s.
4Hodrick and Prescott recommend choosing λ = 1,600 for quarterly series and argue that the value of λ
should vary with the square of the frequency of the series. This implies a λ of 100 for annual data and 14,400 为了
monthly data.
5See Baxter and King (1999) for detail.
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10 ASIAN DEVELOPMENT REVIEW
数字 2. Growth Cycles of GDP and Investment
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BK = Baxter–King, GDP = gross domestic product, HP = Hodrick–Prescott.
来源: Author’s estimation with data from Reserve Bank of India. 2014. Handbook of Statistics on Indian Economy.
Mumbai.
The growth cycles identified during the 1990s were less erratic over time (数字 2).
The growth cycles of both public investment and private investment were more
volatile than those of output, but private investment was even more volatile than
public investment. The volatility of investment cycles was even more explosive
during the 1950s, and these cycles were highly erratic after the 1990s as well.
Declines in public investment in India were severely deep in 1962, 1981,
1997, 和 2008. Cyclical phases of investment and output were grossly mismatched,
violating the classical observations on business cycles. Private investment in India
was more erratic than public investment. The cyclical variation of private investment
was more frequent than those of output and public investment. There was no syn-
chronization between the growth cycles of private investment and output. 我们有
shown below that the path of cycles of private investment did not follow the cyclical
path of output at all. This reflects a gross violation of the stylized facts of advanced
industrial economies supported mostly by the real business cycle hypothesis.
The growth cycles of investments and output as described above are highly
mismatched and exhibit very different patterns in every cyclical phase. 我们有
shown below that the cyclical movement of investments did not follow the cycles
of output. The share of public investment was significantly dominating before the
1990s. 而且, during the regime of state control prior to the 1990s the government
owned roughly half of the economy’s productive capacity. Indian recessions in that
ENTREPRENEURSHIP, INVESTMENT BEHAVIOR, AND ECONOMIC FLUCTUATIONS IN INDIA 11
phase were mainly triggered by bad monsoons, along with social and political
factors that cannot be predicted fully by market fundamentals. Since the early
1990s, 然而, the free market has come to dominate the economy. 在这
phase, particularly after 2003–2004, the share of private investment was more than
public investment, largely because of the absolute fall in public investment after
the initiation of economic reforms, and the cycles appeared in this phase can be
explained mostly, at least theoretically, by market fundamentals.
We perform ADF and PP unit root tests for the observed series of public
投资, private investment, and GDP, and their growth cycles obtained by de-
trending with HP and BK filters. The choice of lag length is crucial in performing unit
root test to determine the order of integration; the number of lags used in the ADF
regressions has been selected by the Akaike Information Criterion.6 The estimated
test statistics are shown in Table 2. As for most of the macroeconomic time series,
the output and investments contain unit root, implying the presence of a stochastic
trend that is completely unobservable (这 2007).7 The ADF and PP statistics as
如表所示 2 suggest that all the original series are integrated of order 1. 这
presence of unit roots in the original data series has serious macroeconomic policy
implications. Any external shock from economic reforms, 例如, could have
a permanent effect (either positive or negative) on real output and investment. 在
另一方面, the growth cycles are stationary. The hypothesis that the presence of
unit roots is rejected for the growth cycles both in terms of ADF and PP statistics.
IV. Volatility and Persistence of Growth Cycles
This section focuses on the variance and covariance properties of growth
cycles. In this study, the standard deviation measures the extent of volatility of
the different growth cycles with a 95% confidence interval, while the covariance
component captures the persistence of the cycles. The analysis of the covariance and
autocorrelation structure of the original series, as well as their cyclical components,
allows us to single out some stylized facts that seem to represent investment patterns
at the macro level in the Indian economy. The standard deviation and correlation
measures of the original series and the growth cycles from HP filter are shown in
桌子 3.8 Both public investment and private investment were more volatile than
GDP, but private investment exhibited significantly higher volatility than public
投资. The volatility of the cyclical components was significantly lower than
6See Akaike, H. 1969. Fitting Autoregressive Models for Prediction. Annals of the Institute of Statistical
Mathematics. 21 (1). PP. 243–47.
7Nelson and Plosser (1982) observed first by applying ADF test with US 14 macroeconomic time series that
most of the macroeconomic time series contain unit root.
8As the growth cycles from the HP filter are highly synchronized with those from the BK filter, we have used
only cycles from the HP filter to analyze the cyclical behavior of the Indian economy.
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12 ASIAN DEVELOPMENT REVIEW
桌子 3. Estimated Statistics of Growth Cycles, 1950–2013
标准
Deviation
0.86
1.21
1.43
相对的
标准
Deviation
1
1.41
1.66
First Order
Autocorrelation
0.95
0.93
0.91
Contemporaneous
Correlation
with GDP
1
0.96
0.95
Variables
GDP
Public investment
Private investment
HP cycles
0.02
0.05
0.29
GDP
Public investment
Private investment
GDP = gross domestic product, HP = Hodrick–Prescott.
来源: Author’s estimation with data from Reserve Bank of India. 2014. Handbook of Statistics on Indian
Economy. Mumbai.
1
2.5
14.5
1
0.36
0.09
0.02
0.21
−0.06
the volatility in the original series for obvious reasons. 然而, the growth cycles
of private investment were much more volatile than the growth cycles of public
investment and the growth cycles of output. This stylized fact is roughly similar to
those observed in advanced industrialized economies, supporting the hypothesis of
classical business cycle theory.
An autocorrelation structure of the series allows us to single out the per-
sistence of the stochastic character in the macroeconomic cycles. The persistence
indicates the inertia in growth cycles and captures the length of observed fluctua-
系统蒸发散. To explain statistically the persistence of the growth cycles, we have examined
the pattern of the autocorrelation function of the original as well as the detrended
series of GDP, public investment, and private investment. Higher autocorrelation
implies a longer cycle. A positive autocorrelation coefficient indicates that higher
cycles will induce higher ones (and vice versa), while a negative coefficient indicates
that higher cycles will be followed by lower ones (and vice versa).
The large and positive values of the first order autocorrelation coefficient
of the original time series indicate the persistence of the behavior of the series of
GDP and investments. 但, the degree of persistence was very low for the growth
cycles (桌子 3). For private investment, the first order autocorrelation coefficient
was negative, implying that a lower cycle in the current period induced a larger
cycle in the subsequent period (and vice versa). The contemporaneous correlation
between the series is a rough measure of comovement, or degree of synchronization,
它们之间. While the correlation coefficients between public investment and
GDP and between private investment and GDP were positive and very high, 这
correlation between growth cycles of GDP and public investment was very low and
the correlation coefficient between growth cycles of GDP and private investment
was nearly equal to zero. 因此, investments in terms of the original series were
procyclical, but they became acyclical after detrending the series. This stylized fact,
as observed from the original series in India, follows the classical business cycle
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ENTREPRENEURSHIP, INVESTMENT BEHAVIOR, AND ECONOMIC FLUCTUATIONS IN INDIA 13
桌子 4. Estimated Relationship between Growth
Cycles of Public Investment and Output
Variables
Yc
持续的
ARMA
AR1
(西德:4)
Coefficient
0.37
−0.0001
z-statistic
1.03
−0.02
−0.19
0.04
−0.60
10.79
P>z
0.302
0.984
0.551
0
ARMA = autoregressive moving average.
来源: Author’s estimation with data from Reserve Bank of India.
2014. Handbook of Statistics on Indian Economy. Mumbai.
理论. 传统上, business cycle fluctuations of investment and output are highly
synchronized and exhibit very similar patterns (Stock and Watson 1999). 但是
cyclical behavior of investment and output as observed in this study has not been
supported by the classical business cycle hypothesis.
V. Dynamics of Growth Cycles
In terms of the autocorrelation function, we measure the persistence of the
growth cycles. A high autocorrelation coefficient implies a very persistent economic
fluctuation. We examine, 这里, the relation between the cyclicality of real GDP and
投资. We have estimated separately the relationship between the growth
cycles of private investment and GDP, and between the cycles of public investment
and GDP in a dynamic frame. We also carry out vector autoregressive (VAR) 分析
to locate the direction of causality, if any between the cycles.
The econometric model for estimating the relationship is specified as
ln I c
时间,t
= φ01 + φ11 ln yc
t−1
+ u1t
ln I c
磷,t
= φ02 + φ12 ln yc
t−1
+ u2t
(3)
, I c
磷,t
, and yc
I c
t present the cyclical components of public investment, 私人的
时间,t
投资, and output (or GDP) in period t, 分别, and uit is the white-noise
错误.
As the order of integration of the observed series of investments and output
is the same, they may be cointegrated. 然而, we are interested in the dynamic
relation between the growth cycles of investments and output. As the growth cy-
cles are stationary, we can estimate the relationship between the cycles of public
investment and GDP, and between the cycles of private investment and GDP in an
autoregressive moving-average (1, 0) 结构. The relationships are specified in
方程 (3) and the estimated results are shown in Tables 4 和 5. The cycles of
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14 ASIAN DEVELOPMENT REVIEW
桌子 5. Estimated Relationship between Growth
Cycles of Private Investment and Output
Variables
Yc
持续的
ARMA
AR1
(西德:4)
Coefficient
1.59
−0.0001
z-statistic
0.44
−0.12
0.03
0.29
0.24
23.24
P>z
0.663
0.998
0.811
0
ARMA = autoregressive moving average.
来源: Author’s estimation with data from Reserve Bank of India.
2014. Handbook of Statistics on Indian Economy. Mumbai.
桌子 6. Estimated Coefficient of VAR Model
持续的
I c
Pt−1
I c
T t−1
Yc
t−1
I c
磷
−0.01
0.06
0.82
1.12
0.02
1.14
0.69
I c
时间
−0.001
0.01
−0.07
0.42
0.04
2.75
0.43
Yc
0.00
−0.01
−0.04
0.04
0.02
0.99
0.80
R2
χ 2
P> χ 2
VAR = vector autoregressive.
笔记: The estimated coefficients are not statistically significant.
来源: Author’s estimation with data from Reserve Bank of India.
2014. Handbook of Statistics on Indian Economy. Mumbai.
both public investment and private investment are not related significantly to that
of GDP. The AR1 coefficients are also statistically insignificant. 因此, the cyclical
movement of investments did not follow the cycles of GDP supporting the fact that
investment in India, both public and private, had not been badly affected by the
cyclical downturn of the economy. 因此, investments in the Indian economy might
not be determined fully by the rational actions of the entrepreneurs but by the animal
spirits, at least partly.
We also estimate the following VAR model to look at the dynamics and the
direction of causality, 如果有的话, between the growth cycles of investments and output:
ln I c
时间,t
= α01 + β11 ln I c
时间,t−1
+ β12 ln I c
磷,t−1
+ β13 ln yc
t−1
+ ε1t
ln I c
磷,t
= α01 + β21 ln I c
时间,t−1
+ β22 ln I c
磷,t−1
+ β32 ln yc
t−1
+ ε2t
ln yc
t
= α03 + β31 ln I c
时间,t−1
+ β32 ln I c
磷,t−1
+ β33 ln yc
t−1
+ ε3t
(4)
The VAR structure has been specified in Equation (4) and the estimated results
are displayed in Table 6. The estimated coefficients are statistically insignificant in
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ENTREPRENEURSHIP, INVESTMENT BEHAVIOR, AND ECONOMIC FLUCTUATIONS IN INDIA 15
every equation. We do not have causality in either direction. Neither the growth
cycle of public investment nor the growth cycle of output have had a significant
effect on the growth cycle of private investment. The estimated figures shown in the
lower panel of Table 6 also suggest there is not a significant relationship between
他们.
六、. 结论
This study attempts to look into the behavior of growth cycles of private in-
vestment in India in terms of the entrepreneurial impulse. Animal spirits, in Keynes’
看法, characterize the entrepreneur’s decision to undertake investments in the ab-
sence of sufficient information to gauge the probability of success. Keynes’ fun-
damental insight that animal spirits play an important role in affecting investment
decisions may be relevant in analyzing fluctuations in output and investment in India
not only during the recent global financial crisis, but also during different phases of
state control. In India, private investment has exhibited significantly higher volatility
than output. This finding in our study is roughly similar to observations in advanced
industrialized economies. For private investment, lower cycles in the current pe-
riod induced larger cycle in the subsequent period (and vice versa). Investments
were procyclical, but after detrending, the series investments became acyclical. 这
cyclical pattern of investment in India did not follow the classical business cycle
假设. The growth cycles of private investment and output as observed in this
study are not similar and exhibit very different patterns in every cyclical phase. 这是
evident that the cyclical movement of private investment did not follow the cycles of
输出. The fluctuations at growth cycle frequencies of private investment appeared
even when there were no shocks to GDP. These fluctuations in private investment
may be due to the self-fulfilling beliefs of investors.
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