An Empirical Analysis of the Relationship Between
Entrepreneurial Activity and Macroeconomic Conditions
Bahram Adrangi
Mary E. Allender
The University of Portland
I. Introduction
It is well established in the literature
on entrepreneurship that entrepreneurs and new ventures are a significant
source of economic growth and job creation in the U.S. economy (Global
Entrepreneurship Monitor, 1999, 2000; Kirchoff 1994,
1998; Birch, 1979; Baumol, 1993; Dennis, 1993;
Schumpeter, 1934, 1942). Other work discusses the relationship between
entrepreneurship and economic growth as symbiotic rather than causal (Acs, Carlsson, and Karlsson, 2001).
The literature is less well developed concerning the impact that macroeconomic
variables have on levels of entrepreneurship and job creation. For example,
just as the level of interest rates affects the level of investment, economic
theory would suggest that similarly, interest rates should affect
entrepreneurial activity.
Some of the existing literature allows us
to extrapolate conclusions regarding the collective influence of macroeconomic
variables on entrepreneurial growth. A recent study by Dun and Bradstreet
(Business Week, 7/10/00) notes that the number of new businesses incorporated
in 1999 was 20 percent lower than in 1994. However, during the same period, the
number of jobs created by these firms increased slightly. One possible
explanation is that the economy was showing signs of weakening by 1999 which
could explain the decline in business starts but the labor market was still
strong suggesting that workers were more willing to change jobs and start their
own businesses. Overall, we could conclude that slower economic growth inhibits
business startups while low unemployment encourages them.
Kirzner (1979)
shows the opposite to be true. Economic downturns encourage entrepreneurship
because unemployment is rising and workers have few alternative sources of
work. Yusuf and Schindutte (2000) show that in economies where growth is weak, more “survivalist” entrepreneurs
as opposed to innovative, growth oriented firms tend to appear. A recent study
by Martindale-Hubbell (New York Times, 8/4/02) reported that a survey showed
that only 17 percent of respondents cited the slow economy as a motivating
factor behind the choice to become an entrepreneur. The number one motivating
factor cited by 82 percent of respondents, allowed to choose more than one
answer, was being one’s own boss.
Other studies look at fiscal policy
efforts to stimulate the growth of new businesses (Sherman, 1999; Li, 2002).
These studies note that entrepreneurial activity contributes significantly to
overall economic growth and job creation but that many new innovative firms are
undercapitalized. This problem is exacerbated by high interest rates causing
capital sources to dry up and that can be associated with a slowing economy.
Often referred to as incubation programs, this form of fiscal policy provides
credit subsidies to new businesses.
A question of interest is the extent to which
the stock market affects entrepreneurial activity. Does a sluggish or
conversely, a robust stock market have an influence on the choice to start a
new venture? Forbes Magazine (10/29/01) suggests that since a stock market
malaise tends to hurt large corporations, entrepreneurs may feel the pain as
well.
This paper seeks to add to the literature
by looking at the individual impact of macroeconomic variables on
entrepreneurial activity. The array of macroeconomic variables we consider as
affecting entrepreneurship and job creation consists of interest rates, the
current account balance, variations in the stock market, GNP, and the money
supply. We conclude that GNP measured as industrial production, the current
account balance, and interest rates have a significant effect on the births of
new firms. This work also looks into the question of whether the type of
industry in which an entrepreneurial firm is operating has any effect on the
birth of new firms. For example, we can compare service industries and
manufacturing industries in terms of where the most entrepreneurial growth is
occurring. Our paper proceeds in section II by looking at the theoretical
framework underlying our empirical analysis. Section III describes our data
set. Section IV presents our findings and section V offers our conclusions.
II. The Theoretical Framework
Our model looks at the empirical
relationship between macroeconomic conditions and entrepreneurial activity as
measured by the birth rate of firms. As the literature suggests (Adrangi,
Allender, and Anderson, 2002; ICSB, 2000; Kirchoff,
1994,1998; Zacharikis,
Reynolds, Bygrave, 1999), the overall strength of the
economy plays an important role in entrepreneurial activity or the births and
deaths of firms. Thus, macroeconomic conditions should be critical to
entrepreneurial activity. We proceed by laying that theoretical foundation for
our empirical model. Six macroeconomic variables are discussed in terms of
their impact on entrepreneurial activity through the mechanism of aggregate
demand and aggregate supply.
a. GNP
When GNP increases consumption and
investment increase, and so aggregate demand for products and services
increases. This should create more opportunities for entrepreneurs and on an
empirical level, we should see more firm births during periods of substantial
growth in GNP. Newly formed firms should experience healthier rates of growth
as well. Finally, employment turnover in the economy should decline.
An increase in investment resulting from
an increase in GNP will cause an increase in full employment GNP that is
reflected in a rightward shift of the aggregate supply curve. This will
increase market opportunities for entrepreneurs. Figure 1 illustrates these
propositions using the aggregate demand-aggregate supply framework.
(See
Figure 1)
The increase in GNP will cause the
aggregate demand curve AD0 to shift right to AD1 while
the described increase in investment will shift the aggregate supply curve
right from AS0 to AS1.
b. Variations
in the stock market
Stock market growth and fluctuations
affect the net wealth of investors. It is important to note that while the
stock market and the economy often move in the same general direction, this is
not always true (Blinder, 2002). The fundamentals of the economy – growth,
productivity, inflation, consumer spending and investment- may remain sound
while the stock market experiences a drop in stock prices. Such has been the
case more or less since the first two quarters of 2002. This may reduce credit
available to new firms. “Many banks are tightening credit requirements for
small-business loans, venture capitalists are growing more demanding, and angle
investors may not be feeling so generous in an uncertain stock market (Business
Week, July 10, 2000). In addition, the entrepreneur is likely to respond to
stock market fluctuations as its larger, more established counterparts do. In
the long run, a persistent malaise in the stock market may cause consumer and
firm confidence to decline with a resulting decline in aggregate demand and a
market environment that is less hospitable to entrepreneurs.
In the long run, stock market fluctuations
will have no impact on output and full employment GNP since these variables are
based on economic fundamentals.
c. Current
Account Balance
Improving
the current account balance increases aggregate demand in the economy as more
U.S. goods are exported. This in turn increases real GNP with the multiplier
effect. Rising GNP creates an economic environment that is conducive to the
birth and expansion of entrepreneurial firms. Furthermore, the current account
balance is reported every month by the financial media. Its positive balances
give the U.S. equity markets a boost, while negative balances send equity
markets down. Therefore, in sum, the economic and psychological effects of the
current account balance on entrepreneurial activities may be important.
d. Interest Rates
When
interest rates decline as a function of monetary policy, credit becomes cheaper
and more plentiful, and aggregate demand increases with investment and
consumption. This will increase GNP and should provide greater market
opportunities for entrepreneurs, fewer entrepreneur firm deaths and less
employment turnover. An increase in investment associated with lower interest
rates will increase full employment GNP, shifting the aggregate supply function
to the right, thus providing more opportunities for entrepreneurs.
The
objective of the Federal Reserve in recent years has been to reduce interest
rates when recessionary pressures are present and raise rates whenever the
economy is appearing to overheat. Therefore, periods of rising interest rates
in recent years have correlated with rapid economic growth. This type of
economic environment may be particularly conducive to entrepreneurial
activities because usually these activities are financed by investors and
venture capitalists and not necessarily through borrowing. It stands to reason
that during periods of high economic growth and rising interest rates,
entrepreneurial firms may find it easier to raise the necessary capital. Thus,
ironically, it is conceivable that rising interest rates and entrepreneurial
activities may be positively correlated because rising rates may be acting as a
proxy for the growing economy.
III. Data
The data set for this study is provided by
the Databases for the Study of Entrepreneurship. This database provides
information on the birth and expansion of entrepreneurial firms classified by
firm size as well as industry type. Manufacturing, “Other Productive,”
Distributive, and Service industry firms with numbers of employees less than
twenty, between twenty and four hundred ninety nine, and more that five hundred
are considered. The data set spans 1989-1996, allowing for reliable statistical
inferences. Given the nature of the pooled time series and cross sectional data
base, the Ordinary Least Squares method (OLS) may not be appropriate for the
estimation of the models under study. Therefore, we employ an estimation method
that addresses potential problems presented by our type of data. In order to
avoid spurious regression estimates and inferences, all variables are initially
tested for stationarity by unit root tests. The
following is a brief description of the objectives and methods of these tests.
Table 1 reports the findings of the ADF
(Dickey and Fuller (1979)) and PP (Phillips (1987)) tests of unit roots. Panel
A and B present unit root test results for level series and their percentage
changes, respectively. The ADF entails estimating Δt= α+β xt-1 + ∑ YjΔxt-j +Ut and testing the null
hypothesis that b=0 versus the alternative of b<0, for any x. The lag length j in the ADF test
regressions are determined by the Akaike Information
Criterion (AIC). The PP test estimates Δ xt= a + α+β xt-1+Ut and tests the null hypothesis
that b=0 versus the alternative
of β<0. Three variations
of the ADF and PP regressions are estimated: with intercept, trend and
intercept, and neither trend nor intercept. The purpose of this approach is to
insure that the test results are robust in the presence of drifts and trends. The
PP test may be more appropriate if autocorrelation in the series under
investigation is suspected. The statistics are transformed to remove the
effects of autocorrelation from the asymptotic distribution of the test
statistic. The formula for the transformed test statistic is given in Perron (1988). The lag truncation of the Bartlett Kernel in
the PP test is determined by Newey and West (1987). In
both the ADF and PP tests the MacKinnon (1990) critical values are used. Accepting
the null hypothesis means that the series under consideration is not stationary
and a unit root is present.
Following stationarity tests, two sets of regression models are proposed to determine the effects of
size and industry on birth, death, business expansion, and business
contraction. In each regression equation, the dependent variable (employment
birth or death, for example) is regressed on a set of dummy variables that
capture the size or industry effects. The following regression models are
therefore estimated:
Y= α + β2 IPt + β3 IDt + β4 IMt +λ1 SSt + λ2 SMt ++ Xt +ut. (1)
In equation
(1) the dependent variable (establishment birth or expansion, for example) is a
function of the type of industry. Variables IP, ID, and IM, represent
productive, distribution, and manufacturing firms. The objective is to test
whether the type of the industry in which an entrepreneurial firm is operating
has any effect on birth or expansion of the firm. For example, a positive and
statistically significant b3 would
indicate that an entrepreneurial firm in the distribution industry is
contributing to employment creation. Variables IP, ID, IM assume values of one
for productive, distributive, and manufacturing sector firms, respectively, and
zero otherwise. The parameter a captures the effects of service entrepreneurial firms.
Variables
SS and SM represent small and medium entrepreneurial firms, while a partially captures the effects of large size
entrepreneurial firms on the dependent variable. For example, a positive and
statistically significant l would indicate that size of firm may contribute to the birth of entrepreneurial firms. The Variable Xt represents a vector of macroeconomic variables. Nine variations of the equation
are estimated by Newey –West heteroscedasticity and autocorrelation consistent method (NWHAC) (Newey and West (1987)). This method allows for a general covariance matrix estimator
that takes into account both the possibility of serially correlated and heteroscedastic residuals in our pooled time series and
cross section data. In each variation, different combinations of the
macroeconomic variables are the explanatory variables. Thus, subsets of vector Xtrepresent the macroeconomic
explanatory variables discussed above.
IV. Empirical findings
In this section we describe our findings
from a series of estimated regressions. The summary statistics indicate a
slight deviation from normality. These types of deviations may stem from the
nature of our pooled time series and cross sectional observations and may
suggest estimation methods that adjust for non normality of underlying variable
distributions. Results of the ADF and PP stationarity tests are reported in Table 1. It is shown that variables in level are
stationary by the PP test. When the variables are measured in first difference,
then both the PP and ADF test suggest that the variables are stationary. Because
interpreting regressions on first differences of variables are hard to
interpret, we report our regressions in levels of variables. However, the first
difference regressions produced qualitatively the same results and are
available from the authors upon request.
The macroeconomic variables that we have
considered are listed below.
CAB=
the current account balance
CPI=
Consumer price index
DJI=
Dow Jones Industrial Average
INPRD=
Industrial Production Index
M2=
M2 measure of money supply
PRMT=
the Prime rate of interest
FFR=
the federal funds rate
In each case the prefix PC indicates the
percentage change (the rate of change). Lags of the macroeconomic variables
(-1) are also included as explanatory variables. These variables are discussed
in the theoretical section of the paper and are likely to have an effect on
entrepreneurial firms and their activities. Both the birth of entrepreneurial
firms and expansion by these types of firms are considered. Tables 2-A and 2-B,
respectively, present results for regressions that examine factors affecting
the birth and expansion of entrepreneurial firms. Nine regression equations are
estimated to analyze birth and expansion activities. The first equation
includes all the relevant macroeconomic explanatory variables, while the
remaining regression equations allow us to investigate the effects of
individual macroeconomic variables on various types of entrepreneurial firms.
The Newey –West
autocorrelation and heteroscedasticity adjusted
parameter estimates of equation (1) are presented in Table 2. Table 2-A shows
that birth of entrepreneurial firms and the small size of these firms are
positively correlated. This finding may be interpreted as evidence consistent
with the hypothesis that entrepreneurial firms at birth tend to be small. Furthermore,
the dummy variables representing type of industry indicate that the birth of
entrepreneurial firms in the U.S. during the period of this study was not
occurring in manufacturing, processing, or distributive industries. One may
conclude that most entrepreneurial birth has occurred in the service sector. This
is consistent with data from the U.S. statistical handbook. Furthermore, with
the advent of information technology, it is plausible to assume that during the
period of this study entrepreneurs were attracted primarily to technology
intensive service areas.
The macroeconomic variables that appear to
have a statistically significant affect on the birth of entrepreneurial firms
seem to be industrial production and the U.S. current account. The U.S. current
account changes have a positive correlation with the birth of entrepreneurial
firms. This result may be interpreted as a positive relationship between the
birth of these firms and improvements in the U.S. export position. Again,
changes in industrial production are not a factor in the birth of
entrepreneurial companies.
Equations 1 through 9 (columns 1 through 9 . Tables 2-A and 2-B) include various combinations of the
macroeconomic variables that are discussed. We excluded some variables purely
on econometric grounds. For example, some of the variables proved to be
severely multicollinear with others that are included and, therefore, the
statistical results were either meaningless or unobtainable. Therefore, nine
regressions were estimated to test the effects of changes in other important
macroeconomic variables.
Table 2-A shows that the changes in the
money supply in current and past periods do not have any effects on the birth
of entrepreneurial firms. The only variables that seem to have any significant
correlation with the birth of entrepreneurial firms are the federal funds
rates, the prime rate, and industrial production. In all cases the coefficient
of determination indicates the explanatory variables included in the regression
are capturing approximately sixty seven to seventy percent of the variation in
the entrepreneurial birth activities. At first glance, it may appear that the
signs of federal fund and the prime rates are counter to theoretical
expectations. However, as is common with most changes in macroeconomic
variables, there may be offsetting forces at work. For example, changes in
prime rates perhaps do not affect the birth of entrepreneurial firms because
individual investors or venture capitalists often fund these activities. Therefore,
during periods of healthy economic growth, which often coincide with rising
prime rates and federal funds rates, entrepreneurs are more likely to be able
to assemble the potential groups of investors.
On the contrary, during recessionary
periods, similar to years 2001-2002, while the federal funds rates and prime
rates were falling, general economic conditions and consumer sentiments were
not conducive to the birth of entrepreneurial firms. Indeed, as explained
above, action by the Federal Reserve to lower the federal funds rates, may be signaling deteriorating economic conditions. Thus,
the economic climate may not be suitable for the birth of entrepreneurial
activities.
Turning to Table 2-B, we see that
expansion of entrepreneurial firms is significantly correlated with the Dow
Jones Industrial average, industrial production, the prime rate, the inflation
rate, and the current account balance. These results indicate that expansion of
entrepreneurial firms takes place in a different economic environment than the
birth of these firms. For example, rising equity prices, rising prime and
federal funds rates, an improving current account balance, and falling
inflation rates are conducive to the expansion of entrepreneurial firms. Thus,
once the regression models account for stock market variations, some of the
variables that were insignificant in Table 2-A, became statistically
significant. This finding is noteworthy because it shows that equity market
conditions are especially important in explaining entrepreneurial activities.
Rising equity prices may afford
entrepreneurial firms opportunities to expand by means of issuing equity
shares. Furthermore, these firms may be able to use their own shares to
purchase other firms. Rising interest rates may also signal generally improving
economic conditions. For example, the standard loanable funds framework suggests that rising interest rates occur because of increasing
demand for loanable funds. Increasing demand for loanable funds means that businesses are finding more
projects potentially profitable, and therefore, are willing to borrow in the
financial markets to take advantage of these opportunities. If business
borrowing is on the rise, interest rates tend to rise as well. Entrepreneurial
firms may find these conditions favorable for expansion. Therefore, it is
conceivable that rising interest rates act as proxy for healthy economic
conditions and serves to stimulate expansion of entrepreneurial firms. In a
similar vein, an improving current account balance is a harbinger of rising
GNP. Clearly periods of rising GNP help all firms including entrepreneurial
ones.
Falling inflation rates may occur because
of shifts in aggregate demand (AD) or aggregate supply (AS). If inflation rates
fall because of declining AD, the economic environment may not be favorable for
the expansion of entrepreneurial firms. On the other hand, a decline in the
inflation rate triggered by supply side increases, provides the ideal economic context for the expansion of entrepreneurial
companies. Shifts in AS provide the simultaneous benefits of causing economic
expansion and falling general prices. Entrepreneurial firms would find this
environment ideal for growth and expansion. During the time frame under study,
the U.S. economy was experiencing a period of rapid growth accompanied by low
or no inflation. In fact the inflation rate was hovering around 2 percent
accompanied by GNP growth rate of approximately 4 to 5 percent per year. These
figures indicate an era of expanding AS due to technological advancement,
particularly in the information technology sector. Thus, it is plausible that
the falling inflation rate during this period was a result of supply side
factors. This would be a reason for a negative correlation between
entrepreneurial activities and the inflation rate as indicated in Table 2-B.
V. Conclusion
This paper empirically investigates the
effect of macroeconomic conditions on entrepreneurial activity. Specifically we
are interested in how changes in key macroeconomic variables influence the
birth and expansion of entrepreneurial firms. Several macroeconomic variables
enter our regression models. Empirical findings show that the birth of
entrepreneurial firms are influenced by the inflation rate, interest rates, and
the growth in industrial activity which serves as a proxy for GNP, and equity
markets. Results also show that the expansion of entrepreneurial firms takes
place in a different economic environment than the birth of these firms. For
example, rising equity prices, rising prime and federal funds rates, an improving
current account balance, and falling inflation rates are conducive to the
expansion of entrepreneurial firms. An inflationary environment appears to have
a negative effect on both the birth and expansion of entrepreneurial firms.
We have an expectation for future work on
this stream of research. We are interested in looking at the same questions
examined here for industries in a disaggregated form based on the SIC code. In
addition, we would like to obtain specific information on high tech industries
since birth and expansion, as well as deaths, of firms in this sector of the
economy was so prevalent during the previous decade.
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Figure 1
Table 1
Summary and Stationarity Statistics
Level ADF PP M s S K
Birth -2.43 -10.43*** 0.09 0.04 0.10 1.92
Expansion -1.66 -9.34*** 0.33 0.06 0.29 2.61
First
Difference
Birth -7.95*** -21.82***
Expansion -5.40*** -22.51***
Notes: Ms, S, and K stand for mean, standard deviation, skewness, and kurtosis. ADF and PP regressions are
estimated with intercepts and no trends. The lag structure for the ADF test is
determined based on the AIC criterion, while for the PP test Newey –West criterion is used.
*** indicates
significance at 1 percent level.
Table 2-A
Regression Estimation Results
Birth 1 2 3 4 5 6 7 8 9
C 0.118 0.097 0.101 0.099 0.10 0.100 0.090 0.103 0.107
(6.06)*** (13.08)*** (11.59) *** (9.82) *** (6.70) *** (10.74) *** (7.48) *** (15.19) *** (13.24) ***
SS 0.043 0.043 0.043 0.043 0.04 0.043 0.043 0.043 0.043
(6.99) *** (10.28) *** (9.86) *** (9.48) *** (9.33) *** (10.10) *** (6.82) *** (10.83) *** (9.65) ***
SM -0.024 -0.024 -0.024 -0.024 -0.02 -0.024 -0.024 -0.024 -0.02
(-3.04) *** (-3.49) *** (-3.44) *** (-3.51) *** (-3.55) *** (-3.38) *** (-3.88) *** (-3.52) *** (-3.72) ***
IM -0.040 -0.040 -0.040 -0.041 -0.041 -0.039 -0.041 -0.040 -0.040
(-7.12) *** (-4.93) *** (-4.78) *** (-4.85) *** (-4.88) *** (-4.74) *** (-5.50) *** (5.00) *** (-5.24) ***
IP -0.013 -0.013 -0.013 -0.013 -0.013 -0.013 -0.013 -0.013 -0.013
(-2.10) *** (-1.57) (-1.58) (-1.60) (-1.60) (-1.56) (-1.89) *** (-1.59) (-1.63)
ID -0.017 -0.017 -0.022 -0.017 -0.017 -0.018 -0.017 -0.017 -0.017
(-2.88) *** (-1.94) *** (-1.96) *** (-2.01) *** (-2.03) *** (-1.92) *** (-2.41) (-1.95) (-2.09) ***
PCCAB 0.001
(1.63)
PCCPI 0.006
(2.06) ***
PCDJI 0.014 0.018
(0.27) (0.51)
PCINPRD -0.395 -0.241
(-1.67) *** (-1.85) ***
PCM2 0.106 0.216
(.52) (1.69) ***
PRMT -0.004 0.004
(-0.90) (1.97) ***
FFR 0.003
(1.95) ***
PCCAB(-1) 0.001
(1.92) ***
PCCPI(-1) -0.001 -0.003
(0.25) (-1.00)
PCDJI(-1) -0.003
(-0.08)
-1) 0.051 -0.161
(.25) (0.97)
-1) 0.004 -0.003
(0.81) (-1.33)
FFR(-1) -0.002
(-1.30)
R2 0.711 0.671 0.663 0.671 0.675 0.656 0.674 0.67 0.69
F 15.90 21.84 20.52 21.29 21.65 19.85 21.55 26.38 28.89
*** indicates
significance at 1 percent level.
Table
2-B
Regression Estimation Results
EPN 1 2 3 4 5 6 7 8 9
C 0.323 0.330 0.330 0.336 0.327 0.328 0.301 0.093 0.308
(38.60) *** (31.75) *** (29.11) *** (19.88) *** (25.54) *** (41.43) *** (42.25) *** (7.29) *** (16.70) ***
SS -0.078 -0.078 -0.078 -0.078 -0.078 -0.078 -0.078 0.043 -0.078
(-13.79) *** (-9.86) *** (-10.72) *** (-11.09) *** (-9.10) *** (-10.02) *** (-11.82) *** (7.25) *** (-15.71) ***
SM 0.036 0.036 0.036 0.036 0.036 0.036 0.036 -0.024 0.036
(3.74) (4.54) *** (4.95) *** (5.12) *** (4.20) *** (4.62) *** (5.46) *** (-2.94) *** (4.57) ***
IM 0.061 0.064 0.068 0.067 0.062 0.064 0.066 -0.041 0.064
(6.19) *** (6.83) *** (7.85) *** (8.05) *** (6.19) *** (7.05) *** (8.47) *** (-6.85) *** (7.02) ***
IP 0.014 0.014 0.014 0.014 0.014 0.014 0.014 -0.013 0.014
(1.51) (1.57) (1.70) *** (1.76) *** (1.45) (1.59) (1.88) *** (-2.19) *** (1.67)
ID -0.001 -0.001 -0.001 -0.010 -0.001 -0.001 -0.001 -0.017 -0.001
(-0.08) (-0.06) (-0.07) (-0.07) *** (-0.06) (-0.07) *** (-0.08) (-3.02) *** (-0.82)
PCUSD 0.188
(1.27)
PCCAB 0.001
(0.97)
PCCPI
PCDJI -0.039 0.112 0.815
(-0.69) (0.67) (4.63) ***
PCINPRD 0.594 -0.515 -0.919
(4.86) (-1.15) (-1.85) ***
PCM2 -0.764
(-3.60) ***
PRMT -0.012 0.004 0.024
(-5.84) *** (0.81) (3.311) ***
FFR -0.009
(-5.28) ***
PCUSD(-1) -0.054
(-0.36)
PCCAB(-1) 0.004 0.002 0.018
(4.00) *** (0.54) (4.51) ***
PCCPI(-1) -0.021 -0.118
(-0.97) (-4.75) ***
PCDJI(-1) -0.017
(-0.28)
PCINPRD(-1) 0.278
(2.32) ***
PCM2(-1) 0.423
(1.91) ***
PRMT(-1) 0.010 0.007 0.024
(4.74) *** (1.01) (3.65) ***
FFR(-1) 0.007
(4.06) ***
R2 0.779 0.800 0.831 0.842 0.765 0.806 0.861 0.689 0.880
F 36.76 41.74 51.22 55.58 33.99 43.46 64.61 13.90 45.91
*** indicates
significance at 1 percent level. |
