American Journal of Industrial and Business Management, 2013, 3, 378-381
http://dx.doi.org/10.4236/ajibm.2013.34044 Published Online August 2013 (http://www.scirp.org/journal/ajibm)
Research on the Influencing Effect between CHVA and
CPI in China Based on VAR Models*
Jinge Zhou1, Juan Chen2, Xiuli Yu1,3, Yifan Li4, Qifeng Lin4
1Guangdong University of Technology School of Management, Guangzhou, China; 2Guangdong University of Technology School of
Economics and Trade, Guangzhou, China; 3Psychological Education and Research Department, Guangdong University of Technol-
ogy, Guangzhou, China; 4School of Applied Mathematics, Guangdong University of Technology, Guangzhou, China.
Received April 25th, 2013; revised May 25th, 2013; accepted June 25th, 2013
Copyright © 2013 Jinge Zhou, Juan Chen. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
The cointegration test, granger causality test, VAR model, impulse response function and other econometric methods
are used in this paper to analyze the influencing effect between commercial housing vacancy rate and CPI and its delay
impact. The results show that there is a long-term equilibrium relationship between commercial housing vacancy rate
and CPI in China. There are at least one cointegration relationship between CHVR and CPI. The past values of the CPI
appear to contain information which is useful for forecasting changes in the CHVR. CPI has a significant effect on
CHVR and CPI rising drives CHVR.
Keywords: CHVR; CPI; VAR; Cointegration
1. Introduction
The real estate industry is a leading industry of the entire
China economy and influences the quality of life of the
residents. The commercial housing vacancy rate fluctua-
tion entails real estate with commodity house prices. In
recent years, the price of China’s real estate has im-
proved extremely, and CPI also increased sharply. Hence,
some scholars believe that the commercial housing can’t
avoid the risk of inflation.
In the past few years, both domestic and overseas re-
searchers made an empirical analysis of the relationship
between the real estate and CPI. Raymond’s thesis
showed that there is no causal relationship between land
supply and housing prices and their estimations, which
based on the annual data of Hong Kong’s public land
sales, found that the government acts to maximise land
revenue [1]. Jack H. Rubens, Michael T. Bond and James
R. Webb believed that assets have the ability to protect
investors from the effects of inflation are generally la-
belled inflation hedges; the real estate has been regarded
as one of the best inflation hedges of past years [2]. Wil-
lian C. Wheaton holds the view that the vacancy rate,
fixed in the short run, determines the expected length of
sale and search, which plays a central role in the reserva-
tion prices of buyer and seller [3]. SHEN Yue and LIU
Hong-Yu researched the relationship between the real
estate development investment and GDP in China [4].
ZHOU Zhi-Chun, LI Zheng and MAO Jie researched
how real estate industry corresponds to economic vicis-
situdes, which is an empirical analysis based on Chinese
data [5]. WANG Yao-Wu, Jin Haiyuan considered the
real estate supply and land supply is the most important
factor. They also thought the second important factor is
the interest rate [6].
To conclude, both domestic and overseas reseachers
made an empirical analysis of the relationship between
real estate and CPI. However, related researching did not
underline the commercial housing vacancy rate, which is
the cornerstone of China real estate market. According to
the fact above, this paper made some research from the
perspective of effect size and effect time lag between
commercial housing vacancy rate and CPI on China real
estate market. The research is of positive value and po-
litical reference under the back ground of China’s current
economic system and real estate industrial integration.
2. Data Processing
All data series are annually begins in 1995 and ends in
2010, it shows by Table 1. Data on China’s CHVR
(which is commercial housing vacancy rate) and consumer
*This is an extended version of the paper at the 2011 International
Conference on Networks and Information.

Research on the Influencing Effect between CHVA and CPI in China Based on VAR Models 379
Table 1. 1995-2010, China’s CHVR and CPI’s annual data.
Years CHVR
The chain
growth of
CHVR
The chain
growth of CPI
1995 0.1209 0.0239 0.5450
1996 0.1397 0.0188 0.5170
1997 0.1654 0.0257 −0.0500
1998 0.1802 0.0148 −0.1330
1999 0.1960 0.0158 −0.1580
2000 0.1670 −0.0290 0.0250
2001 0.1540 −0.0130 −0.0330
2002 0.1400 −0.0140 −0.0330
2003 0.1400 0.0000 0.2670
2004 0.1179 −0.0221 0.1920
2005 0.1211 0.0032 0.1330
2006 0.1010 −0.0201 0.2330
2007 0.0820 −0.0190 0.5330
2008 0.0950 0.0130 0.1080
2009 0.1083 0.0133 0.1420
2010 0.1130 0.0047 0.3750
prices (CPI) are all from the China National Bureau of
Statistics (CNBS).
Because of the data acquisition is more difficult. We
use the annual data. This could have an impact on the
accuracy of the article.
3. Empirical Analysis
3.1. Unit Root Test
The ADF test was the first test developed for testing the
null hypothesis of root and was the most commonly used
test in practice [7].
This value is just under less than 5% critical value in
Table 2. CPI and CHVR are stationary time series. So
we can undertake next inspection. Because the serials are
same-order single integral serial. Hence, we can further
test the long-term equilibrium relationship between all
variables.
3.2. VAR Model’s Cointegration
Two time series with stochastic trends can move together
so closely over the long run that they appear to have the
same trend component, that is, they appear to have a
common trend, which are said to be cointegrated [7]. In
this section, we introduce a test for whether cointegration
is present.
CHVR and CPI share a common stochastic trend, be-
cause their prod under 0.05, it shows by Table 3. The
spread or the difference between the two rates does not
exhibit a trend. They appear to be cointegrated. Accord-
ing to the cointegration test results we can estimate that
there are at least one cointegration relationship between
CHVR and CPI.
3.3. Granger Causality Test
One useful application of the F-statistic in time series
forecasting is to test whether the lags of one of the in-
cluded regressors has useful predictive content, above
and beyond the other regressors in the model. The claim
that a variable has no predictive content corresponds to
the null hypothesis that the coefficients on all lags of that
variable are zero. This is called the Granger causality
statistic, and the associated test is called Granger causal-
ity test [8].
We consider the relationship between the CHVR and
CPI. Based on the OLS estimates (Table 4), the F-statis-
tic testing the null hypothesis that the coefficients on all
lags of the CPI is 3.83 (p < 0.1): we can conclude (at the
0.1 significance level) that the CPI Granger-causes
changes in the CHVR. It do means that the past values of
the CPI appear to contain information that is useful for
forecasting changes in the CHVR, beyond that contained
in the past values of the CHVR.
4. VAR Model Estimation
Vector autoregression (VAR) is a set of k times series
regressions, in which the regressors are lagged values of
all k series. A VAR model extends the univariate autore-
gression to a list, or “vector”, of time series variables.
The equation is called a VAR model.
Table 2. ADF test results.
value 5% level
critical value Conclusion
CPI −2.20 Stable
CHVR −2.67 Stable
Table 3. Johansen cointegration test results.
Hypothesized
No. of CE (s)Eigenvalue
Trace
Statistic 0.05 Critical Value Prob.
None 0.50 15.77 12.32 0.01
At most 1 0.35 6.01 4.13 0.02
Table 4. Test results of granger causality.
Null HypothesisF-StatisticAssociated
Prob. Conclusion
KONG does not
Granger Cause
CPI 0.45 0.65
Agree null
hypothesis
CPI does not
Granger Cause
KONG 3.83 0.06
Refuse null
hypothesis

Research on the Influencing Effect between CHVA and CPI in China Based on VAR Models 380
1
In the case of two time series variables, Yt and Xt, the
VAR (p) consists of the two equations:
10 years, the impulse responses of CPI to a one standard
deviation shock in the CHVR equation. The main results
of this contractionary shock on the other variables in the
system can be summarized as follows:
10 11
1111 1
ttp
tptp tpt
YY
YX X

 

 

 
(1)
CPI has a great influence on the CHVR. When the CPI
rises, the vacancy rates increase accordingly. The re-
sponse of CPI to CHVR is significant negative response
appears firstly—as the CHVR level rises gradually to
reach a trough two years after the initial shock. The de-
cline becomes significant after three years. Finally,
CHVR decreases steadily after the seven years to reach
its lowest level in the three years, and returns to its
pre-shock level ten years after CPI impulse.
2021 1
22112
tt
2
p
tptp tpt
XY
YX X

 

 

 
(2)
where the β’s and γ’s are unknown coefficients and μ1t
and μ2t are error terms.
The VAR assumptions are the time series regression
assumptions of Key Concept (1), applied to each equa-
tion. The coefficients of a VAR are estimated by esti-
mating each equation by OLS [9]. The response of the CHVR to CPI is smaller in mag-
nitude than the response of CPI to CHVR. More impor-
tantly, the shock dies out very quickly, five years after
the initial impulse so that there is no innovation paradox.
The system is estimated with annually data from 1995
to 2010, under the baseline system. In selected period,
we are constrained it by the availability of data for the
China National Bureau of Statistics. As suggested by the
relevant lag selection criteria (Akaike Information Crite-
rion, Schwartz Bayesian Criterion) we use two lags. The
CPI allows for a contemporaneous response of the
CHVR (In the Figure 1 the red lines mean error bars, the
blue one mean line of impact).
5. Summaries
The results show that there is a long-term equilibrium
relationship between CHVR and CPI in China. There are
at least one cointegration relationship between CHVR
and CPI. The CPI Granger-causes changes in the CHVR.
It is certain that the past values of the CPI appear to con-
tain information that is useful for forecasting changes in
The orthogonalized residuals of the CPI equation are
identified as CHVR. Figure 1 reports, over a period of
-.01
.0 0
.0 1
.0 2
12345678910
Response of KZL to KZ L
-.01
.00
.01
.02
12345678910
Response of KZL to CPI
-.3
-.2
-.1
.0
.1
.2
.3
12345678910
Response of CPI to KZL
-.3
-.2
-.1
.0
.1
.2
.3
12345678910
Response of CPI to CPI
Response to Cholesky On e S.D. Innovati ons ± 2 S.E.
0.02
0.01
0.00
−0.01
0.02
0.01
0.00
−0.01
0.3
0.2
0.1
0.0
−0.1
−0.2
−0.3
0.3
0.2
0.1
0.0
−0.1
−0.2
−0.3
Figure 1. Impulse responses to a CHVR shock-system with CPI (1995-2010).

Research on the Influencing Effect between CHVA and CPI in China Based on VAR Models 381
the CHVR, beyond that contained in the past values of
the CHVR, not vice versa. CPI has a great influence on
the CHVR. When the CPI rises, the vacancy rates in-
crease accordingly. The response of CPI to CHVR is
significant response appears for the first—as the CHVR
level rises gradually to reach a trough two years after the
initial shock. The decline becomes significant after three
years. Finally, CHVR declines steadily after the seven
years to reach its lowest level in the three years, and re-
turns to its pre-shock level ten years after CPI impulse.
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