目录Housing Consumption and Economic Growth in China (2)住房消费和经济增长在中国 (10)摘要 (10)关键词: (10)一、介绍 (11)二、方法 (11)c .固定式测试 (12)d .协整检验 (12)E大肠误差修正模型(ECM)[6] (13)f.格兰杰因果关系检验 (13)三、应用程序和结果 (14)a .数据和变量 (14)b .固定式测试 (14)e系列是平稳序列 (14)d .误差修正模型 (14)四、结论 (15)引用 (15)Housing Consumption and Economic Growthin ChinaWang XJ (Wang Xijun)School of Economics & Management, Weifang University of China,xjwang69@Abstract:Consumption is a very important part in social reproduction, and its driving effect on social economic growth always plays the leading role. Housing is the basic living material which is essential for people‟s life; housing consumption is the important material condition for the labor force reproduction. This study, based on China‟s statistical data from 1985 to 2007,by employing co-integration theory, Granger causality test and error correction model (ECM),respectively investigates the relationship between consumption, housing consumption and economic growth. The empirical result denotes that there exists bilateral Granger causality relationship between consumption and economic growth. For a long period, there exists long term stable equilibrium relationship between GDP, consumption, and housing consumption; consumption and housing consumption both promote the growth of GDP. Housing consumption‟s contribution to the growth of GDP is obviously higher than consumption. For a short period, consumption spurs the growth of GDP more than housing consumption.Keywords:Housing consumption; Economic growth;Co-integration ; ECM; Granger causality testI. INTRODUCTIONConsumption is a very important part in social reproduction, and its drivingeffect on social economic growth always plays the leading role. Housing is the basic living material which is essential for people‟s life; housing consumption is the important material condition for the labor force reproduction. In the period of planned economy, China‟s housing consump tion was strictly controlled by the state, carrying out practicality distribution. With the development of market economy, the government of China formally cancelled the policy of welfare housing in 1998 and carried out that of currency housing, putting forward the plan of fostering housing industry into consumption hotspot and economic growth point. Housing consumption gradually becomes the new consumption hotspot; thus it has been the research focus of academic field and the governments of all levels. Theoretically, housing consumption belongs to the research category of consumption economics. Scholars abroad carry out their research on residents‟ housing consumption behavior mainly by …income hypothesis‟ consumption function model. But the research of scholars abroad is based on western developed market economy system; China is still on the stage of gradually perfecting market economy system: especially the real estate market has not fully developed, housing price is persistently increasing, and residents‟ income grows slowly. On such background, doing research on housing consumption, Chinese scholars cannot blindly take in foreign research approaches and research ideas. We must take domestic actual situation into consideration and critically use foreign research approaches and research ideas for reference and take them in. Presently, Chinese scholars‟ research on housing consumption focuses on housing consumption credit, housing consumption behavior, housing consumption affecting factors, housing system reform and so on. Besides, the research is mostly qualitative and lacks the research results of housing consumption and economic growth. As the foreign research is comparatively beyond China‟s present economic growth, it is hard to apply in China; some domestic research is not systematic and devoid of theoretic depth. Therefore, this paper, based on China‟s statistical data from 1985 to 2007, investigates the relationship between housing consumption and economic growth by employing co-integration theory, Granger causality test and error correction model (ECM).II. METHODSThe purpose of empirical analysis in this paper is to test the relationship between housing consumption and economic growth by means of cointegration technique. Cointegration technique is a new one which is applied to dynamic models‟ enactment, estimation and verification. It mainly analyzes the nonstationarity of time series, build nonstationary variable economic model, and explore the long run equilibrium relationship between nonstationary variables. Firstly, the paper has the stationary test of time variable series; secondly, the paper tests the cointegration relationship between the variables; thirdly, the paper builds error correction model, which can not only examine the long run relationship between variables, but also examine the short run cause and effect relationship; finally, the paper make a further test and analysis of cause and effect relationship between time variable series involved in cointegration relationship.C. Stationary testThe time series data of many economic indicators do not have the feature of stable process. For the time-series data formed in nonstationary process, traditional mathematical statistics and econometrics methods seem powerless. Besides using sequential autocorrelation analytic chart, modern econometrics judges the stationarity of time series by a more formal approach, that is, to have statistical tests. Unit root test is one of the statistical tests which is universally applied. This approach judges the stationarity of a certain time series through judging whether it has roots of unity. Commonly used hypothesis testing approaches include DF test, ADF test and PP test. This paper, by employing ADF test, gives a stationarity test of time series. ADF test is achieved by Dickey and Fuller who improved DF test to ensure the characteristic of leuco-noise of random interference item[1].D. Cointegration testIn the domain of economy, previous modeling technique has hypothesis of dynamic stationarity, and empirical analysis based on time series assumes that time series is stationary. While in fact, economic time series is usually nonstationary. Engleand Granger(1987) point that if the linear combination of two nonstationary time series is stationary, the two nonstationary time series have a co-integration relationship, that is, the two series have a common time tendency, so it can be viewed that there exists a long-run equilibrium relationship[3]. Therefore, we can apply co-integration test approach to test whether there exists the long-term equilibrium co-integration relationship between series. Presently, co-integration test methods mainly include Engle-Granger‟s two-stage Co-integration test and Johansen Co-integration test. Engle-Granger Co-integration test was put forward by Engle and Granger, which only takes the bivariant process into account, and this process can merely possess nought or only one co-integration vector. While Johansen Co-integration test was first put forward by Johansen and Juselius, which is applied to test the co-integration relationship between multivariables by using maximum likelihood estimation in vector autoregression system[4][5].This paper, by adopting Engle-Granger‟s two-stage Co-integration test method, has a co-integration test of time series. The steps of Engle-Granger‟s two-stage test method go as follows: Step 1: use common least square method (OLS) to estimate the long-term static regression equation and calculate non- equilibrium error. Step 2: use ADF statistics test to estimate the stationarity of the residual error series. If the residual error series is estimated to be stationary, it suggests there exists a co-integration relationship between Variables.E. Error correction model(ECM)[6]Error correction model was firstly adopted by Sargon, and then its application was promoted by Herdry, Anderson and Davidson. The main purpose of the initial application of error-correction model is to set up short-term dynamic model so as to make up for the shortcomings of long-term static model. It can reflect the mechanism of the short-term deviation to long-run equilibrium as well as the long-run equilibrium relationship between different time series. In recent years, error-correction method has become one of the prevailing analyzing methods in applying economic measurement time series model. Adopting the method of error-correction model can, through itslong-term equilibrium item, concentratively displays the modification mechanism of explained variables to non-equilibrium, driven by the long-term equilibrium rule in economic theory. Meanwhile, as there does not usually exist remarkable statistic relativity between short-term dynamic perturbation item and long-term equilibrium item, thus we can make an economic explanation respectively. Because so long as we explain there is a co-integration relationship between variables and explained variables, there surely exists the only Granger-causality relationship, to set up models by applying error-correction method won‟t result in …false regression”, as is usually shown in traditional economic measurement model building, therefore, it can clearly reveal the mechanism of action between economic variables.F. Granger causality testBased on error correction model (ECM), we can apply Granger causality test to have a test of both long-term and short-term cause and effect relationship. Granger causality test was put forward by Granger (1969)[7] and Sims (1972)[8], with its basic idea that the predictive validity of the variable Y under the condition of including the past information of the variables X and Y is superior to that of only considering the past information of Y, that is, the variable X helps to explain Y‟s future variation, so X is the Granger-causality of Y, or else it is called non-Granger-causality.III. APPLICATION AND RESULTA. Data and variablesThe samples used for the paper analysis all come from annual data from 1985 to 2007, and the data are all from China Statistical Yearbook of every year. In this paper, GDP stands for the level of economic growth, C stands for the value of consumption, and HC stands for the value of housing consumption. In order to eliminate ill effects of inflation, this paper amends GDP, C and HC by using CPI index; in order to eliminate heteroskedasti city in time series, this paper transforms variables according to natural logarithm, and the cointegration relationship between original series aftertransformation does not alter. The logarithmic forms of variables are ln GDP, ln C and ln HC. Besides data processing is fulfilled on a computer by means of software Eviews5.1[9][10].B. Stationary testBefore making a cointegration analysis, we must have a unit root test of series to see whether the series is stationary. As shown in Table Ⅰ[11]. It is known from Table 1 that ln GDP, ln C and ln HC are non-stationary; after the first order difference, they become stationary. It is obvious that these three series both belong to first order integration I (1).C.Test results show that the statistic of ADF test ofthe residual error series e equals -2.5726, less than the critical value of 5%, which equals -1.9572. Accordingly, we consider the estimated residual error series e to be stationary series, which indicates there is a coin tegration relationship between variables ln GDP, ln C and ln HC. It is known from Model 1 that for a 120 long period, whenever consumption increases by 1%, GDP will decrease by 0.1815%; whenever housing consumption increases by 1%, GDP will increase by 0.3941%.D. Error correction modelAs there exists a cointegration relationship between variables ln GDP, ln C and ln HC, we can build error-correction model (ECM). The model follows. 0.0027 0.6484 ln 0.1096 ln 0.3009 1 ln ˆ −Δ = + Δ + Δ −t t t GDP C HC E (5) (0.0164)(0.1557) (0.0490) (0.1580) R2 = 0.6636 , Adj−R2 = 0.6076 , DW=0.791,F =11.8371 Model 5 has had the significance test, and the symbols of variables are comparatively cosistent with those of long run equilibrium relationship; the regression coefficient of error correction item Et-1 has passed the significance test and the coefficient of error correction is negative value, according with reverse correction mechanism. It is shown from Model 5 that for a shor period, GDP is affected by consumption and housing consumption in the positive direction, but the impact is low.IV. CONCLUSIONThe conclusions of the study are as follows:1) Granger causality test shows that there exists bilateral Granger causality relationship between consumption and economic growth when the lag of each variable is 2, while there does not exist Granger causality relationship between housing consumption and economic growth. Consumption influences the growth of GDP, and the growth of GDP also influences the increase of consumption.2) For a long period, there exists long term stable equilibrium relationship between GDP, consumption, and housing consumption; consumption and housing consumption both promote the growth of GDP. Housing consumption‟s contribution to the growth of GDP is obviously higher than consumption. Whenever consumption increases by 1%, GDP will increase by 0.1815%; whenever housing consumption increases by 1%, GDP will increase by 0.3941%. For a short period, consumption spurs the growth of GDP more than housing consumption. Whenever consumption increases by 1%, GDP will increase by 0.6484%; whenever housing consumption increases by 1%, GDP will increase by 0.1096%.3) From the above tests and analysis, we can conclude that, for a long period, housing consumption‟s contribution to the growt h of GDP is obviously higher than consumption. Therefore, in order to improve residents‟ living standard and transform China‟s economic growth manner, the government urgently need to take a series of policiesand measures to enforce housing consumption‟s driving effect on economic growth, thus establishing economic growth mode based on consumption.REFERENCES[1] D. A. Dickey and W. A. Fuller. “Distribution of the estimators for autoregressive time series with a unit root”[J]. Journal of american statistic al association,1979, 74: 427-431.[2] Li Zi-nai and Pan Wen-qing. “Econometrics ” [M]. Higher Education Press, 2005.(in Chinese)[3] R. F. Engle and C. W. J. Granger. “Cointegration and error correction: Representation, estimation, and testing”[J]. Econo metrica, 1987, 55:251-276.[4] S. Johansen, Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models[J], Econometrica, 1991, 59,1551-1580.[5] S. Johansen, Statistical analysis of cointegration vectors[J], Journal of Economic Dynamics and Control, 1988, 12, 231-254.[6] Stephen Malpezzi, A simple error correction model of house prices[J], Journal of Housing Economics, 1999.8:27-62.[7] C. W. J. Granger, Investigating causal relations by econometric models and cross-spectral methods[J], Econometrica，1969, 37: 424-438. 121[8] C. A. Sims, Money, Income and causality[J], American Economic Review, 1972, 62: 540-542.[9] Eviews 5.0 Command and programming reference, Quantitative Micro Software, 2004.[10] Eviews 5.0 Use r‟s guide, Quantitative Micro Software, 2004.[11] Yi Dan-hui, Data analysis and Eviews application[M], China Statistics Press. 2002. (in Chinese)Author BiographyWang Xijun, male, CUMT, Doctor‟s degree, School of Economics & Management of Weifang University, instructor, real estate economics, 149 Dongfengdong Street, Weifang, Shandong, 261061住房消费和经济增长在中国小王XJ(王Xijun)经济学院和管理、潍坊大学、中国xjwang69@摘要消费是一个非常重要的部分社会再生产,其驾驶影响社会经济增长总是扮演主角。