英文翻译 模板

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 Growth

in China

Wang XJ (Wang Xijun)

School of Economics & Management, Weifang University of China,

xjwang69@https://www.sodocs.net/doc/1f6063841.html,

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 test

I. INTRODUCTION

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. 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. METHODS

The 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 test

The 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 test

In 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. Engle

and 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 its

long-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 test

Based 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 RESULT

A. Data and variables

The 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 after

transformation 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 test

Before 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 of

the 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 model

As 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 ? ?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. CONCLUSION

The 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 policies

and 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 Biography

Wang 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@https://www.sodocs.net/doc/1f6063841.html,

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。住房

是最基本的生活资料是人们必不可少的生命,住房消费是重要的材料劳动力再生产的条件。本研究中,根据中国的统计数据从1985年到2007年,通过采用协整理论、格兰杰因果关系检验和误差修正模型(ECM),之间的关系进行分别消费、住房消费和经济增长。结果表明,那里

格兰杰因果关系存在双边消费与经济增长之间。对于一个长期以来,存在长期稳定的均衡国内生产总值之间的关系、消费和住房消费;消费和住房消费既促进了GDP的增长。住房消费的增长贡献的国内生产总值明显高于消费。有一段时间了,消费刺激GDP的增长超过住房消费。

关键词:

住房消费;经济增长;

协整;ECM;格兰杰因果关系检验

一、介绍

消费是一个非常重要的部分在社会繁殖,其驾驶影响社会

经济增长总是扮演主角。住房是基本生活资料是至关重要的为人们的生活,住房消费的重要的物质条件,劳动力繁殖。在计划经济时期,中国的住房消费是严格控制的的状态,进行实用性分布。随着市场经济的发展,中国政府正式取消的政策福利住房和1998年的开展货币住房,提出的计划促进住房产业成为消费热点和经济增长点。住房消费逐渐成为新的消费热点,因此它已经被学术领域的研究焦点,各级政府。

从理论上说,住房消费属于消费经济学研究范畴。学者国外开展他们的研究在居民的住房消费行为主要由收入假说”消费函数模型。但该研究的国外的学者是基于西方发达市场

经济系统;中国仍在舞台上的逐步完善市场经济体制:尤其是房地产市场还没有完全

发达,住房价格持续增加,和居民的收入增长缓慢。在这样的背景,做研究住房消费,中国学者不能盲目地接受外国研究方法和研究思路。我们必须采取考虑到国内的实际情况,使用外国的研究途径和批判性的研究想法供参考,带他们在。目前,中国学者的研究住房消费集中在住房消费信贷、住房消费行为,住房消费影响因素,住房制度改革等等。此外,的研究主要是定性的和缺乏研究结果住房消费和经济增长。随着国外研究比较超越中国目前的经济增长,很难适用于中国;一些国内的研究并不系统,缺乏理论深度。因此,本文结合在中国的统计数据从1985年到2007年,之间的关系进行住房消费与经济增长的运用协整理论、格兰杰因果关系检验和误差修正模型(ECM)。

二、方法

目的本文实证分析为了测试住房消费之间的关系和经济增长通过协整技术。协整技术是一个新的一个应用于动态模型的设定,估计和验证。它主要分析非平

稳时间序列,建立非平稳变量的经济模型,并探讨了长期均衡非平稳变量之间的关系。首先,本文有固定时间的考验变量系列;其次,本文测试了协整关系变量之间的相关程度;第三,本文构建错误修正模型,它不仅可以检查长期关系的变量,但也检查短期因果关系;最后,本文进一步测试和分析因果关系的时间变量参与协整关系的系列。

c .固定式测试

时间序列数据的许多经济指标没有稳定过程的特点。为在非平稳的时间序列数据形成过程,传统的数理统计和计量经济学方法似乎无能为力。除了使用顺序自相关分析图表,现代计量经济学时间序列的平稳性的法官,一个更正式的方法,即,有统计测试。单位根检验是一个统计测试是普遍吗应用。这种方法的平稳性的法官特定的时间序列通过判断是否有根的团结。常用的假设检验方法包括DF 测试、ADF可信度和检验。这纸,运用ADF试验,给出了一个稳定性测试的时间序列。ADF测试是通过低劣的和富勒谁改善DF测试以确保特性的隐色噪的随机干扰项[1]。

d .协整检验

在经济领域,之前的建模技术假说的动态稳定性,实证分析基于时间序列假设时间序列是平稳的。而事实上,经济时间系列通常是不稳定的。恩格尔和格兰杰(1987)指出,如果的线性组合两个非平稳的时间序列是平稳,两个非平稳的时间序列有协整关系,也就是说,两个系列都有一个共同的时间趋势,所以它可以被视为存在长期均衡关系[3]。因此,我们可以应用协整检验的方法来测试是否存在长期均衡的协整系列之间的关系。目前,协整测试方法主要包括恩格尔格兰杰的两级Johansen协整检验和协整检验。恩格尔格兰杰协整检验提出了通过格尔和格兰杰,只以bivariant过程考虑,这过程可以仅仅拥有零或只有一个协整向量。虽然Johansen协整试验首先通过Johansen和Juselius提出,这是用于测试该协整关系吗multivariables之间通过使用最大似然估计在向量自回归系统[4][5]。本文通过采用恩格尔格兰杰的两级协整检验方法,有一个时间序列的协

整检验。的步骤恩格尔格兰杰的两级测试方法如下:

步骤1:使用普通最小二乘法(OLS)估计长期静态回归方程和计算非平衡误差。

步骤2:利用ADF统计测试来评估平稳性的残余误差系列。如果剩余误差系列估计是静止不动的,它表明存在协整关系变量。

e.大肠误差修正模型(ECM)[6]

误差修正模型首先采用萨尔贡,然后应用而被提升Herdry,安德森和戴维森。主要目的最初的应用是建立误差修正模型建立短期动态模型,以弥补对于静态模型的缺点长期。它可以反映机制的短期偏差来长期均衡以及长期不同的时间序列之间的均衡关系。近年来,错误纠正方法已经成为一个流行的分析方法在应用经济计量模型的时间序列。采用误差修正模型的方法可以通过其长期均衡项,显示的复杂其改性机理的解释变量非平衡,受长期的均衡规则在经济理论。与此同时,因为那里没有通常存在显著的统计相关性短期动态扰动项和长期的衡项目,因此我们可以做一个经济分别解释。因为只要我们解释有协整关系变量和解释变量,肯定存在唯一的格兰杰因果关系,建立模型采用纠错方法不会导致“虚假回归”,通常表现在传统的经济计量模型建立,因此,它可以清楚地揭示出的机制经济变量之间的作用。

f·格兰杰因果关系检验

基于误差修正模型(ECM),我们可以应用格兰杰因果关系检验有一个测试的两个

长期和短期的因果关系。格兰杰因果关系检验是格兰杰提出的(1969)[7]和西姆斯(1972)[8],它的基本思想预测效度的变量Y在条件,包括过去的信息变量X和Y是优于只考虑到过去信息的Y,即变量X有助于解释Y的未来变化,所以X是Y 的格兰杰因果关系,否则它是叫什么非格兰杰因果关系。

三、应用程序和结果

a .数据和变量

样品用于本文分析所有来从1985年到2007年的年度数据,这些数据所有从中国统计年鉴每年的。在本文对国内生产总值的水平的经济增长,C代表消费的价值,HC代表价值的住房消费。在为了消除通货膨胀的负面影响,本文修正GDP,C 和HC利用CPI指数;在秩序消除异方差性在时间序列,这纸转换变量根据自然对数,协整关系原创系列转换后不会改变。这个对数形式的变量是lnGDP,lnC、lnHC。除了数据处理是实现在一个计算机通过软件Eviews5.1[9][10]。

b .固定式测试

做协整分析之前,我们必须有一个单位根检验,看是否系列系列是静止的。见表Ⅰ[11]众所周知从表1,lnGDP,lnC、lnHC是不稳定的;在第一个订单差异,他们变得平稳。很明显,这三个系列都属于一阶集成我(1)。试验结果表明,该统计测试的ADF剩余误差系列e = -2.5726,小于5%的临界值,这等于-1.9572。因此,我们认为估计的残差。

c.系列是平稳序列

这表明有协变量之间的关系lnGDP,lnHC lnC、。众所周知,从模型1120年长期以来,每当消费增长1%,国内生产总值将减少0.1815%;每当住房消费增长1%,国内生产总值将增加0.3941%。

d .误差修正模型

作为存在协整关系变量间lnGDP,lnC、lnHC,我们可以建立误差修正模型(ECM)。该遵循。0.0027 0.6484 0.1096 0.3009 1 ln ln ln??Δ= +Δ+Δ?t t t GDP C HC E(5)(0.0164)(0.1557)(0.0490)(0.1580)R2 = 0.6636,R2 = 0.6076,

?形容词DW = 0.791,F = 11.8371模型5有显著性检验,符号的变量是相对cosistent与长期的均衡关系;回归系数的误差修正项et - 1含量已通过了显著性检验,误差系数校正是负值,根据反向修正机制。它显示从模型5,对于一个肖时期,GDP是受消费和住房消费的正方向,但是影响是低

四、结论

研究的结论如下:

1)格兰杰因果关系检验表明,存在格兰杰因果关系的双边消费和经济增长的滞后时每个变量是2,虽然不存在格兰杰因果关系住房消费和经济增长。消费影响GDP的增长,GDP的增长也影响消费的增加。

2)长期以来,存在着长期的稳定GDP的均衡关系,消费,和住房消费;消费和住房消费既促进GDP的增长。住房消费的增长贡献的国内生产总值是明显高于消费。每当消费增长1%,国内生产总值将增加0.1815%;当住房消费增加1%,国内生产总值将增长0.3941%。有一段时间了,消费刺激GDP的增长超过住房消费。每当消费增加了1%,GDP将增长0.6484%;当住房消费增长1%,国内生产总值将增加0.1096%。

3)从上面的测试和分析,我们可以认为,长期以来,住房消费的增长贡献的国内生产总值明显高于消费。因此,在为了提高居民的生活水平和转变中国经济增长方式政府迫切需要采取一系列的政策和措施,加强住房消费的对经济增长的带动作用,因此建立经济增长模式基于消费。

引用

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[2]和潘李子奈文清。“计量经济学”[M]。高等教育出版社,2005年。(在中国)

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[7]c·w·j·格兰杰因果关系的调查计量经济学模型和交叉谱方法[J],货物,1969年,37:424 - 438。121年

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[9]Eviews 5.0命令和编程参考,定量微软件,2004。

[10]Eviews 5.0用户指南,定量微软件,2004年。

[11]易建联丹回族,数据分析和Eviews应用[M],中国统计出版社。2002。(在中国)作者传记王Xijun,男,CUMT,博士学位,学校经济学和管理的潍坊大学教练、房地产经济学、149 Dong fengdong街,潍坊,山东,261061

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