互助问答125问动态空间计量模型的估计问题0001

1、您好！使用动态空间计量模型对问题进行估计时，同时使用的是矩估计的方法，如 何实现那个 AR (1)和AR(2)的检验呢？相应的代码是什么？AR(1), AR(2)是用来检验动态面板，如差分或系统GM媪否存在扰动项一阶和二阶自相关的。动态空间计量模型的使用未见推广的这些检验。空间计量中有SARg型，即空间自相关(回归)模型。动态空间计量，动态空间面板模型的构建与检验仍 然处在前沿的研究阶段，Elhost (2010)对Arrelano和bond的差分GMMt计量进 行扩展，使其包括一个内生交互效应，且同时发现这种估计量仍然可能存在严重的 偏误，特别是 Wy的参数的估计。Kukenova andM

2、onteiro (2009)、Jacobs et al(2009)同时研究了动态面板数据模型(Yt-1, Wy1 ),并扩展了 Blundell and Bond (1998)的系统 GMM&计，使之 包含内生交互效应(Wy),前者同时研究了内生解释变量 Z,后者研究了误差项 的空间自相关 Wo他们发现系统GMM&计量要彳于差分 GMM且减小了参数估计。 动态空间面板模型的stata程序如下： clear * 清空 ssc install spregdpd(or spregdhp) * 安装 spregdpd 或 spregdhp 动态空间面板 模板 help spregdpd

3、(or spregdhp) *参阅帮助文件(有案例) help输出： Title+ spregdpd: Spatial Panel Arellano-Bond Linear Dynamic Regression:Lag & Durbin Mo > delsSyntax+spregdpd depvarindepvars weight , nc(#) wmfile(weight_file)model(sar|sdm)run(xtabond|xtdhp|xtdpd|xtdpdsys) be fe relmspac lmhet lmnormdiag tests stand inv inv

4、2 mfx(lin, log)noconstantpredict(new_var)resid(new_var) inst(vars) diff(vars) endog(vars) pre(vars) dgmmiv(varlist) collzero tolog twostep level(#) vce(vcetype) Model Options +1- model(sar)MLE Spatial Panel Lag Model (SAR)2- model(sdm)MLE Spatial Panel Durbin Model (SDM)Run Options+1- run(xtdhp) Reg

5、ression 2- run(xtabond) Regression3- run(xtdpd) Regression4- run(xtdpdsys)NEWHan-Philips (2010) Linear Dynamic PanelxtabondArellano-Bond Linear Dynamic PanelxtdpdArellano-Bond (1991) Linear Dynamic Panelxtdpdsys Arellano-Bover/Blundell-Bond (1995, 1998)System LinearDynamic Panel RegressionOptions+*n

6、c(#)Number of Cross Sections UnitsTime series observationsmust be Balanced in each Cross Sectionwmfile(weight_file) OpenCROSS SECTION weight matrix file.teststwostepdisplay ALLlmh, lmn, lmsp, diag tests two-step estimate run(xtdpd)dgmmiv(varlist)DifferencedEquation run(xtdpd) inst(varlist)Variablesr

7、un(xtabond)length is lag(1)diff(varlist)ExogenousVariables run(xtabond) endog(varlist)run(xtabond,xtdpdsys) pre(varlist)Variablesrun(xtabond, xtdpdsys)GMM Instruments forAdditional InstrumentalDependent Variable LagAlready DifferencedEndogenous VariablesPredeterminedvce(vcetype) ols,robust, cluster,

8、 bootstrap, jackknife, hc2, hc3level(#) islevel(95)confidence intervals level; defaultSpatial Panel Aautocorrelation Tests+lmspac Spatial Panel Aautocorrelation Tests:* Ho: Error has NoSpatial AutoCorrelationHa: Error hasSpatial AutoCorrelation* Spatial Econometrics Regression Models:* (1) Spatial P

9、anel Data Regression Models:spregxtSpatial Panel Regression Econometric Models:StataModule Toolkitgs2slsxtGeneralized Spatial Panel 2SLS Regressiongs2slsarxt Generalized Spatial Panel Autoregressive 2SLS Regression spglsxtSpatial Panel Autoregressive Generalized LeastSquaresRegressionspgmmxtSpatial

10、Panel Autoregressive GeneralizedMethod ofMoments Regressionspmstarxt (m-STAR) Spatial Lag Panel Models spmstardxt(m-STAR) Spatial Durbin Panel Modelsspmstardhxt(m-STAR) Spatial Durbin MultiplicativeHeteroscedasticityPanel Modelsspmstarhxt (m-STAR) Spatial Lag Multiplicative HeteroscedasticityPanel M

11、odelsspregdhpSpatial Panel Han-Philips Linear DynamicRegression: Lag& Durbin ModelsspregdpdSpatial Panel Arellano-Bond Linear DynamicRegression:Lag & Durbin Modelsspregfext Spatial Panel Fixed Effects Regression: Lag & DurbinModelsspregrext Spatial Panel Random Effects Regression: Lag &a

12、mp;Durbin ModelsspregsacxtMLE Spatial AutoCorrelation Panel Regression (SAC)spregsarxtMLE Spatial Lag Panel Regression (SAR)spregsdmxt MLE Spatial Durbin Panel Regression (SDM) spregsemxt MLE Spatial Error Panel Regression (SEM)* * (2) Spatial Cross Section Regression Models: spregcsSpatial Cross Se

13、ction Regression EconometricModels:Stata Module Toolkitgs2slsGeneralized Spatial 2SLS Cross SectionsRegressiongs2slsarGeneralized Spatial Autoregressive 2SLS CrossSectionsRegressiongs3slsGeneralized Spatial 3SLS Cross SectionsRegressiongs3slsarGeneralized Spatial Autoregressive 3SLS CrossSectionsReg

14、ressionspautoreg Spatial Cross Section Regression ModelsspgmmSpatial Autoregressive GMM CrossSections Regressionspmstar(m-STAR) Spatial Lag Cross Sections Modelsspmstard(m-STAR) Spatial Durbin Cross Sections Modelsspmstardh (m-STAR) Spatial Durbin MultiplicativeHeteroscedasticity Cross Sections Mode

15、ls spmstarh(m-STAR) Spatial Lag MultiplicativeHeteroscedasticityCross Sections Models spregsacMLE Spatial AutoCorrelation Cross SectionsRegression(SAC) spregsarMLE Spatial Lag Cross Sections Regression(SAR)spregsdmMLE Spatial Durbin Cross Sections Regression(SDM)spregsemMLE Spatial Error Cross Secti

16、ons Regression(SEM)* * (3) Tobit Spatial Regression Models:* *(3-i)Tobit Spatial Panel Data Regression Models: sptobitgmmxt Tobit Spatial GMM Panel Regression sptobitmstarxtTobit (m-STAR) Spatial Lag Panel Models sptobitmstardxtTobit (m-STAR) Spatial Durbin Panel Models sptobitmstardhxtTobit (m-STAR

17、) Spatial Durbin Multiplicative HeteroscedasticityPanel ModelssptobitmstarhxtTobit (m-STAR) Spatial Lag Multiplicative HeteroscedasticityPanel Modelssptobitsacxt Tobit MLE Spatial AutoCorrelation (SAC) Panel Regression sptobitsarxt Tobit MLE Spatial Lag Panel Regression sptobitsdmxt Tobit MLE Spatia

18、l Panel Durbin Regression sptobitsemxt Tobit MLE Spatial Error Panel Regression spxttobit Tobit Spatial Panel Autoregressive GLS Regression* (3-2) Tobit Spatial Cross Section Regression Models: sptobitgmm Tobit Spatial GMM Cross Sections Regression sptobitmstar Tobit (m-STAR) Spatial Lag Cross Secti

19、ons Models sptobitmstardTobit (m-STAR) Spatial Durbin Cross Sections Models sptobitmstardhTobit (m-STAR) Spatial Durbin Multiplicative HeteroscedasticityCross SectionssptobitmstarhTobit (m-STAR) Spatial Lag Multiplicative Heteroscedasticity CrossSectionssptobitsac Tobit MLE AutoCorrelation (SAC) Cross Sections Regression sptobitsa