空间面板数据分析——R的splm包资料

空间面板数据分析R的splm包 （任建辉，暨南大学）The splm package provides methods for fitting spatial panel data by maximum likelihood and GM. 安装R软件及其编辑器Rstudio 网址： /下载好Rstudio以后，操作都可以Rstudio中完成了，包括命令的编写、命令运行、图形展示，最方便的要数查看数据了。 R界面 Rstudio界面，形如matlab下面进入正题，了解splm包中的数据、命令及结果展示。所有命令都写在编辑窗口（studio左上区域），可以单独的运行每行命令，也可选取一段一起执行，点run按钮。1、首先，安装splm包并导入，命令如下:intall.packages(“splm”)，选择最近的下载点library(splm) library(splm)载入需要的程辑包：MASS载入需要的程辑包：nlme载入需要的程辑包：spdep载入需要的程辑包：sp载入需要的程辑包：Matrix载入需要的程辑包：plm载入需要的程辑包：bdsmatrix载入程辑包：bdsmatrix下列对象被屏蔽了from package:base: backsolve载入需要的程辑包：Formula载入需要的程辑包：sandwich载入需要的程辑包：zoo载入程辑包：zoo下列对象被屏蔽了from package:base: as.Date, as.Date.numeric载入需要的程辑包：spam载入需要的程辑包：gridSpam version 0.40-0 (2013-09-11) is loaded.Type help( Spam) or demo( spam) for a short introduction and overview of this package.Help for individual functions is also obtained by adding thesuffix .spam to the function name, e.g. help( chol.spam).载入程辑包：spam下列对象被屏蔽了from package:bdsmatrix: backsolve下列对象被屏蔽了from package:base: backsolve, forwardsolve载入需要的程辑包：ibdreg载入需要的程辑包：car载入需要的程辑包：lmtest载入需要的程辑包：Ecdat载入程辑包：Ecdat下列对象被屏蔽了from package:car: Mroz下列对象被屏蔽了from package:nlme: Gasoline下列对象被屏蔽了from package:MASS: SP500下列对象被屏蔽了from package:datasets: Orange载入需要的程辑包：maxLik载入需要的程辑包：miscToolsPlease cite the maxLik package as:Henningsen, Arne and Toomet, Ott (2011). maxLik: A package for maximum likelihood estimation in R. Computational Statistics 26(3), 443-458. DOI 10.1007/s00180-010-0217-1.If you have questions, suggestions, or comments regarding the maxLik package, please use a forum or tracker at maxLiks R-Forge site:/projects/maxlik/Warning message:程辑包Matrix是用R版本3.0.3 来建造的 注意：在导入splm时，如果发现还有其他配套的包没有安装，需要先安装。 2、接着，查看数据及结构，命令如下： data(Produc,package=”Ecdat”) View(Produc) 3、引入空间权重矩阵（spatial weights matrix），命令如下data(usaww)Views(usaww)4、空间面板数据的广义矩估计，命令spgmGM GM summary(GM)Spatial panel fixed effects GM modelCall:spgm(formula = log(gsp) log(pcap) + log(pc) + log(emp) + unemp, data = Produc, listw = usaww, spatial.error = TRUE, moments = fullweights)Residuals: Min. 1st Qu. Median 3rd Qu. Max. -0.14000 -0.01950 -0.00316 0.01530 0.16800 Estimated spatial coefficient, variance components and theta: Estimaterho 0.sigma2_v 0.Coefficients: Estimate Std. Error t-value Pr(|t|) log(pcap) -0. 0. -0.0854 0. log(pc) 0. 0. 10.2405 2.2e-16 *log(emp) 0. 0. 27.5256 2.2e-16 *unemp -0. 0. -3.5691 0. *-Signif. codes: 0 * 0.001 * 0.01 * 0.05 . 0.1 15、空间面板极大似然估计，命令spmlfm- log(gsp)log(pcap)+log(pc)+log(emp)+unemp# fixed effects panel with spatial errorsFespaterr fm Fespaterr summary(Fespaterr)Spatial panel fixed effects error modelCall:spml(formula = fm, data = Produc, listw = mat2listw(usaww), model = within, spatial.error = b, hess = FALSE)Residuals: Min. 1st Qu. Median 3rd Qu. Max. -0.1250 -0.0238 -0.0035 0.0171 0.1880 Coefficients: Estimate Std. Error t-value Pr(|t|) rho 0. 0. 16.9138 2e-16 *log(pcap) 0. 0. 0.2052 0.83745 log(pc) 0. 0. 8.8494 2e-16 *log(emp) 0. 0. 28.0638 2e-16 *unemp -0. 0. -2.0788 0.03764 * -Signif. codes: 0 * 0.001 * 0.01 * 0.05 . 0.1 1#random effects panal with spatial lagRespatlag Respatlag summary(Respatlag)Spatial panel random effects ML modelCall:spreml(formula = formula, data = data, index = index, w = listw2mat(listw), w2 = listw2mat(listw2), lag = lag, errors = errors, cl = cl)Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. 1.38 1.57 1.70 1.70 1.80 2.13 Error variance parameters: Estimate Std. Error t-value Pr(|t|) phi 21.3175 8.3017 2.5678 0.01023 *Spatial autoregressive coefficient: Estimate Std. Error t-value Pr(|t|) lambda 0. 0. 5.554 2.793e-08 *Coefficients: Estimate Std. Error t-value Pr(|t|) (Intercept) 1. 0. 11.0016 2.2e-16 *log(pcap) 0. 0. 0.5190 0.6037 log(pc) 0. 0. 10.4258 2.2e-16 *log(emp) 0. 0. 25.3892 2.2e-16 *unemp -0. 0. -6.5009 7.984e-11 *-Signif. codes: 0 * 0.001 * 0.01 * 0.05 . 0.1 16、伴有随机效应和序列误差相关的空间面板模型的极大似然估计，命令speml#random effects panel with spatial lag and serial error correlation#optimization method set to “BFGS“Sarsrmod Sarsrmod summary(Sarsrmod)Spatial panel random effects ML modelCall:spreml(formula = fm, data = Produc, w = usaww, lag = TRUE, errors = sr, method = BFGS)Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. 2.66 3.02 3.18 3.18 3.31 3.77 Error variance parameters: Estimate Std. Error t-value Pr(|t|) psi 0. 0. 1214.1 |t|) lambda 0. 0. 9.973 |t|) (Intercept) 1. 0. 5.4295 5.652e-08 *log(pcap) 0. 0. 2.2829 0.02244 * log(pc) 0. 0. 0.7636 0.44510 log(emp) 0. 0. 25.1801 bsjktest(fm, data=Produc, listw = usaww, test=C.1)Baltagi, Song, Jung and Koh C.1 conditional testdata: log(gsp) log(pcap) + log(pc) + log(emp) + unempLM = 0.2617, df = 1, p-value = 0.609alternative hypothesis: spatial dependence in error terms, sub RE and serial corr.（2）bsktest，Baltigi,Song and Koh LM test for spatial panels bsktest(fm,data=Produc, listw = mat2listw(usaww),+ test=LM1, standardize=TRUE)Baltagi, Song and Koh SLM1 marginal testdata: log(gsp) log(pcap) + log(pc) + log(emp) + unempSLM1 = 0.083, p-value = 0.9338alternative hypothesis: Random effects（3）Covariance extractor method for splm objects sarremod library(lmtest) coeftest(sarremod)z test of coefficients: Estimate Std. Error z value Pr(|z|) (Intercept) 1. 0. 11.0016 2.2e-16 *log(pcap) 0. 0. 0.5190 0.6037 log(pc) 0. 0. 10.4258 2.2e-16 *log(emp) 0. 0. 25.3892 library(car) lht(sarremod,log(pcap)=log(pc)Linear hypothesis testHypothesis:log(pcap) - log(pc) = 0Model 1: restricted modelModel 2: function (x, .) UseMethod(formula) Df Chisq Pr(Chisq) 1 2 1 36.268 1.719e-09 *-Signif. codes: 0 * 0.001 * 0.01 * 0.05 . 0.1 1(更多详情请查看splm的help文档以及文后列的参考文献) 参考文献1.Baltagi,B.H.,Song,S.H.,Jung B. and Koh, W.(2007) Testing panel data regression models with spatial and serial error correlation. Journal of Econometrics,140,5-512.Baltagi,B.H.,Song,S.H and Koh, W.(2003) Testing panel data regression models with spatial error correlation. Journal of Econometrics,117,123-1503.Millo,G.,Piras,G.(2012) splm:Spatial Panel Data Models in R. Journal of Statistical Software,47(1),1-38.URL /v47/i01/4.Elhorst, J.P (2003) Specification and estimation of spatial panel data models, International Regional Science Review,26,pages 244-2685.Elhorst, J.P(2009) Spatial panel data models, In Fisher,M.M. and Getis,A.(eds),Handbook of Applied Spatial Analysis Springer,Berlin6.Giovanni Millo and Gaetano Carmeci,(2011) “Non-life insurance consumption in Italy: a subregional panel data analysis”, Journal of Geographical Systems,13:273-2987.Qu Feng and William C. Horrace,(2012)”Alternative Measures of Technical Efficiency :Skew,Bias and Scale”, Journal of Applied Econometrics, Forthcoming.8.Kapoor,M.,Kelejian,H.H. and Prucha,I.R.(2007) Panel data model with spatially correlated error components,Journal of Econometrics,140,pages 97-1309.Mutl,J.,and Pfaffermayr,M.(2011) The H