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R语言学习:如何构建空间回归模型?

momo  · 知乎专栏  ·  · 2023-10-31 20:39
文献来源Prokopczuk, M., et al. (2023). Convenience yield riskAppendix A. Supplementary data【数据+Stata+Python+Matlab】示例代码library(plm) library(splm) library(spdep) setwd("C:\\Download\\1-s2.0-S0140988323000348-mmc2\\code_without_data\\helpers\\paneldata\\numericalchecks") W = read.table("MunnellW.txt") W = as.matrix(W) lw = mat2listw(W) fm <- log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp # SARAR FE sararfe <- spgm(fm, data=dataMunnell, listw=lw, lag=TRUE, spatial.error=TRUE, model="within", method="w2sls") summary(sararfe) # spatial coefficient (rho) and t statistic rhofe <- sararfe$rho[1] rhofe_t <- sararfe$rho[1] / sqrt(sararfe$rho[2]) print(rhofe) print(rhofe_t) # SARAR RE sararre <- spgm(fm, data=dataMunnell, listw=lw, lag=TRUE, spatial.error=TRUE, model="random", method="g2sls") summary(sararre) # spatial coefficient (rho) and t statistic rhore <- sararre$rho[1] rhore_t <- sararre$rho[1] / sqrt(sararre$rho[2]) print(rhore) print(rh ………………………………

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