从固定效应空间面板模型估计结果中提取AIC
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我正在使用Millo和Piras（2012）的“splm”软件包来估算具有国家（个人）固定效应的SPatial Durbin模型。
ks = log(spa.sak$index_ret+2) ~ log(spa.sak$lagindex+2) + log(spa.sak$cred_def_rate+2) + log(spa.sak$ch_in_ex_rate+2) +
log(spa.sak$un_inf+2) + log(spa.sak$gdp_growth+2) + log(spa.sak$cha_in_int_rate+2) + country
ERVM<- spml(formula = ks, data = spa.sak, index = c('id','months'), listw = ERV, model = "within", lag = TRUE, effect = "individual", spatial.error = "none")
> summary(ERVM)
Spatial panel fixed effects lag model
Call:
spml(formula = ks, data = spa.sak, index = c("id", "months"),
listw = ERV, model = "within", effect = "individual", lag = TRUE,
spatial.error = "none")
Residuals:
Min. 1st Qu. Median 3rd Qu. Max.
-0.783343 -0.028334 -0.003056 0.020040 1.199906
Spatial autoregressive coefficient:
Estimate Std. Error t-value Pr(>|t|)
lambda -0.0040442 0.0061785 -0.6546 0.5127
Coefficients:
Estimate Std. Error t-value Pr(>|t|)
log(spa.sak$lagindex + 2) -2.5577e-01 1.6593e-02 -15.4142 < 2.2e-16 ***
log(spa.sak$cred_def_rate + 2) 1.3427e-02 2.3244e-02 0.5777 0.5635
log(spa.sak$ch_in_ex_rate + 2) -5.1946e-04 2.2338e-03 -0.2325 0.8161
log(spa.sak$un_inf + 2) 1.1360e-01 2.2112e-01 0.5138 0.6074
log(spa.sak$gdp_growth + 2) 3.1093e+00 2.1339e+00 1.4571 0.1451
log(spa.sak$cha_in_int_rate + 2) -1.4148e+01 3.1507e+00 -4.4905 7.105e-06 ***
countryEGYPT 1.0659e-02 1.7940e-02 0.5942 0.5524
countryGHANA 2.3214e-02 2.0567e-02 1.1287 0.2590
countryKENYA 1.6923e-02 1.9599e-02 0.8634 0.3879
countryMALAWI 1.0190e-02 3.2028e-02 0.3182 0.7504
countryMAURITIUS 2.5843e-03 1.3530e-02 0.1910 0.8485
countryMOROCCO 4.2243e-03 1.4848e-02 0.2845 0.7760
countryNAMIBIA -1.0416e-02 1.4408e-02 -0.7229 0.4697
countryNIGERIA 1.2794e-02 1.8327e-02 0.6981 0.4851
countrySOUTH AFRICA -9.4671e-04 1.3665e-02 -0.0693 0.9448
countryTANZANIA 1.9698e-02 1.9822e-02 0.9937 0.3204
countryTUNISIA 1.4579e-02 1.4895e-02 0.9788 0.3277
countryUGANDA 3.1850e-02 2.1042e-02 1.5137 0.1301
countryZAMBIA 3.3671e-02 2.1202e-02 1.5881 0.1123
countryZIMBABWE 1.4299e-02 3.1798e-02 0.4497 0.6529
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> str(ERVM)
List of 15
$ coefficients : Named num [1:21] -0.004044 -0.255767 0.013427 -0.000519 0.113601 ...
..- attr(*, "names")= chr [1:21] "lambda" "log(spa.sak$lagindex + 2)" "log(spa.sak$cred_def_rate + 2)" "log(spa.sak$ch_in_ex_rate + 2)" ...
$ errcomp : NULL
$ vcov : num [1:21, 1:21] 3.82e-05 0.00 0.00 0.00 0.00 ...
$ spat.coef : Named num -0.00404
..- attr(*, "names")= chr "lambda"
$ vcov.errcomp : NULL
$ residuals : Named num [1:3390] -0.1195 0.0395 0.0151 -0.0186 0.0285 ...
..- attr(*, "names")= chr [1:3390] "5-01/01/2000" "5-01/01/2001" "5-01/01/2002" "5-01/01/2003" ...
$ fitted.values: num [1:3390, 1] 0.681 0.712 0.704 0.725 0.719 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:3390] "5-01/01/2000" "5-01/01/2001" "5-01/01/2002" "5-01/01/2003" ...
.. ..$ : NULL
..- attr(*, "names")= chr [1:3390] "5-01/01/2000" "5-01/01/2001" "5-01/01/2002" "5-01/01/2003" ...
$ sigma2 : num 0.0192
$ type : chr "fixed effects lag"
$ model :'data.frame': 3390 obs. of 21 variables:
..$ y : num [1:3390] 0.537 0.731 0.697 0.685 0.728 ...
..$ log.spa.sak.lagindex...2. : num [1:3390] 0.851 0.71 0.752 0.674 0.731 ...
..$ log.spa.sak.cred_def_rate...2. : num [1:3390] 2.83 2.83 2.83 2.83 2.83 ...
..$ log.spa.sak.ch_in_ex_rate...2. : num [1:3390] 0.696 0.69 0.711 0.768 0.695 ...
..$ log.spa.sak.un_inf...2. : num [1:3390] 0.697 0.694 0.693 0.692 0.694 ...
..$ log.spa.sak.gdp_growth...2. : num [1:3390] 0.693 0.693 0.693 0.693 0.693 ...
..$ log.spa.sak.cha_in_int_rate...2.: num [1:3390] 0.693 0.693 0.693 0.693 0.693 ...
..$ countryEGYPT : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countryGHANA : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countryKENYA : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countryMALAWI : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countryMAURITIUS : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countryMOROCCO : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countryNAMIBIA : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countryNIGERIA : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countrySOUTH.AFRICA : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countryTANZANIA : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countryTUNISIA : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countryUGANDA : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countryZAMBIA : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
..$ countryZIMBABWE : num [1:3390] 0 0 0 0 0 0 0 0 0 0 ...
$ call : language spml(formula = ks, data = spa.sak, index = c("id", "months"), listw = ERV, model = "within", effect = "individual| __truncated__
$ logLik : num 1895
$ method : chr "eigen"
$ effects : chr "spfe"
$ res.eff :List of 2
..$ :List of 7
.. ..$ res.sfe : num [1:15, 1] 0.00264 -0.00152 0.0017 0.0023 0.0106 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:15] "1" "2" "3" "4" ...
.. .. .. ..$ : NULL
.. ..$ res.se.sfe: Named num [1:15] 2.55 2.55 2.55 2.55 2.55 ...
.. .. ..- attr(*, "names")= chr [1:15] "1" "2" "3" "4" ...
.. ..$ intercept : num [1, 1] 8.43
.. ..$ res.se.con: num [1, 1] 2.55
.. ..$ xhat : num [1:3390, 1] 0.681 0.712 0.704 0.725 0.719 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3390] "5-01/01/2000" "5-01/01/2001" "5-01/01/2002" "5-01/01/2003" ...
.. .. .. ..$ : NULL
.. ..$ N.vars : int 35
.. ..$ res.e : num [1:3390, 1] -0.1195 0.0395 0.0151 -0.0186 0.0285 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3390] "5-01/01/2000" "5-01/01/2001" "5-01/01/2002" "5-01/01/2003" ...
.. .. .. ..$ : NULL
..$ res.corr: NULL
- attr(*, "class")= chr "splm"
除了以下两个，我成功获得估计结果。
1）如上所示，我只能得到lambda和eta系数的值。我无法得到 heta估计结果或与空间滞后解释变量相关的值，这些变量显示了时空关系。如何提取它？
2）我无法提取AIC和BIC进行模型比较。在第一种情况下，我的试验没有成功，而对于第二种情况，我试过了
AICsplm(ERVMX, criterion="AIC")
但是我收到了一个错误。
我该如何解决这些问题？
更新：我已经解决了这个问题，请参阅下面的内容。
答案
提前构建响应和预测变量的空间滞后，并添加到公式并运行。
就AIC而言，需要从github download AICsplm.R下载AICsplm.R来运行代码。
source(AICsplm.R)
AICsplm(ERVM)
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