I am using the package 'splm' of Millo and Piras(2012) to estimate a SPatial Durbin model with country (individual) fixed effect.
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"
I am successful to obtain estimation results except the following two.
1) As it is shown above, I am only able to get values for \lambda and \beta coefficients. I can not get the \theta estimation result or value that relates with the spatially lagged explanatory variables which shows the spatio-temporal relationships. How can extract it?
2) I am not able to extract AIC and BIC for model comparison. In the first case, my trials weren't successful, and For the second case, I have tried
AICsplm(ERVMX, criterion="AIC")
But I got an error.
How can I fix these issues?
UPDATE: I have solved the issue and please refer below.
Construct the spatial lag for both response and predictor variable in advance and add in to the formula and run.
As far as AIC is concerned, there is a need to download AICsplm.R from github download AICsplm.R to run the code.
source(AICsplm.R)
AICsplm(ERVM)