df_fd <- data.frame(firm = NOTp$States, year = NOTp$Year, d_logy = diff(NOTp$logy), d_logk_hat = diff(NOTp$logk_hat), d_logl = diff(NOTp$logl))
View(df_fd)
df_fd<-na.omit(df_fd)
sfa_fd <- sfa(d_logy ~ d_logk_hat + d_logl, data = df_fd)
summary(sfa_fd, extraPar = TRUE)
Error Components Frontier (see Battese & Coelli 1992)
Inefficiency decreases the endogenous variable (as in a production function)
The dependent variable is logged
Iterative ML estimation terminated after 7 iterations:
log likelihood values and parameters of two successive iterations
are within the tolerance limit
final maximum likelihood estimates
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.235760 0.083541 2.8221 0.0047711 **
d_logk_hat 0.235319 0.124720 1.8868 0.0591901 .
d_logl 0.561305 0.135936 4.1292 3.640e-05 ***
sigmaSq 0.119857 0.034466 3.4775 0.0005061 ***
gamma 0.433820 0.272528 1.5918 0.1114218
sigmaSqU 0.051996 0.046313 1.1227 0.2615610
sigmaSqV 0.067860 0.018240 3.7204 0.0001989 ***
sigma 0.346203 0.049778 6.9550 3.526e-12 ***
sigmaU 0.228027 0.101552 2.2454 0.0247413 *
sigmaV 0.260500 0.035010 7.4408 1.001e-13 ***
lambdaSq 0.766223 0.850163 0.9013 0.3674473
lambda 0.875341 0.485618 1.8025 0.0714620 .
varU 0.018894 NA NA NA
sdU 0.137457 NA NA NA
gammaVar 0.217791 NA NA NA
Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
log likelihood value: -20.36822
cross-sectional data
total number of observations = 104
mean efficiency: 0.8421357