summary {ssfa} | R Documentation |
SSFA summaries
Description
The function print.ssfa
is used to display the values of SFA and SSFA estimated coefficients. In particular:
- for SFA the function displays the Intercept
, the regressors beta coefficients, the inefficiency variance sigmau2
, the stochastic error variance sigmav2
and the total variance sigma2
;
- for SSFA the function displays, in addition, the decomposition of the inefficiency variance into sigmau2_dmu
and sigmau2_sar
, respectively the part of inefficiency variance due to DMU's specificities and to the spatial dependence, and finally, the spatial lag parameter rho
.
The function summary.ssfa
is used to display the summary results of SFA and SSFA. In particular:
- for SFA the summary shows the estimation of SFA coefficients (Intercept
, beta coefficients, sigmau2
and sigmav2
) and others useful information as the total variance sigma2
, the inefficiency parameter Lambda
(sigmau/sigmav)
, the Moran I
statistic, the mean of efficiency, the LR-test and the AIC values;
- for SSFA the summary shows, in addition, the decomposition of the inefficiency variance into sigmau2_dmu
and sigmau2_sar
and the spatial lag parameter rho
.
Usage
## S3 method for class 'ssfa'
print(x, ...)
## S3 method for class 'ssfa'
summary(object, ...)
Arguments
x |
an object of class |
object |
an object of class |
... |
further arguments for methods. |
Note
Please note that the classical SFA inefficiency variance sigmau2
, in the SSFA, is decomposed into sigmau2_dmu
and sigmau2_sar
, respectively the part of inefficiency variance due to DMU's specificities and to the spatial dependence, i.e. sigmau2 = sigmau2_dmu + sigmau2_sar
and consequently the total variance is given by sigma2 = sigmau2_dmu + sigmau2_sar + sigmav2
.
References
Anselin, L. (1995). Local indicators of spatial association, Geographical Analysis, 27, 93-115.
Fusco, E. and Vidoli, F. (2013). Spatial stochastic frontier models: controlling spatial global and local heterogeneity, International Review of Applied Economics, 27(5) 679-694.
Fusco, E. (2020). Spatial Dependence in Efficiency Parametric Models: A Generalization and Simulation Studies, "Scienze Regionali, Italian Journal of Regional Science" Speciale/2021, 595-618.
Kumbhakar, S. C., and C. A. K. Lovell (2000). Stochastic Frontier Analysis, Cambridge University Press.
Examples
library(ssfa)
data(SSFA_example_data)
data(Italian_W)
ssfa <- ssfa(log_y ~ log_x, data = SSFA_example_data,
data_w=Italian_W, form = "production", par_rho=TRUE)
print(ssfa)
summary(ssfa)