Maximum likelihood estimation of a stochastic frontier model with residual covariance Reportar como inadecuado




Maximum likelihood estimation of a stochastic frontier model with residual covariance - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Abstract

In theoretical literature on productivity, the disturbance terms of the stochastic frontier model are assumed to be independent random variables. In this paper, we consider a stochastic production frontier model with residuals that are both spatially and time-wise correlated. We introduce generalizations of the Maximum Likelihood Estimation procedure suggested in Cliff and Ord 1973 and Kapoor 2003. We assume the usual error component specification, but allow for possible correlation between individual specific errors components. The model combines specifications usually considered in the spatial literature with those in the error components literature. Our specifications are such that the model’s disturbances are potentially spatially correlated due to geographical or economic activity. For instance, for agricultural farmers, spatial correlations can represent productivity shock spillovers, based on geographical proximity and weather. These spillovers effect estimation of efficiency.



Item Type: MPRA Paper -

Original Title: Maximum likelihood estimation of a stochastic frontier model with residual covariance-

English Title: Maximum likelihood estimation of a stochastic frontier model with residual covariance-

Language: English-

Keywords: spatial stochastic production frontier models, correlated errors-

Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal ModelsC - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C24 - Truncated and Censored Models ; Switching Regression Models ; Threshold Regression ModelsC - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C21 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions-





Autor: Simwaka, Kisu

Fuente: https://mpra.ub.uni-muenchen.de/39726/







Documentos relacionados