Maximum likelihood estimation of seemingly unrelated stochastic frontier regressions

In this paper, we propose the copula-based maximum likelihood (ML) approach to estimate the multiple stochastic frontier (SF) models with correlated composite errors. The motivation behind the extension to system of SF regressions is analogous to the classical generalization to system of seemingly u...

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Bibliographic Details
Published in:Journal of productivity analysis 2013-08, Vol.40 (1), p.1-14
Main Authors: Lai, Hung-pin, Huang, Cliff J.
Format: Article
Language:English
Subjects:
Accounting/Auditing
Consistent estimators
Copula functions
Correlation coefficients
Cumulative distribution functions
Econometrics
Economic models
Economic statistics
Economic theory
Economics
Economics and Finance
Estimating techniques
Estimation bias
Estimation methods
Estimators
Hospitality industry
Hotels & motels
Maximum likelihood estimation
Maximum likelihood method
Microeconomics
Monte Carlo simulation
Noise
Operations Research/Decision Theory
Random variables
Restaurants
Stochastic models
Studies
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Summary:In this paper, we propose the copula-based maximum likelihood (ML) approach to estimate the multiple stochastic frontier (SF) models with correlated composite errors. The motivation behind the extension to system of SF regressions is analogous to the classical generalization to system of seemingly unrelated regressions (Zellner in J Am Statist Assoc 57:348-368, 1962). A demonstration of the copula approach is provided via the analysis of a system of two SF regressions. The consequences of ignoring the correlation between the composite errors are examined by a Monte Carlo experiment. Our findings suggest that the stronger the correlation between the two SF regressions, the more estimation efficiency is lost in separate estimations. Estimation without considering the correlated composite errors may cause significantly efficiency loss in terms of mean squared errors in estimation of the SF technical efficiency. Finally, we also conduct an empirical study based on Taiwan hotel industry data, focusing on the SF regressions for the accommodation and restaurant divisions. Our results, which are consistent with the findings in simulation, show that joint estimation is significantly different from separate estimation without considering the correlated composite errors in the two divisions.
ISSN:0895-562X
1573-0441
DOI:10.1007/s11123-012-0289-8