Stochastic production function estimation: small sample properties of ML versus FGLS

Just-Pope production functions have been traditionally estimated by feasible generalized least squares (FGLS). This paper investigates the small-sample properties of FGLS and maximum likelihood (ML) estimators in heteroscedastic error models. Monte Carlo experiment results show that in small samples, even when the error distribution departs significantly from normality, the ML estimator is more efficient and suffers from less bias than FGLS. Importantly, FGLS was found to seriously understate the risk effects of inputs and provide biased marginal product estimates. These results are explained by showing that the FGLS criteria being optimized at the multiple stages are not logically consistent.

[1]  Steven T. Buccola,et al.  Joint Risk Preference-Technology Estimation with a Primal System , 1991 .

[2]  A. Hallam,et al.  Stochastic technology in a programming framework: a generalised mean variance farm model. , 1990 .

[3]  Brian C. D'Silva,et al.  Measuring stochastic technology for the multi-product firm: The irrigated farms of Sudan , 1989 .

[4]  B. McCarl,et al.  Small-Sample Evaluation of Mean-Variance Production Function Estimators , 1986 .

[5]  P. Hazell.  Sources of Increased Instability in Indian and U.S. Cereal Production , 1984 .

[6]  B. McCarl,et al.  Influence of hatchery smolt releases on adult salmon production and its variability , 1983 .

[7]  William E. Griffiths,et al.  Using Time-Series and Cross-Section Data to Estimate a Production Function with Positive and Negative Marginal Risks , 1982 .

[8]  H. White.  A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity , 1980 .

[9]  Wayne A. Fuller,et al.  Least Squares Estimation When the Covariance Matrix and Parameter Vector are Functionally Related , 1980 .

[10]  R. Just,et al.  Production Function Estimation and Related Risk Considerations , 1979 .

[11]  J. Magnus.  Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix , 1978 .

[12]  R. Just,et al.  Stochastic specification of production functions and economic implications , 1978 .

[13]  A. Harvey.  Estimating Regression Models with Multiplicative Heteroscedasticity , 1976 .

[14]  Jan Kmenta,et al.  A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models , 1974 .

[15]  C. Hildreth,et al.  Some Estimators for a Linear Model With Random Coefficients , 1968 .

[16]  Sabul C. Kumbhakar.  Production risk, technical efficiency, and panel data , 1993 .

[17]  D. Byerlee,et al.  RELATIVE VARIABILITY IN WHEAT YIELDS ACROSS COUNTRIES AND OVER TIME , 1990 .

[18]  Jock R. Anderson,et al.  Estimating risk effects in Chinese foodgrain production. , 1990 .

[19]  N. Kiefer,et al.  Testing Normality in Econometric Models , 1982 .

[20]  W. E. Taylor.  Small Sample Properties of a Class of Two Stage Aitken Estimators , 1977 .