Stochastic production function estimation: small sample properties of ML versus FGLS
- 1 April 1997
- journal article
- research article
- Published by Taylor & Francis in Applied Economics
- Vol. 29 (4) , 459-469
- https://doi.org/10.1080/000368497326958
Abstract
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.This publication has 20 references indexed in Scilit:
- STOCHASTIC TECHNOLOGY IN A PROGRAMMING FRAMEWORK: A GENERALISED MEAN-VARIANCE FARM MODELJournal of Agricultural Economics, 1990
- Measuring Stochastic Technology for the Multi‐product Firm: The Irrigated Farms of SudanCanadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, 1989
- Small‐Sample Evaluation of Mean‐Variance Production Function EstimatorsAmerican Journal of Agricultural Economics, 1986
- Sources of Increased Instability in Indian and U.S. Cereal ProductionAmerican Journal of Agricultural Economics, 1984
- Using Time-Series and Cross-Section Data to Estimate a Production Function with Positive and Negative Marginal RisksJournal of the American Statistical Association, 1982
- Least Squares Estimation When the Covariance Matrix and Parameter Vector are Functionally RelatedJournal of the American Statistical Association, 1980
- Production Function Estimation and Related Risk ConsiderationsAmerican Journal of Agricultural Economics, 1979
- Stochastic specification of production functions and economic implicationsJournal of Econometrics, 1978
- Estimating Regression Models with Multiplicative HeteroscedasticityEconometrica, 1976
- Some Estimators for a Linear Model With Random CoefficientsJournal of the American Statistical Association, 1968