Estimating the Fixed-Effect Stochastic Frontier Models with Error in Variables: A GMM Method
Date Issued
2016
Date
2016
Author(s)
ZHOU, TONG
Abstract
Since Greene (2005) introduced the true-fixed effect stochastic frontier (TFESF) model, its estimation method has been gaining attention due to the complication from the heterogeneous effects (incidental parameters problem) in the microeconometric analysis. Traditional MLE methods have trouble dealing with it because of its inherently complex likelihood functions. The estimation also deteriorates into serious bias when measurement error problem arises for fixed effect panel data models. This paper proposes a two-step estimation strategy to address the two aforementioned problems. In the first step, we extend Hong and Tamer(2003) to obtain a consistent estimator of interest and the measurement error''s variance needed to estimate ineffciency parameters in stochastic frontier analysis in the second step. Then, we show how to extend Chen and Wang(2015) and derive the MoM estimator for TFESF models when its composite error varies in distribution. We derive closed-form estimators for two-parameter models (normal-half nor-mal or normal-exponential). Finally, simulation results indicate that our MoM estimators have good performance for finite sample sizes.
Subjects
Measurement error
Stochastic Frontier Model
Fixed Effect
Panel Data
revised Method of Moments
Type
thesis
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