Flexible Estimation of Heteroskedastic Stochastic Frontier Models via Two-step Iterative Nonlinear Least Squares

2019 ◽  
Author(s):  
Federico Belotti ◽  
Giancarlo Ferrara
Keyword(s):  
Least Squares ◽  
Stochastic Frontier ◽  
Nonlinear Least Squares ◽  
Stochastic Frontier Models

scholarly journals Accounting for Skewed or One-Sided Measurement Error in the Dependent Variable

2021 ◽  
pp. 1-23
Author(s):  
Daniel L. Millimet ◽  
Christopher F. Parmeter
Keyword(s):  
Measurement Error ◽  
Linear Regression ◽  
Least Squares ◽  
Political Science ◽  
Stochastic Frontier ◽  
Nonlinear Least Squares ◽  
Science Research ◽  
Stochastic Frontier Model ◽  
Classical Measurement ◽  
Regression Framework

Abstract While classical measurement error in the dependent variable in a linear regression framework results only in a loss of precision, nonclassical measurement error can lead to estimates, which are biased and inference which lacks power. Here, we consider a particular type of nonclassical measurement error: skewed errors. Unfortunately, skewed measurement error is likely to be a relatively common feature of many outcomes of interest in political science research. This study highlights the bias that can result even from relatively “small” amounts of skewed measurement error, particularly, if the measurement error is heteroskedastic. We also assess potential solutions to this problem, focusing on the stochastic frontier model and Nonlinear Least Squares. Simulations and three replications highlight the importance of thinking carefully about skewed measurement error as well as appropriate solutions.

scholarly journals Nonparametric least squares methods for stochastic frontier models

2016 ◽  
Vol 47 (3) ◽  
pp. 189-204 ◽  
Author(s):  
Léopold Simar ◽  
Ingrid Van Keilegom ◽  
Valentin Zelenyuk
Keyword(s):  
Least Squares ◽  
Stochastic Frontier ◽  
Stochastic Frontier Models
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