Wald, QLR, and score tests when parameters are subject to linear inequality constraints

This paper develops Wald-type, QLR, and score-type tests for linear equality constraints in a general class of extremum estimation problems where the parameter space is characterized by a finite number of linear equality and inequality constraints. We show that the asymptotic null distributions of t...

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Published inJournal of econometrics Vol. 235; no. 2; pp. 2005 - 2026
Main Authors Fan, Yanqin, Shi, Xuetao
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.08.2023
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Abstract This paper develops Wald-type, QLR, and score-type tests for linear equality constraints in a general class of extremum estimation problems where the parameter space is characterized by a finite number of linear equality and inequality constraints. We show that the asymptotic null distributions of the Wald and QLR statistics are discontinuous in an implicit nuisance parameter and propose an algorithm to identify it. In contrast, the asymptotic null distribution of the score statistic is not discontinuous in any model parameter but depends on a polytope projection. We present an algorithm based on the Fourier–Motzkin elimination to compute such a projection. We study the consistency and local power properties of the three tests. Finally, we present numerical results of our tests’ finite sample performance from a Monte Carlo study and conduct an empirical illustration of a Mincer earnings regression.
AbstractList This paper develops Wald-type, QLR, and score-type tests for linear equality constraints in a general class of extremum estimation problems where the parameter space is characterized by a finite number of linear equality and inequality constraints. We show that the asymptotic null distributions of the Wald and QLR statistics are discontinuous in an implicit nuisance parameter and propose an algorithm to identify it. In contrast, the asymptotic null distribution of the score statistic is not discontinuous in any model parameter but depends on a polytope projection. We present an algorithm based on the Fourier-Motzkin elimination to compute such a projection. We study the consistency and local power properties of the three tests. Finally, we present numerical results of our tests' finite sample performance from a Monte Carlo study and conduct an empirical illustration of a Mincer earnings regression.
Author Shi, Xuetao
Fan, Yanqin
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Keywords Implicit nuisance parameter
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Uniform inference
Polytope projection
Mincer earnings regression
Extremum estimation
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Bonferroni-type correction
Random coefficients model
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Snippet This paper develops Wald-type, QLR, and score-type tests for linear equality constraints in a general class of extremum estimation problems where the parameter...
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SubjectTerms algorithms
Bonferroni-type correction
class
econometrics
Extremum estimation
Implicit nuisance parameter
Mincer earnings regression
Monte Carlo method
Polytope projection
Random coefficients model
Uniform inference
Title Wald, QLR, and score tests when parameters are subject to linear inequality constraints
URI https://dx.doi.org/10.1016/j.jeconom.2023.02.009
https://www.proquest.com/docview/2888003268
Volume 235
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