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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Vydáno v:Journal of econometrics Ročník 235; číslo 2; s. 2005 - 2026
Hlavní autoři: Fan, Yanqin, Shi, Xuetao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.08.2023
Témata:
algorithms
Bonferroni-type correction
class
econometrics
Extremum estimation
Implicit nuisance parameter
Mincer earnings regression
Monte Carlo method
Polytope projection
Random coefficients model
Uniform inference
Implicit nuisance parameter
C12
Uniform inference
Polytope projection
Mincer earnings regression
Extremum estimation
C18
Bonferroni-type correction
Random coefficients model
ISSN:0304-4076, 1872-6895
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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content type line 23
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2023.02.009