An introduction to econometric applications of empirical process theory for dependent random variables

DOI10.1080/07474939308800261zbMath0802.62099OpenAlexW2021139296MaRDI QIDQ4694423

Donald W. K. Andrews

Publication date: 11 August 1993

Published in: Econometric Reviews (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07474939308800261

zbMATH Keywords

nuisance parameterdependent random variablessemi-parametric estimationstochastic equicontinuitybracketing functional limit resultsempirical process-type functional limit theorypreliminary infinite- dimensional nuisance parameter

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Order statistics; empirical distribution functions (62G30) Functional limit theorems; invariance principles (60F17)

Related Items (13)

Bootstrap conditional distribution tests in the presence of dynamic misspecification ⋮ Boundedly pivotal structural change tests in continuous updating GMM with strong, weak identification and completely unidentified cases ⋮ Evaluating forecast performance with state dependence ⋮ Inference of local regression in the presence of nuisance parameters ⋮ GENERAL TRIMMED ESTIMATION: ROBUST APPROACH TO NONLINEAR AND LIMITED DEPENDENT VARIABLE MODELS ⋮ Chi-squared tests for evaluation and comparison of asset pricing models ⋮ M-estimators with non-standard rates of convergence and weakly dependent data ⋮ Least trimmed squares in nonlinear regression under dependence ⋮ Testing for non-nested conditional moment restrictions using unconditional empirical likelihood ⋮ TESTING FOR NONNESTED CONDITIONAL MOMENT RESTRICTIONS VIA CONDITIONAL EMPIRICAL LIKELIHOOD ⋮ Testing for GARCH effects: A one-sided approach ⋮ Generalized method of trimmed moments ⋮ Bootstrap specification tests for diffusion processes

This page was built for publication: An introduction to econometric applications of empirical process theory for dependent random variables