RELIABLE INFERENCE FOR GMM ESTIMATORS? FINITE SAMPLE PROPERTIES OF ALTERNATIVE TEST PROCEDURES IN LINEAR PANEL DATA MODELS

DOI10.1081/ETC-200049126zbMath1061.62023OpenAlexW1970176361WikidataQ63353175 ScholiaQ63353175MaRDI QIDQ4678782

Stephen D. Bond, Frank A. G. Windmeijer

Publication date: 23 May 2005

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

Full work available at URL: https://doi.org/10.1081/etc-200049126

zbMATH Keywords

hypothesis testinggeneralized method of momentsfinite sample inference

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Parametric hypothesis testing (62F03) Bootstrap, jackknife and other resampling methods (62F40) Monte Carlo methods (65C05) Asymptotic properties of parametric tests (62F05)

Related Items (15)

A joint serial correlation test for linear panel data models ⋮ IDENTIFICATION ROBUST INFERENCE FOR MOMENTS-BASED ANALYSIS OF LINEAR DYNAMIC PANEL DATA MODELS ⋮ Finite-sample corrected inference for two-step GMM in time series ⋮ The weak instrument problem of the system GMM estimator in dynamic panel data models ⋮ On the performance of block-bootstrap continuously updated GMM for a class of non-linear conditional moment models. Moving block bootstrap inference under weak identification ⋮ A doubly corrected robust variance estimator for linear GMM ⋮ Simple and trustworthy cluster-robust GMM inference ⋮ Asymptotic refinements of a misspecification-robust bootstrap for generalized method of moments estimators ⋮ Asymptotic refinements of a misspecification-robust bootstrap for GEL estimators ⋮ Simplified estimation and testing in unbalanced repeated measures designs ⋮ THE ASYMPTOTIC PROPERTIES OF THE SYSTEM GMM ESTIMATOR IN DYNAMIC PANEL DATA MODELS WHEN BOTH N AND T ARE LARGE ⋮ GMM inference in spatial autoregressive models ⋮ Testing initial conditions in dynamic panel data models ⋮ Finite sample properties of the GMM Anderson–Rubin test ⋮ Robust standard errors in transformed likelihood estimation of dynamic panel data models with cross-sectional heteroskedasticity

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