Inference about clustering and parametric assumptions in covariance matrix estimation

DOI10.1016/j.csda.2011.06.034zbMath1318.62182OpenAlexW2001655697MaRDI QIDQ429607

Tony S. Wirjanto, Mikko Packalen

Publication date: 20 June 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2011.06.034

zbMATH Keywords

powersizecovariance matrix estimatorcluster-robustfinite samplesheteroskedasticity-robust

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Measures of association (correlation, canonical correlation, etc.) (62H20) Point estimation (62F10)

Related Items (1)

The structure of autocovariance matrix of discrete time subfractional Brownian motion

Cites Work

• A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
• Asymptotic properties of a robust variance matrix estimator for panel data when \(T\) is large
• Robust tests for heteroskedasticity in the one-way error components model
• On the application of robust, regression-based diagnostics to models of conditional means and conditional variances
• Testing for heteroskedasticity and serial correlation in a random effects panel data model
• How Much Should We Trust Differences-In-Differences Estimates?
• The Finite-Sample Performance of White's Test for Heteroskedasticity Under Stochastic Regressors
• Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression
• Maximum Likelihood Estimation of Misspecified Models

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