Analyzing repeated-game economics experiments: robust standard errors for panel data with serial correlation
AbstractThe purpose of this study is to provide guidance to those who analyze data from repeated-game experiments. In particular, I propose the use of heteroskedasticity-autocorrelation consistent (HAC) covariance estimators for panel data, which allows researchers to conduct hypothesis tests without having to place structure on the heteroskedasticity and/or serial correlation likely present in econometric models. Through Monte Carlo experiments I explore the properties of three panel HAC covariance estimators within a linear regression framework, including a new HAC covariance estimator proposed in this study, for a range of cross-section (
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 38862.
Date of creation: Jan 2009
Date of revision:
applied econometrics; laboratory experiments; monte carlo simulations; robust inference;
Find related papers by JEL classification:
- C9 - Mathematical and Quantitative Methods - - Design of Experiments
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
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