Dynamic Linear Panel Regression Models with Interactive Fixed Effects
Hyungsik Roger Moon, Martin Weidner
TLDR
This paper analyzes and bias-corrects LS estimators and test statistics for dynamic linear panel regression with interactive fixed effects.
Key contributions
- Analyzes LS estimator in dynamic linear panel regression with interactive fixed effects.
- Identifies two sources of asymptotic bias: error correlation/heteroscedasticity and predetermined regressors.
- Provides a bias-corrected LS estimator and corrected Wald, LR, and LM test statistics.
- Demonstrates asymptotic chi-squared distribution for corrected tests and good finite sample performance.
Why it matters
This paper addresses a critical issue in econometric modeling: biased estimators in dynamic panel data. By providing bias-corrected methods, it improves the reliability of statistical inference. This is crucial for accurate economic and social science research using panel data.
Original Abstract
We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example lagged-dependent variables. The first-order asymptotic theory of the least squares (LS) estimator of the regression coefficients is worked out in the limit where both the cross-sectional dimension and the number of time periods become large. We find two sources of asymptotic bias of the LS estimator: bias due to correlation or heteroscedasticity of the idiosyncratic error term, and bias due to predetermined (as opposed to strictly exogenous) regressors. We provide a bias-corrected LS estimator. We also present bias-corrected versions of the three classical test statistics (Wald, LR, and LM test) and show their asymptotic distribution is a chi-squared distribution. Monte Carlo simulations show the bias correction of the LS estimator and of the test statistics also work well for finite sample sizes.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.