DYNAMIC LINEAR PANEL REGRESSION MODELS WITH INTERACTIVE FIXED EFFECTS

By H. R. Moon, Martin P. Weidner. Econometric Theory, 2017

192 citations recorded by the source corpus.

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 χ2-distribution. Monte Carlo simulations show the bias correction of the LS estimator and of the test statistics also work well for finite sample sizes.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/2c08bbd8f8ed7e57238b84be77f24d60be50d202

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Citations in the corpus

Estimation of random coefficients logit demand models with interactive fixed effects (2012, background): “The result that choosing REST > R has only a small effect on the estimator is not covered by the asymptotic theory in this paper, where we assume REST = R, but is consistent with the analytical results found in Moon and Weidner (2013b) for the linear model with interactive fixed effects.”
Shrinkage Estimation of Dynamic Panel Data Models with Interactive Fixed Effects (2016, unknown): “Using 0 = − 0 by Assumptions A.1(ii)-(iii), A.4(iii), S.1(i)-(ii), and A.6(i), we have I1 ≤ 0 °°°¡000¢−1 ¡ 00 0¢−1°°°2 X =1 °° 00ε00Xε00°°2sp ≤ 0 °°°¡000¢−1 ¡ 00 0¢−1°°°2 0 ( )2 X =1 °° 00ε0Xε00°°2sp + 0 °°°¡000¢−1 ¡ 00 0¢−1°°°2 X =1 °° 00ε00Xε00°°2sp = 0 °°°¡000¢−1 ¡ 00 0¢−1°°°2 X =1 °° 00ε0X”
NBER WORKING PAPER SERIES MATRIX COMPLETION METHODS FOR CAUSAL PANEL DATA MODELS (2018, unknown): “In the modern part of this literature (Bai (2003, 2009); Pesaran (2006); Moon and Weidner (2015, 2017); Bai and Ng (2017)) researchers allow for more complex asymptotics with both N and T increasing, at rates that allow for consistent estimation of the factors V and loadings U after imposing…”
Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure (2019, unknown): “Also note that Moon and Weidner (2017) report additional bias term that generalizes the Nickell bias which typically occurs in dynamic panel models with fixed effects.”
Approximate factor models with weaker loadings (2021, background): “Moon, R., and M. Weidner (2017): “Dynamic Linear Panel Regression Models with Interactive Fixed Effects,” Econometric Theory, 33, 158–195.”
Common correlated effects estimation of heterogeneous dynamic panel quantile regression models (2020, unknown): “Recent papers have focused on the estimation of models with a fixed number of unobserved factors (see e.g. Pesaran (2006), Bai (2009), Pesaran and Chudik (2014), Moon and Weidner (2015, 2017), Chudik and Pesaran (2015)).”
THE ASYMPTOTIC PROPERTIES OF THE SYSTEM GMM ESTIMATOR IN DYNAMIC PANEL DATA MODELS WHEN BOTH N AND T ARE LARGE (2015, background): “These include Phillips and Sul (2003, 2007), Sarafidis and Robertson (2009), Sarafidis, Yamagata and Robertson (2009), Robertson, Sarafidis, and Symons (2010), Sarafidis and Yamagata (2010), Moon and Weidner (2010), and Hayakawa (2012).”
Grouped Patterns of Heterogeneity in Panel Data (2015, methodology): “where we have used that the sum of the left-hand sides in (50) and (51) is zero.”
Fixed Effects Estimation of Large-TPanel Data Models (2018, methodology): “The above formulas for asymptotic bias and variance thus contain those in Bai (2009) and Moon & Weidner (2017) as special cases.10 However, these results rely on an auxiliary consistency proof, which is usually model specific and therefore can be delicate.”
On factor models with random missing: EM estimation, inference, and cross validation (2020, background): “Hallin and Líska (2007), Onatski (2009, 2010, 2012), and Ahn and Horenstein (2013), among others.”