X-DIFFERENCING AND DYNAMIC PANEL MODEL ESTIMATION

By Chirok Han, Peter C. B. Phillips, Donggyu Sul. Econometric Theory, 2013

66 citations recorded by the source corpus.

This paper introduces a new estimation method for dynamic panel models with fixed effects and AR(p) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called X-differencing, that eliminates fixed effects and retains information and signal strength in cases where there is a root at or near unity. The resulting “panel fully aggregated” estimator (PFAE) is obtained by pooled least squares on the system of X-differenced equations. The method is simple to implement, consistent for all parameter values, including unit root cases, and has strong asymptotic and finite sample performance characteristics that dominate other procedures, such as bias corrected least squares, generalized method of moments (GMM), and system GMM methods. The asymptotic theory holds as long as the cross section (n) or time series (T) sample size is large, regardless of the n/T ratio, which makes the approach appealing for practical work. In the time series AR(1) case (n = 1), the FAE estimator has a limit distribution with smaller bias and variance than the maximum likelihood estimator (MLE) when the autoregressive coefficient is at or near unity and the same limit distribution as the MLE in the stationary case, so the advantages of the approach continue to hold for fixed and even small n. Some simulation results are reported, giving comparisons with other dynamic panel estimation methods.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/c28f4e8294c2ebacaa022d5a2696fa1e6366a5b5

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In Economics and Econometrics

1. Liangjun Su
2. Kazuhiko Hayakawa
3. Zhentao Shi
4. Peter C. B. Phillips
5. Sainan Jin
6. Xun Lu
7. Yiguo Sun
8. Jiti Gao

Citations in the corpus

• BIAS REDUCTION FOR DYNAMIC NONLINEAR PANEL MODELS WITH FIXED EFFECTS (2011, unknown): “Bias correction for linear dynamic panel models was also considered by Moon and Phillips (2004), Phillips and Sul (2003), and Han, Phillips, and Sul (2010).”
• THE ASYMPTOTIC PROPERTIES OF THE SYSTEM GMM ESTIMATOR IN DYNAMIC PANEL DATA MODELS WHEN BOTH N AND T ARE LARGE (2015, background): “In line with the former approach, Hahn, Hausman, and Kuersteiner (2007), Han and Phillips (2010), and Han, Phillips, and Sul (2014) propose variants of IV/GMM estimators, while Hayakawa and Pesaran (2012) extend the ML approach of Hsiao et al. (2002) to allow for crosssectional heteroskedasticity.”
• Identifying Latent Structures in Panel Data (2016, methodology): “Other methods, such as median unbiased estimation, indirect inference (Gourieroux, Phillips, and Yu, 2010), and X-differencing (Han, Phillips, and Sul, 2014) have been used in dynamic panel data models to avoid bias problems.”