Estimating long-run coefficients and bootstrapping standard errors in large panels with cross-sectional dependence

This presentation explains how to estimate long-run coefficients and bootstrap standard errors in a dynamic panel with heterogeneous coefficients, common factors, and many observations over cross-sectional units and time periods. The common factors cause cross-sectional dependence, which is approximated by cross-sectional averages. Heterogeneity of the coefficients is accounted by taking the unweighted averages of the unit-specific estimates. Following Chudik, Mohaddes, Pesaran and Raissi (2016, Advances in Econometrics 36: 85-135) I consider three models to estimate long-run coefficients: a simple dynamic model (CS-DL), an error-correction model, and an ARDL model (CS-ARDL). I explain how to estimate all three models using the Stata community-contributed command xtdcce2. Secondly, I compare the nonparametric standard errors and bootstrapped standard errors. The bootstrap follows on the lines of Goncalves and Perron (2016) and the community-contributed command boottest by Roodman, Nielsen, Webb, and Mackinnon (2018). The challenges are to maintain the error structure across time and cross-sectional units and to encompass the dynamic structure of the model.