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

This talk 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 for 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 fit all three models using the community-contributed command xtdcce2. Then 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 (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.