Computational Statistics & Data Analysis
Editorial: 2nd Special Issue on Statistical Signal Extraction and Filtering
Computational Statistics & Data Analysis
Sieve bootstrap t-tests on long-run average parameters
Computational Statistics & Data Analysis
Early warning systems for sovereign debt crises: The role of heterogeneity
Computational Statistics & Data Analysis
Factor-GMM estimation with large sets of possibly weak instruments
Computational Statistics & Data Analysis
One-step robust estimation of fixed-effects panel data models
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Recently, the large T panel literature has emphasized unobserved, time-varying heterogeneity that may stem from omitted common variables or global shocks that affect each individual unit differently. These latent common factors induce cross-section dependence and may lead to inconsistent regression coefficient estimates if they are correlated with the explanatory variables. Moreover, if the process underlying these factors is nonstationary, the individual regressions will be spurious but pooling or averaging across individual estimates still permits consistent estimation of a long-run coefficient. The need to tackle both error cross-section dependence and persistent autocorrelation is motivated by the evidence of their pervasiveness found in three well-known, international finance and macroeconomic examples. A range of estimators is surveyed and their finite-sample properties are examined by means of Monte Carlo experiments. These reveal that a mean group version of the common-correlated-effects estimator stands out as the most robust since it is the preferred choice in rather general (non) stationary settings where regressors and errors share common factors and their factor loadings are possibly dependent. Other approaches which perform reasonably well include the two-way fixed effects, demeaned mean group and between estimators but they are less efficient than the common-correlated-effects estimator.