Time series: theory and methods
Time series: theory and methods
Forecasting nonlinear time series with neural network sieve bootstrap
Computational Statistics & Data Analysis
A bootstrap panel unit root test under cross-sectional dependence, with an application to PPP
Computational Statistics & Data Analysis
Early warning systems for sovereign debt crises: The role of heterogeneity
Computational Statistics & Data Analysis
Unobserved heterogeneity in panel time series models
Computational Statistics & Data Analysis
On the asymptotic t-test for large nonstationary panel models
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Panel estimators can provide consistent measures of a long-run average parameter even if the individual regressions are spurious. However, the t-test on this parameter is fraught with problems because the limit distribution of the test statistic is non-standard and rather complicated, particularly in panels with mixed (non-)stationary errors. A sieve bootstrap framework is suggested to approximate the distribution of the t-statistic. An extensive Monte Carlo study demonstrates that the bootstrap is quite useful in this context.