Showing 1 - 10 of 14
This study uses Monte Carlo analysis to investigate the performances of five different meta-analysis (MA) estimators: the Fixed Effects (FE) estimator, the Weighted Least Squares (WLS) estimator, the Random Effects (RE) estimator, the Precision Effect Test (PET) estimator, and the Precision...
Persistent link: https://www.econbiz.de/10010478499
This paper shows how to bootstrap hypothesis tests in the context of the Parks (Efficient estimation of a system of regression equations when disturbances are both serially and contemporaneously correlated 1967) estimator. It then demonstrates that the bootstrap outperforms Parks's top...
Persistent link: https://www.econbiz.de/10012018487
Persistent link: https://www.econbiz.de/10011886419
This paper shows how to bootstrap hypothesis tests in the context of the Parks’s (1967) Feasible Generalized Least Squares estimator. It then demonstrates that the bootstrap outperforms FGLS(Parks)’s top competitor. The FGLS(Parks) estimator has been a workhorse for the analysis of panel...
Persistent link: https://www.econbiz.de/10012160012
Non-spherical errors, namely heteroscedasticity, serial correlation and cross-sectional correlation are commonly present within panel data sets. These can cause significant problems for econometric analyses. The FGLS(Parks) estimator has been demonstrated to produce considerable efficiency gains...
Persistent link: https://www.econbiz.de/10010301698
Non-spherical errors, namely heteroscedasticity, serial correlation and cross-sectional correlation are commonly present within panel data sets. These can cause significant problems for econometric analyses. The FGLS(Parks) estimator has been demonstrated to produce considerable efficiency gains...
Persistent link: https://www.econbiz.de/10010303845
This paper shows how to bootstrap hypothesis tests in the context of the Parks (Efficient estimation of a system of regression equations when disturbances are both serially and contemporaneously correlated 1967) estimator. It then demonstrates that the bootstrap outperforms Parks's top...
Persistent link: https://www.econbiz.de/10012020272
This study uses Monte Carlo analysis to investigate the performances of five different meta-analysis (MA) estimators: the Fixed Effects (FE) estimator, the Weighted Least Squares (WLS) estimator, the Random Effects (RE) estimator, the Precision Effect Test (PET) estimator, and the Precision...
Persistent link: https://www.econbiz.de/10010478803
This paper shows how to bootstrap hypothesis tests in the context of the Parks's (1967) Feasible Generalized Least Squares estimator. It then demonstrates that the bootstrap outperforms FGLS(Parks)'s top competitor. The FGLS(Parks) estimator has been a workhorse for the analysis of panel data...
Persistent link: https://www.econbiz.de/10012160886
This study uses Monte Carlo analysis to investigate the performances of five different meta-analysis (MA) estimators: the Fixed Effects (FE) estimator, the Weighted Least Squares (WLS) estimator, the Random Effects (RE) estimator, the Precision Effect Test (PET) estimator, and the Precision...
Persistent link: https://www.econbiz.de/10010907416