Artikel
A combination method for averaging OLS and GLS estimators
To avoid the risk of misspecification between homoscedastic and heteroscedastic models, we propose a combination method based on ordinary least-squares (OLS) and generalized least-squares (GLS) model-averaging estimators. To select optimal weights for the combination, we suggest two information criteria and propose feasible versions that work even when the variance-covariance matrix is unknown. The optimality of the method is proven under some regularity conditions. The results of a Monte Carlo simulation demonstrate that the method is adaptive in the sense that it achieves almost the same estimation accuracy as if the homoscedasticity or heteroscedasticity of the error term were known.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 7 ; Year: 2019 ; Issue: 3 ; Pages: 1-12 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
- Thema
-
combination method
GLS
model averaging
OLS
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Liu, Qingfeng
Vasnev, Andrey L.
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2019
- DOI
-
doi:10.3390/econometrics7030038
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Liu, Qingfeng
- Vasnev, Andrey L.
- MDPI
Entstanden
- 2019
Ähnliche Objekte (12)
Averaging of moment condition estimators
On uniform asymptotic risk of averaging GMM estimators
The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators
The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators
Forecast Combination and Model Averaging using Predictive Measures
Forecast combination and Bayesian model averaging: A prior sensitivity analysis
When can we determine the direction of omitted variable bias of OLS estimators?
Crowdsourcing of Economic Forecast – Combination of Forecasts Using Bayesian Model Averaging
Crowdsourcing of Economic Forecast – Combination of Forecasts Using Bayesian Model Averaging
OLS | OLS are CooL
OLS
*OLS*
Averaging of moment condition estimators
On uniform asymptotic risk of averaging GMM estimators
The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators
The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators
Forecast Combination and Model Averaging using Predictive Measures
Forecast combination and Bayesian model averaging: A prior sensitivity analysis
When can we determine the direction of omitted variable bias of OLS estimators?
Crowdsourcing of Economic Forecast – Combination of Forecasts Using Bayesian Model Averaging
Crowdsourcing of Economic Forecast – Combination of Forecasts Using Bayesian Model Averaging
OLS | OLS are CooL
OLS
*OLS*
Averaging of moment condition estimators
On uniform asymptotic risk of averaging GMM estimators
The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators
The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators
Forecast Combination and Model Averaging using Predictive Measures
Forecast combination and Bayesian model averaging: A prior sensitivity analysis
When can we determine the direction of omitted variable bias of OLS estimators?
Crowdsourcing of Economic Forecast – Combination of Forecasts Using Bayesian Model Averaging
Crowdsourcing of Economic Forecast – Combination of Forecasts Using Bayesian Model Averaging
OLS | OLS are CooL
OLS