Bayesian averaging of classical estimates in asymmetric vector autoregressive models
| Autor: | Dennis S. Mapa, Manuel Leonard F. Albis |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Statistics and Probability
Statistics::Applications 05 social sciences Autocorrelation Bayesian probability Regression Statistics::Computation Vector autoregression Autoregressive model Moving average Robustness (computer science) Modeling and Simulation 0502 economics and business Statistics Econometrics Statistics::Methodology 050207 economics 050205 econometrics Mathematics Variable (mathematics) |
| Zdroj: | Communications in Statistics - Simulation and Computation. 46:1760-1770 |
| ISSN: | 1532-4141 0361-0918 |
| DOI: | 10.1080/03610918.2015.1011335 |
| Popis: | The estimated vector autoregressive (VAR) model is sensitive to model misspecifications, resulting to biased and inconsistent parameter estimates. This article extends the Bayesian averaging of classical estimates, a robustness procedure in cross-section data, to a vector time-series that is estimated using a large number of asymmetric VAR models. The proposed procedure was applied to simulated data from various forms of model misspecifications. The results of the simulation suggest that, under misspecification problems, particularly if an important variable and moving average (MA) terms were omitted, the proposed procedure gives robust results and better forecasts than the automatically selected equal lag-length VAR model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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