Nearly unbiased estimation of autoregressive models for bounded near‐integrated stochastic processes
The paper investigates the estimation bias of autoregressive models for bounded near‐integrated stochastic processes and the performance of the standard procedures in the literature that aim to correct the estimation bias. In some cases, the bounded nature of the stochastic processes worsens the est...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/173440 |
| Acceso en línea: | https://hdl.handle.net/2445/173440 |
| Access Level: | acceso abierto |
| Palabra clave: | Econometria Anàlisi de regressió Anàlisi estocàstica Mètode de Montecarlo Econometrics Regression analysis Analyse stochastique Monte Carlo method |
| Sumario: | The paper investigates the estimation bias of autoregressive models for bounded near‐integrated stochastic processes and the performance of the standard procedures in the literature that aim to correct the estimation bias. In some cases, the bounded nature of the stochastic processes worsens the estimation bias effect. The paper extends two popular autoregressive estimation bias correction procedures to cover bounded stochastic processes. Monte Carlo simulations reveal that accounting for the bounded nature of the stochastic processes leads to improvements in the estimation of autoregressive models. Finally, an illustration is given using the unemployment rate of the G7 countries. |
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