A panel data approach to economic forecasting: the bias-corrected average forecast
In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to o...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2008 |
| País: | Brasil |
| Institución: | Fundação Getulio Vargas (FGV) |
| Repositorio: | Repositório Institucional do FGV (FGV Repositório Digital) |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.fgv.br:10438/731 |
| Acceso en línea: | http://hdl.handle.net/10438/731 |
| Access Level: | acceso abierto |
| Palabra clave: | Forecast combination Forecast-combination puzzle Common features Panel data Bias-corrected average forecast Economia Previsão econômica - Modelos econométricos |
| Sumario: | In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise based upon data from a well known survey is also presented. Overall, theoretical and empirical results show promise for the feasible bias-corrected average forecast. |
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