Nowcasting Brazilian GDP: a performance assessment of dynamic factor models
This work compares dynamic factor model’s forecasts for Brazilian GDP. Our approach takes into account mixed frequencies and can handle missing data. We implement three models: the first is based on the Principal Components Analysis methodology; the second employs a two-step estimation method with q...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Brasil |
| Institución: | Fundação Getulio Vargas (FGV) |
| Repositorio: | Repositório Institucional do FGV (FGV Repositório Digital) |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.fgv.br:10438/22986 |
| Acceso en línea: | https://hdl.handle.net/10438/22986 |
| Access Level: | acceso abierto |
| Palabra clave: | GDP Dynamic factor model Nowcasting Economia Produto interno bruto Previsão econômica Indicadores econômicos |
| Sumario: | This work compares dynamic factor model’s forecasts for Brazilian GDP. Our approach takes into account mixed frequencies and can handle missing data. We implement three models: the first is based on the Principal Components Analysis methodology; the second employs a two-step estimation method with quarterly inputs; the last is similar to the former but uses monthly series. A real-time out-of-sample exercise is proposed to assess the performance of these models. A dataset is created for each day within 27 quarters - from the fourth quarter of 2010 up to the second quarter of 2017. For recent periods, the nowcasts estimated by both two-step procedures perform better than the average predictions of Focus Survey, a bulletin organized by the Brazilian Central Bank. We also show evidence that the average of GDP forecasts from this survey may be biased |
|---|