Nowcasting Activity in Argentina using Dynamic Factor Models
Having a correct assessment of current business cycle conditions is one of the major challenges for monetary policy conduct. Given that GDP figures are available with a significant delay central banks are increasingly using Nowcasting as a useful tool for having an immediate perception of economic c...
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| Autores principales: | , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2018
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/169113 |
| Aporte de: |
| Sumario: | Having a correct assessment of current business cycle conditions is one of the major challenges for monetary policy conduct. Given that GDP figures are available with a significant delay central banks are increasingly using Nowcasting as a useful tool for having an immediate perception of economic conditions. We develop a GDP growth Nowcasting exercise using a broad and restricted set of indicators to construct different models including dynamic factor models as well as a FAVAR.
We compare each individual model with alternative combinations in a pseuro-real time out-ofsample exercise and find an improvement in predictive performance using the Giacomini and White (2004) test. Finally we introduce a DFM state-space approach, being able to measure the impact of data releases or news on sequential forecast. |
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