Nowcasting GDP in Argentina: Comparing the Predictive Ability of Different Models

Emilio Blanco, Laura D’Amato (), Fiorella Dogliolo () and Lorena Garegnani ()
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Laura D’Amato: Central Bank of Argentina, UBA
Fiorella Dogliolo: Central Bank of Argentina, UNLP
Lorena Garegnani: Central Bank of Argentina, UNLP

No 201774, BCRA Working Paper Series from Central Bank of Argentina, Economic Research Department

Abstract: 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 their relative forecasting ability using the Giacomini and White (2004) test and find no significant difference in predictive ability among them. Nevertheless a combination of them proves to significantly improve predictive performance.

Keywords: nowcasting; dynamic factor models; forecast pooling (search for similar items in EconPapers)
JEL-codes: C22 C53 E37 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2017-12
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Citations: View citations in EconPapers (1)

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