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 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

Author dc:creator

• Gomes, Guilherme Branco

Advisor dc:contributor.advisor

• Issler, João Victor

• GDP
• Dynamic factor model
• Nowcasting

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/10438/22986

OAI identifier oai:identifier

oai:repositorio.fgv.br:10438/22986