Real-time forecasting US GDP from small-scale factor models

Maximo Camacho and Jaime Martíinez-Martin
Authors registered in the RePEc Author Service: Jaime Martinez-Martin

No 1210, Working Papers from BBVA Bank, Economic Research Department

Abstract: This paper proposes two refinements to the single-index dynamic factor model developed by Aruoba and Diebold (AD, 2010) to monitor US economic activity in real time. First, we adapt the model to include survey data and financial indicators. Second, we examine the predictive performance of the model when the goal is to forecast GDP growth. We find that our model is unequivocally the preferred alternative to compute backcasts. In nowcasting and forecasting, our model is able to forecast growth as well as AD and much better than several baseline alternatives. In addition, we find that our model could be used to predict more accurately the US business cycles.

Keywords: real-time forecasting; business cycles; US GDP (search for similar items in EconPapers)
JEL-codes: C22 E27 E32 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2012-06
New Economics Papers: this item is included in nep-bec, nep-fdg, nep-for and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Real-time forecasting US GDP from small-scale factor models (2014)
Working Paper: Real-time forecasting us GDP from small-scale factor models (2014)
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