Liebermann, Joelle (2010): Real-time nowcasting of GDP: Factor model versus professional forecasters.
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This paper performs a fully real-time nowcasting (forecasting) exercise of US real gross domestic product (GDP) growth using Giannone, Reichlin and Small (2008) factor model framework which enables one to handle unbalanced datasets as available in real-time. To this end, we have constructed a novel real-time database of vintages from October 2000 to June 2010 for a rich panel of US variables, and can hence reproduce, for any given day in that range, the exact information that was available to a real-time forecaster. We track the daily evolution throughout the current and next quarter of the model nowcasting performance. Analogously to Giannone et al. (2008) pseudo real-time results, we find that the precision of the nowcasts increases with information releases. Furthermore, the Survey of Professional Forecasters (SPF) does not carry additional information with respect to the model best specification, suggesting that the often cited superiority of the SPF, attributable to judgment, is weak over our sample. Then, as one moves forward along the real-time data flow, the continuous updating of the model provides a more precise estimate of current quarter GDP growth and the SPF becomes stale compared to all the model specifications. These results are robust to the recent recession period.
|Item Type:||MPRA Paper|
|Original Title:||Real-time nowcasting of GDP: Factor model versus professional forecasters|
|English Title:||Real-time nowcasting of GDP: Factor model versus professional forecasters|
|Keywords:||Real-time data; Nowcasting; Forecasting; Factor model|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy
C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C33 - Models with Panel Data; Longitudinal Data; Spatial Time Series
|Depositing User:||JOELLE LIEBERMANN|
|Date Deposited:||15. Feb 2011 23:44|
|Last Modified:||13. Feb 2013 22:35|
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