Forecasting GDP at the regional level with many predictors

Robert Lehmann and Klaus Wohlrabe

Discussion Papers in Economics from University of Munich, Department of Economics

Abstract: In this paper, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden- Württemberg) and Eastern Germany. We overcome the problem of a ’data-poor environment’ at the sub-national level by complementing various regional indicators with more than 200 national and international indicators. We calculate single– indicator, multi–indicator, pooled and factor forecasts in a pseudo real–time setting. Our results show that we can significantly increase forecast accuracy compared to an autoregressive benchmark model, both for short and long term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP.

Keywords: regional forecasting; forecast combination; factor models; model confidence set; data–rich environment (search for similar items in EconPapers)
JEL-codes: C32 C52 C53 E37 R11 (search for similar items in EconPapers)
Date: 2013-09-14
New Economics Papers: this item is included in nep-for, nep-geo and nep-ure
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Citations: View citations in EconPapers (12)

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Related works:
Journal Article: Forecasting GDP at the Regional Level with Many Predictors (2015)
Journal Article: Forecasting GDP at the Regional Level with Many Predictors (2015)
Working Paper: Forecasting GDP at the regional level with many predictors (2013)
Working Paper: Forecasting GDP at the Regional Level with Many Predictors (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:lmu:muenec:17104

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