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Nowcasting of the Gross Regional Product

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  • Anna Norin

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    Abstract

    Business cycles are usually defined at a national level. The implicit assumption being that it affects all regions similarly. This is combined with a lack of timely information on regional economic development as annual values of the gross regional product (GRP) are often published with up to two years lag. The present paper evaluates a method of obtaining values of the GRP as soon as monthly and quarterly business cycle indicators become available. Building on earlier work on using bridge equations to obtaining quarterly values of GDP growth, a method is proposed were annual GRP growth is estimated using a large number of business cycle indicators. The procedure is applied to data for the Northern regions of Sweden. With the present method it is possible to continuously refine GRP growth values throughout the year. By utilizing the information content in available business cycle indicators, a nowcast of the GRP is obtained as opposed to a pure forecast based solely on past information. Nowcasts will then provide valuable information on how current highs or lows are affecting different regions.

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    File URL: http://www-sre.wu.ac.at/ersa/ersaconfs/ersa10/ERSA2010finalpaper768.pdf
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    Bibliographic Info

    Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa10p768.

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    Date of creation: Sep 2011
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    Handle: RePEc:wiw:wiwrsa:ersa10p768

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    1. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, . "Survey Data as Coincident or Leading Indicators," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    2. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    3. G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2009. "Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 595-611.
    4. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
    5. Giuseppe Parigi & Roberto Golinelli, 2007. "The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 77-94.
    6. Jurgen A. Doornik, 2008. "Encompassing and Automatic Model Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 915-925, December.
    7. Luis C. Nunes, 2005. "Nowcasting quarterly GDP growth in a monthly coincident indicator model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 575-592.
    8. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    9. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    10. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, vol. 20(3), pages 447-460.
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