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On estimating contemporaneous quarterly regional GDP

  • Bernardí Cabrer-Borrás

    (Departamento de Análisis Económico, Universidad de Valencia, Spain)

  • Jose Manuel Pavía-Miralles

    (Departamento Economía Aplicada, Universidad de Valencia, Spain)

Registered author(s):

    Subnational regional jurisdictions rarely have at their disposal a reasonable array of timely statistics to monitor their economic condition. In light of this, we develop a procedure that simultaneously estimates a quarterly time series for all regions of a country based upon quarterly national and annual regional data. While other such techniques exist, we suggest a temporal error structure that eliminates possible spurious jumps. Using our approach, regional analysts should now be able to distribute national growth among regions as soon as quarterly national figures are released. In a Spanish application, we detail some practicalities of the process and show that our proposal produces better estimates than the uniregional methods often used. Copyright © 2007 John Wiley & Sons. Ltd.

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    File URL: http://hdl.handle.net/10.1002/for.1018
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    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 26 (2007)
    Issue (Month): 3 ()
    Pages: 155-170

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    Handle: RePEc:jof:jforec:v:26:y:2007:i:3:p:155-170
    DOI: 10.1002/for.1018
    Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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    1. V. Guerrero & J. Martínez, 1995. "A recursive ARIMA-based procedure for disaggregating a time series variable using concurrent data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 4(2), pages 359-376, December.
    2. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
    3. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-73, April.
    4. Rossi, Nicola, 1982. "A Note on the Estimation of Disaggregate Time Series When the Aggregate Is Known," The Review of Economics and Statistics, MIT Press, vol. 64(4), pages 695-96, November.
    5. Milton Friedman, 1962. "The Interpolation of Time Series by Related Series," NBER Books, National Bureau of Economic Research, Inc, number frie62-1, September.
    6. Milton Friedman, 1962. "Introduction to "The Interpolation of Time Series by Related Series"," NBER Chapters, in: The Interpolation of Time Series by Related Series, pages 1-3 National Bureau of Economic Research, Inc.
    7. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-75, November.
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