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Quarterly estimates of regional GDP in Poland – application of statistical inference of functions of parameters

Author

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  • Mateusz Pipień
  • Sylwia Roszkowska

Abstract

The article presents the application of a linear regression model to the problem of space-time disaggregation of the GDP of the Polish economy. In the approach described, the structural parameters of linear regression are subject to estimation, where the annual GDP of voivodships (regions of Poland at NUTS 2 level) or the rate of its changes represent the explained variable, while the annual domestic GDP or the rate of its changes is the explanatory variable. The authors propose to estimate the quarterly GDP and its changes in individual regions as functions of regression parameters. The study presents the results of GDP estimates and its changes in individual regions. Alternative approaches were subject to evaluation in terms of the level of statistical uncertainty associated with the estimation and in terms of the level of spatial diversity of the estimated values.

Suggested Citation

  • Mateusz Pipień & Sylwia Roszkowska, 2015. "Quarterly estimates of regional GDP in Poland – application of statistical inference of functions of parameters," NBP Working Papers 219, Narodowy Bank Polski.
  • Handle: RePEc:nbp:nbpmis:219
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    References listed on IDEAS

    as
    1. Santos Silva, J. M. C. & Cardoso, F. N., 2001. "The Chow-Lin method using dynamic models," Economic Modelling, Elsevier, vol. 18(2), pages 269-280, April.
    2. Massimiliano Marcellino, 2007. "Pooling‐Based Data Interpolation and Backdating," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, January.
    3. Wolfang Polasek & Carlos Llano & Richard Sellner, 2010. "Bayesian Methods for Completing Data in Spatial Models," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 2(2), pages 194-214, June.
    4. Daniel O. Stram & William W. S. Wei, 1986. "A Methodological Note On The Disaggregation Of Time Series Totals," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(4), pages 293-302, July.
    5. repec:sae:niesru:v:161:y::i:1:p:84-89 is not listed on IDEAS
    6. Wolfgang Polasek, 2013. "Spatial Chow-Lin Models for Completing Growth Rates in Cross-sections," Economics Series 295, Institute for Advanced Studies.
    7. Salazar, Eduardo & Smith, Richard & Weale, Martin & Wright, Stephen, 1997. "A Monthly Indicator of GDP," National Institute Economic Review, National Institute of Economic and Social Research, vol. 161, pages 84-89, July.
    8. 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 169-173, April.
    9. Eduardo Salazar & Richard Smith & Martin Weale & Stephen Wright, 1997. "A Monthly Indicator of GDP," National Institute Economic Review, National Institute of Economic and Social Research, vol. 161(1), pages 84-89, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Disaggregation of observed macroeconomic categories; classical linear regression model; uncertainty of estimation.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts

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