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Spatial Chow-Lin Methods: Bayesian And Ml Forecast Comparisons

Author

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  • Wolfgang Polasek

    (IHS, Austria and The Rimini Centre of Economic Analisys, Italy)

  • Richard Sellner

    (IHS, Austria)

Abstract

Completing data that are collected in disaggregated and heterogeneous spatial units is a quite frequent problem in spatial analyses of regional data. Chow and Lin (1971) (CL) were the first to develop a unified framework for the three problems (interpolation, extrapolation and distribution) of predicting disaggregated times series by so-called indicator series. This paper develops a spatial CL procedure for disaggregating cross-sectional spatial data and compares the Maximum Likelihood and Bayesian spatial CL forecasts with the naive pro rata error distribution. We outline the error covariance structure in a spatial context, derive the BLUE for the ML estimator and the Bayesian estimation procedure by MCMC. Finally we apply the procedure to European regional GDP data and discuss the disaggregation assumptions. For the evaluation of the spatial Chow-Lin procedure we assume that only NUTS 1 GDP is known and predict it at NUTS 2 by using employment and spatial information available at NUTS 2. The spatial neighborhood is defined by the inverse travel time by car in minutes. Finally, we present the forecast accuracy criteria comparing the predicted values with the actual observations.

Suggested Citation

  • Wolfgang Polasek & Richard Sellner, 2008. "Spatial Chow-Lin Methods: Bayesian And Ml Forecast Comparisons," Working Paper series 38_08, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:38_08
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    References listed on IDEAS

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    1. James P. LeSage & R. Kelley Pace, 2004. "Models for Spatially Dependent Missing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 233-254, September.
    2. James P. Lesage, 1997. "Bayesian Estimation of Spatial Autoregressive Models," International Regional Science Review, , vol. 20(1-2), pages 113-129, April.
    3. Di Fonzo, Tommaso, 1990. "The Estimation of M Disaggregate Time Series When Contemporaneous and Temporal Aggregates Are Known," The Review of Economics and Statistics, MIT Press, vol. 72(1), pages 178-182, February.
    4. 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.
    5. Milton Friedman, 1962. "The Interpolation of Time Series by Related Series," NBER Books, National Bureau of Economic Research, Inc, number frie62-1, July.
    6. 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 372-375, November.
    7. 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 695-696, November.
    8. Fernandez, Roque B, 1981. "A Methodological Note on the Estimation of Time Series," The Review of Economics and Statistics, MIT Press, vol. 63(3), pages 471-476, August.
    9. 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.
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    Cited by:

    1. Fabio Pammolli & Francesco Porcelli & Francesco Vidoli & Monica Auteri & Guido Borà, 2017. "La spesa sanitaria delle Regioni in Italia - Saniregio2017," Working Papers CERM 01-2017, Competitività, Regole, Mercati (CERM).
    2. Wolfgang Polasek & Richard Sellner, 2013. "The Does Globalization Affect Regional Growth? Evidence for NUTS-2 Regions in EU-27," DANUBE: Law and Economics Review, European Association Comenius - EACO, issue 1, pages 23-65, March.
    3. Vidoli, Francesco & Auteri, Monica, 2022. "Health-care demand and supply at municipal level: A spatial disaggregation approach," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).

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