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Spatial and Temporal Time Series Conversion: A Consistent Estimator of the Error Variance-Covariance Matrix

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  • Massimo Gerli
  • Giovanni Marini
Abstract
Spatial and Temporal Time Series Conversion - A Consistent Estimator of the Error Variance-Covariance Matrix. Abstract: We focus on the problem of time series conversion from low to high frequency satisfying the twofold temporal and spatial constraint. We offer a simple solution to variance-covariance matrix estimation of the error terms. Since the residuals of high frequency equations of the indicated indicator model are not observable, we inferred the characteristics of their stochastic process through both a specific hypothesis (VAR 1 process) and estimation of the related annual model. We derive a consistent estimator of the variance-covariance matrix and we prove that Di Fonzo's (1990) estimator based on this matrix is asymptotically equivalent to a GLS estimator.

Suggested Citation

  • Massimo Gerli & Giovanni Marini, 2006. "Spatial and Temporal Time Series Conversion: A Consistent Estimator of the Error Variance-Covariance Matrix," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 373-405.
    • Handle: RePEc:oec:stdkaa:5l9k4xw1sbzq
    DOI: 10.1787/jbcma-v2005-art10-en
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    References listed on IDEAS

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    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. 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.
    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 169-173, April.
    4. 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.
    5. Adrian R Pagan & Anthony D Hall, 1983. "Diagnostic tests as residual analysis," Published Paper Series 1983-1, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    6. James Mitchell & Richard J. Smith & Martin R. Weale & Stephen Wright & Eduardo L. Salazar, 2005. "An Indicator of Monthly GDP and an Early Estimate of Quarterly GDP Growth," Economic Journal, Royal Economic Society, vol. 115(501), pages 108-129, February.
    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.
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    Keywords

    Spatial and temporal disaggregation; VAR;

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