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Temporal disaggregation by state space methods: Dynamic regression methods revisited

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  • Tommaso Proietti

Abstract

The paper advocates the use of state space methods to deal with the problem of temporal disaggregation by dynamic regression models, which encompass the most popular techniques for the distribution of economic flow variables, such as Chow-Lin, Fernandez and Litterman. The state space methodology offers the generality that is required to address a variety of inferential issues that have not been dealt with previously. The paper contributes to the available literature in three ways: (i) it concentrates on the exact initialization of the different models, showing that this issue is of fundamental importance for the properties of the maximum likelihood estimates and for deriving encompassing autoregressive distributed lag models that nest exactly the traditional disaggregation models; (ii) it points out the role of diagnostics and revisions histories in judging the quality of the disaggregated estimates and (iii) it provides a thorough treatment of the Litterman model, explaining the difficulties commonly encountered in practice when estimating this model. Copyright Royal Economic Society 2006

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Bibliographic Info

Article provided by Royal Economic Society in its journal Econometrics Journal.

Volume (Year): 9 (2006)
Issue (Month): 3 (November)
Pages: 357-372

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Handle: RePEc:ect:emjrnl:v:9:y:2006:i:3:p:357-372

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  1. Andrew Harvey & Chia-Hui Chung, 2000. "Estimating the underlying change in unemployment in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 303-309.
  2. Robert B. Litterman, 1983. "A random walk, Markov model for the distribution of time series," Staff Report, Federal Reserve Bank of Minneapolis 84, Federal Reserve Bank of Minneapolis.
  3. James H. Stock & Mark W. Watson, 1988. "A Probability Model of The Coincident Economic Indicators," NBER Working Papers 2772, National Bureau of Economic Research, Inc.
  4. Hendry, David F & Mizon, Grayham E, 1978. "Serial Correlation as a Convenient Simplification, not a Nuisance: A Comment on a Study of the Demand for Money by the Bank of England," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 88(351), pages 549-63, September.
  5. Palm, F.C. & Nijman, Th., 1982. "Missing observations in the dynamic regression model," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  6. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, Econometric Society, vol. 55(2), pages 251-76, March.
  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.
  8. Tommaso Proietti, 2004. "On the Estimation of Nonlinearly Aggregated Mixed Models," Econometrics, EconWPA 0411012, EconWPA.
  9. Filippo Moauro & Giovanni Savio, 2005. "Temporal disaggregation using multivariate structural time series models," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 8(2), pages 214-234, 07.
  10. 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 471-76, August.
  11. Tommaso Proietti & Filippo Moauro, 2006. "Dynamic factor analysis with non-linear temporal aggregation constraints," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 281-300.
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