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Temporal Disaggregation by State Space Methods: Dynamic Regression Methods Revisited

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  • Tommaso Proietti

    (Dipartimento di Scienze Statistiche, Udine)

Abstract

The paper documents and illustrates state space methods that implement time series disaggregation by regression methods, with dynamics that depend on a single autoregressive parameter. The most popular techniques for the distribution of economic flow variables, such as Chow-Lin, Fernandez and Litterman, are encompassed by this unifying framework. The state space methodology offers the generality that is required to address a variety of inferential issues, such as the role of initial conditions, which are relevant for the properties of the maximum likelihood estimates and for the the derivation of encompassing representations that nest exactly the traditional disaggregation models, and the definition of a suitable set of real time diagnostics on the quality of the disaggregation and revision histories that support model selection. The exact treatment of temporal disaggregation by dynamic regression models, when the latter are formulated in the logarithms, rather than the levels, of an economic variable, is also provided. The properties of the profile and marginal likelihood are investigated and the problems with estimating the Litterman model are illustrated. In the light of the nonstationary nature of the economic time series usually entertained in practice, the suggested strategy is to fit an autoregressive distribute lag model, which, under a reparameterisation and suitable initial conditions, nests both the Chow-Lin and the Fernandez model, thereby incorporating our uncertainty about the presence of cointegration between the aggregated series and the indicators.

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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0411011.

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Length: 33 pages
Date of creation: 15 Nov 2004
Date of revision:
Handle: RePEc:wpa:wuwpem:0411011

Note: Type of Document - pdf; pages: 33
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Web page: http://128.118.178.162

Related research

Keywords: Autoregressive Distributed Lag Models; COMFAC; Augmented Kalman filter and smoother; Marginal Likelihood; Logarithmic Transformation.;

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  1. 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, vol. 88(351), pages 549-63, September.
  2. 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.
  3. 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.
  4. 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.
  5. Tommaso Proietti & Filippo Moauro, 2004. "Dynamic Factor Analysis with Nonlinear Temporal Aggregation Constraints," Econometrics 0401003, EconWPA.
  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 372-75, November.
  7. Filippo Moauro & Giovanni Savio, 2005. "Temporal disaggregation using multivariate structural time series models," Econometrics Journal, Royal Economic Society, vol. 8(2), pages 214-234, 07.
  8. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
  9. 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.
  10. Tommaso Proietti, 2004. "On the Estimation of Nonlinearly Aggregated Mixed Models," Econometrics 0411012, EconWPA.
  11. 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.
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