Temporal Disaggregation by State Space Methods: Dynamic Regression Methods Revisited

16 Pages Posted: 1 Nov 2006

See all articles by Tommaso Proietti

Tommaso Proietti

University of Rome II - Department of Economics and Finance

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.

Suggested Citation

Proietti, Tommaso, Temporal Disaggregation by State Space Methods: Dynamic Regression Methods Revisited. Econometrics Journal, Vol. 9, No. 3, pp. 357-372, November 2006. Available at SSRN: https://ssrn.com/abstract=941527 or http://dx.doi.org/10.1111/j.1368-423X.2006.00189.x

Tommaso Proietti (Contact Author)

University of Rome II - Department of Economics and Finance ( email )

Via Columbia, 2
Rome, 00133
Italy

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