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.

Proietti, T. (2006). Temporal disaggregation by state space methods: dynamic regression methods revisited. ECONOMETRICS JOURNAL, 9(3), 357-372 [10.1111/j.1368-423X.2006.00189.x].

Temporal disaggregation by state space methods: dynamic regression methods revisited

PROIETTI, TOMMASO
2006-01-01

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.
2006
Pubblicato
Rilevanza internazionale
Articolo
Sì, ma tipo non specificato
Settore SECS-S/03 - STATISTICA ECONOMICA
English
Con Impact Factor ISI
autoregressive distributed lag models; Kalman filter and smoother; marginal likelihood
Proietti, T. (2006). Temporal disaggregation by state space methods: dynamic regression methods revisited. ECONOMETRICS JOURNAL, 9(3), 357-372 [10.1111/j.1368-423X.2006.00189.x].
Proietti, T
Articolo su rivista
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/14456
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