Template-type: ReDIF-Paper 1.0
Author-Name: José Casals Carro
Author-Workplace-Name: Universidad Complutense de Madrid. Departamento de Fundamentos del Análisis Económico II (Economía Cuantitativa).
Author-Workplace-Homepage:
 https://www.ucm.es/fundamentos-analisis-economico2
Author-Name: Miguel Jerez Méndez 
Author-Email: mjerez@ccee.ucm.es
Author-Workplace-Name: Universidad Complutense de Madrid. Departamento de Fundamentos del Análisis Económico II (Economía Cuantitativa).
Author-Workplace-Homepage:
 https://www.ucm.es/fundamentos-analisis-economico2
Author-Name: Sonia Sotoca López
Author-Email: sotoca@ccee.ucm.es
Author-Workplace-Name: Universidad Complutense de Madrid. Departamento de Fundamentos del Análisis Económico II (Economía Cuantitativa).
Author-Workplace-Homepage:
 https://www.ucm.es/fundamentos-analisis-economico2  
Title: Modelling an forecasting time series sampled at different frequencies
Abstract: This paper discusses how to specify an observable high-frequency model for a vector of time series sampled at high and low 
	frequencies. To this end we first study how aggregation over time affects both, the dynamic components of a time series and their 
	observability, in a multivariate linear framework. We find that the basic dynamic components remain unchanged but some of them, 
	mainly those related to the seasonal structure, become unobservable. Building on these results, we propose a structured 
	specification method built on the idea that the models relating the variables in high and low sampling frequencies should be 
	mutually consistent. After specifying a consistent and observable high-frequency model, standard state-space techniques provide 
	an adequate framework for estimation, diagnostic checking, data interpolation and forecasting. Our method has three main uses. 
	First, it is useful to disaggregate a vector of low-frequency time series into high-frequency estimates coherent with both, the 
	sample information and its statistical properties. Second, it may improve forecasting of the low-frequency variables, as the 
	forecasts conditional to high-frequency indicators have in general smaller error variances than those derived from the 
	corresponding low-frequency values. Third, the resulting forecasts can be updated as new high-frequency values become available, 
	thus providing an effective tool to assess the effect of new information over medium term expectations. An example using national 
	accounting data illustrates the practical application of this method.
Keywords: State-space models, Kalman filter, Temporal disaggregation, Observability, Seasonality.
Length: 50 pages 
Creation-Date: 2006
Number:
 0603
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae0603.txt
File-URL: https://eprints.ucm.es/id/eprint/7911/1/0603.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:0603