Hybrid Forecasting with Estimated Temporally Aggregated Linear Processes

Forthcoming, Journal of Forecasting

To appear in the Journal of Forecasting

20 Pages Posted: 19 Sep 2012 Last revised: 2 Jun 2014

See all articles by Lyudmila Grigoryeva

Lyudmila Grigoryeva

University of Konstanz

Juan-Pablo Ortega

Centre National de la Recherche Scientifique (CNRS); Nanyang Technological University

Date Written: September 19, 2012

Abstract

We introduce a new strategy for the prediction of linear temporal aggregates, we call it "hybrid", and study its performance using asymptotic theory. This scheme consists of carrying out model parameter estimation with data sampled at the highest available frequency and the subsequent prediction with data and models aggregated according to the forecasting horizon of interest. We develop explicit expressions that approximately quantify the mean square forecasting errors associated to the different prediction schemes and that take into account the estimation error component. These approximate estimates indicate that the hybrid forecasting scheme tends to outperform the so called "all-aggregated" approach and, in some instances, the "all-disaggregated'' strategy that is known to be optimal when model selection and estimation errors are neglected. Unlike other related approximate formulas existing in the literature, the ones proposed in this paper are totally explicit and require neither assumptions on the second order stationarity of the sample nor Monte Carlo simulations for their evaluation.

Keywords: linear models, ARMA, temporal aggregation, forecasting, finite sample forecasting, flow temporal aggregation, stock temporal aggregation, multistep forecasting

JEL Classification: C32, C53

Suggested Citation

Grigoryeva, Lyudmila and Ortega, Juan-Pablo and Ortega, Juan-Pablo, Hybrid Forecasting with Estimated Temporally Aggregated Linear Processes (September 19, 2012). Forthcoming, Journal of Forecasting, To appear in the Journal of Forecasting, Available at SSRN: https://ssrn.com/abstract=2148895 or http://dx.doi.org/10.2139/ssrn.2148895

Lyudmila Grigoryeva (Contact Author)

University of Konstanz ( email )

Fach D-144
Universitätsstraße 10
Konstanz, D-78457
Germany

Juan-Pablo Ortega

Centre National de la Recherche Scientifique (CNRS) ( email )

16 route de Gray
Besançon, 25030
France

HOME PAGE: http://juan-pablo-ortega.com

Nanyang Technological University ( email )

21 Nanyang Link
Singapore, 637371
Singapore

HOME PAGE: http://https://juan-pablo-ortega.com

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