Economics at your fingertips  

Forecasting compositional time series: A state space approach

Ralph Snyder (), Keith Ord (), Anne B. Koehler, Keith McLaren () and Adrian N. Beaumont

International Journal of Forecasting, 2017, vol. 33, issue 2, 502-512

Abstract: A framework for the forecasting of composite time series, such as market shares, is proposed. Based on Gaussian multi-series innovations state space models, it relies on the log-ratio function to transform the observed shares (proportions) onto the real line. The models possess an unrestricted covariance matrix, but also have certain structural elements that are common to all series, which is proved to be both necessary and sufficient to ensure that the predictions of shares are invariant to the choice of base series. The framework includes a computationally efficient maximum likelihood approach to estimation, relying on exponential smoothing methods, which can be adapted to handle series that start late or finish early (new or withdrawn products). Simulated joint prediction distributions provide approximations to the required prediction distributions of individual shares and the associated quantities of interest. The approach is illustrated on US automobile market share data for the period 1961–2013.

Keywords: Log ratio transformation; Market shares; Maximum likelihood estimation; Model invariance; Multi-series models; New products; Prediction distributions; US automobiles sales; Vector exponential smoothing (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Forecasting Compositional Time Series: A State Space Approach (2015) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2019-06-13
Handle: RePEc:eee:intfor:v:33:y:2017:i:2:p:502-512