Forecasting aggregates and disaggregates with common features

Antoni Espasa and Iván Mayo-Burgos

International Journal of Forecasting, 2013, vol. 29, issue 4, 718-732

Abstract: This paper focuses on the provision of consistent forecasts for an aggregate economic indicator, such as a consumer price index and its components. The procedure developed is a disaggregated approach based on single-equation models for the components, which take into account the stable features that some components share, such as a common trend and common serial correlation. Our procedure starts by classifying a large number of components based on restrictions from common features. The result of this classification is a disaggregation map, which may also be useful in applying dynamic factors, defining intermediate aggregates or formulating models with unobserved components. We use the procedure to forecast inflation in the Euro area, the UK and the US. Our forecasts are significantly more accurate than either a direct forecast of the aggregate or various other indirect forecasts.

Keywords: Common trends; Common serial correlation; Inflation; Euro area; UK; US; Cointegration; Single-equation econometric models; Disaggregation maps (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207012001355
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Forecasting aggregates and disaggregates with common features (2012)
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: https://EconPapers.repec.org/RePEc:eee:intfor:v:29:y:2013:i:4:p:718-732

DOI: 10.1016/j.ijforecast.2012.10.004

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 Catherine Liu ().