Forecast Densities for Economic Aggregates from Disaggregate Ensembles

CAMA Working Paper No. 10/2010

Posted: 31 Aug 2010

See all articles by Francesco Ravazzolo

Francesco Ravazzolo

Free University of Bolzano

Shaun P. Vahey

Australian National University (ANU) - Centre for Applied Macroeconomic Analysis (CAMA)

Multiple version iconThere are 2 versions of this paper

Date Written: March 12, 2010

Abstract

We propose a methodology for producing forecast densities for economic aggregates based on disaggregate evidence. Our ensemble predictive methodology utilizes a linear mixture of experts framework to combine the forecast densities from potentially many component models. Each component represents the univariate dynamic process followed by a single disaggregate variable. The ensemble produced from these components approximates the many unknown relationships between the disaggregates and the aggregate by using time-varying weights on the component forecast densities. In our application, we use the disaggregate ensemble approach to forecast US Personal Consumption Expenditure inflation from 1997Q2 to 2008Q1. Our ensemble combining the evidence from 11 disaggregate series outperforms an aggregate autoregressive benchmark, and an aggregate time-varying parameter specification in density forecasting.

Keywords: Ensemble forecasting, disaggregates

JEL Classification: C11, C32, C53, E37, E52

Suggested Citation

Ravazzolo, Francesco and Vahey, Shaun P., Forecast Densities for Economic Aggregates from Disaggregate Ensembles (March 12, 2010). CAMA Working Paper No. 10/2010. Available at SSRN: https://ssrn.com/abstract=1668149 or http://dx.doi.org/10.2139/ssrn.1668149

Francesco Ravazzolo (Contact Author)

Free University of Bolzano ( email )

Bolzano
Italy

Shaun P. Vahey

Australian National University (ANU) - Centre for Applied Macroeconomic Analysis (CAMA) ( email )

ANU College of Business and Economics
Canberra, Australian Capital Territory 0200
Australia

Register to save articles to
your library

Register

Paper statistics

Downloads
23
Abstract Views
358
PlumX Metrics