Forecast Densities for Economic Aggregates from Disaggregate Ensembles

32 Pages Posted: 10 May 2010

See all articles by Francesco Ravazzolo

Francesco Ravazzolo

Free University of Bozen-Bolzano - Faculty of Economics and Management; BI Norwegian Business School

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 5, 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 out-performs 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 5, 2010). Norges Bank Working Paper 2010/02. Available at SSRN: https://ssrn.com/abstract=1601253 or http://dx.doi.org/10.2139/ssrn.1601253

Francesco Ravazzolo

Free University of Bozen-Bolzano - Faculty of Economics and Management ( email )

Via Sernesi 1
39100 Bozen-Bolzano (BZ), Bozen 39100
Italy

BI Norwegian Business School ( email )

Nydalsveien 37
Oslo, 0442
Norway

HOME PAGE: http://www.francescoravazzolo.com/

Shaun P. Vahey (Contact Author)

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
33
Abstract Views
479
PlumX Metrics