Forecasting Macroeconomic Variables Using Disaggregate Survey Data

28 Pages Posted: 13 Mar 2012

See all articles by Kjetil Martinsen

Kjetil Martinsen

Norges Bank

Francesco Ravazzolo

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

Fredrik Wulfsberg

Oslo Business School

Multiple version iconThere are 2 versions of this paper

Date Written: March 11, 2012

Abstract

We propose to construct factor models based on disaggregate survey data to forecast national aggregate macroeconomic variables. We apply our methodology to Norges Bank’s regional survey, which allows to construct regional and sectoral factor models, and to the Swedish Business Tendency survey, which allows to derive sectoral factor. The analysis identifies which information extracted from the regions and the sectors performs particularly well at forecasting different variables and horizons. Results show that several factor models beat an autoregressive benchmark in forecasting inflation and unemployment rate. However, the factor models are most successful in now casting and forecasting GDP growth. Forecast combinations of regional and sectoral factor models based on past performance give in most cases the most accurate forecasts.

Keywords: factor models, macroeconomic forecasting, qualitative survey data

JEL Classification: C53, C80

Suggested Citation

Martinsen, Kjetil and Ravazzolo, Francesco and Wulfsberg, Fredrik, Forecasting Macroeconomic Variables Using Disaggregate Survey Data (March 11, 2012). Available at SSRN: https://ssrn.com/abstract=2020153 or http://dx.doi.org/10.2139/ssrn.2020153

Kjetil Martinsen

Norges Bank ( email )

P.O. Box 1179
Oslo, N-0107
Norway

Francesco Ravazzolo (Contact Author)

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/

Fredrik Wulfsberg

Oslo Business School ( email )

Pilestredet 35
Oslo, 0557
Norway

Register to save articles to
your library

Register

Paper statistics

Downloads
100
Abstract Views
1,339
rank
227,902
PlumX Metrics