Comparing Forecasting Performance with Panel Data

63 Pages Posted: 28 May 2019

See all articles by Allan Timmermann

Allan Timmermann

University of California, San Diego (UCSD) - Rady School of Management

Yinchu Zhu

University of Oregon

Date Written: April 29, 2019

Abstract

This paper develops new methods for testing equal predictive accuracy in panels of forecasts that exploit information in the time series and cross-sectional dimensions of the data. Using a common factor setup, we establish conditions on cross-sectional dependencies in forecast errors which allow us to conduct inference and compare performance on a single cross-section of forecasts. We consider both unconditional tests of equal predictive accuracy as well as tests that condition on the realization of common factors and show how to decompose forecast errors into exposures to common factors and an idiosyncratic variance component. Our tests are demonstrated in an empirical application that compares IMF forecasts of country-level real GDP growth and inflation to private-sector survey forecasts and forecasts from a simple time-series model..

Keywords: Economic forecasting, Panel Data; Factor Models, Panel Diebold-Mariano Test, GDP growth, Inflation Forecasts

JEL Classification: C53

Suggested Citation

Timmermann, Allan and Zhu, Yinchu, Comparing Forecasting Performance with Panel Data (April 29, 2019). Available at SSRN: https://ssrn.com/abstract=3380755 or http://dx.doi.org/10.2139/ssrn.3380755

Allan Timmermann (Contact Author)

University of California, San Diego (UCSD) - Rady School of Management ( email )

9500 Gilman Drive
Rady School of Management
La Jolla, CA 92093
United States

Yinchu Zhu

University of Oregon ( email )

1280 University of Oregon
Eugene, OR 97403
United States

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