Comparing Forecasting Performance with Panel Data

66 Pages Posted: 30 May 2019

See all articles by Allan Timmermann

Allan Timmermann

UCSD ; Centre for Economic Policy Research (CEPR)

Yinchu Zhu

University of Oregon

Date Written: May 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, GDP growth, Inflation forecasts, panel data

Suggested Citation

Timmermann, Allan and Zhu, Yinchu, Comparing Forecasting Performance with Panel Data (May 2019). CEPR Discussion Paper No. DP13746, Available at SSRN:

Allan Timmermann (Contact Author)

UCSD ( email )

9500 Gilman Drive
La Jolla, CA 92093-0553
United States
858-534-0894 (Phone)


Centre for Economic Policy Research (CEPR)

United Kingdom

Yinchu Zhu

University of Oregon ( email )

1280 University of Oregon
Eugene, OR 97403
United States

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