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(Abstract
for 1996 paper available below. Full Research Working Papers can be ordered at no charge.)
ABSTRACT
This paper uses Monte Carlo experiments to examine the small-sample properties of some
commonly used tests of equal forecast accuracy. The study pays particular attention to
test power, evaluated using both asymptotic and empirical critical values. In addition to
evaluating different tests, this paper evaluates the performance of different methods of
determining the bandwidth used in computing autocorrelation-consistent test statistics.
The simulation results show that tests of equal forecast accuracy have somewhat inflated
size and modest or even low power. Moreover, the performances of the different tests and
the bandwidth selection criteria are broadly similar.
JEL Nos.: C53, C12, C52.
Key words: Forecast evaluation; Granger causality; Size; Power
Todd E. Clark is a senior economist at the Federal Reserve Bank of Kansas City. He
gratefully acknowledges the research assistance of Mangal Goswami and the helpful comments
of Charles Engel, Sharon Kozicki, Ken West, and seminar participants at the Federal
Reserve Bank of Kansas City. The views expressed herein are solely those of the author and
do not necessarily reflect the views of the Federal Reserve Bank of Kansas City or the
Federal Reserve System. E-mail: todd.clark@kc.frb.org.
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