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Federal Reserve Bank of St. Louis
Working Papers
Tests of Equal Accuracy for Nested Models with Estimated Factors
Silvia Goncalves
Michael W. McCracken
Benoit Perron
Abstract

In this paper we develop asymptotics for tests of equal predictive ability between nested models when factor-augmented regression models are used to forecast. We provide conditions under which the estimation of the factors does not affect the asymptotic distributions developed in Clark and McCracken (2001) and McCracken (2007). This enables researchers to use the existing tabulated critical values when conducting inference. As an intermediate result, we derive the asymptotic properties of the principal components estimator over recursive windows. We provide simulation evidence on the finite sample effects of factor estimation and apply the tests to the case of forecasting excess returns to the S&P 500 Composite Index.


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Silvia Goncalves & Michael W. McCracken & Benoit Perron, Tests of Equal Accuracy for Nested Models with Estimated Factors, Federal Reserve Bank of St. Louis, Working Papers 2015-25, 14 Sep 2015, revised 29 Aug 2016.
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Keywords: factor model; out-of-sample forecasts; recursive estimation
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