Tests of Equal Accuracy for Nested Models with Estimated Factors

Silvia Goncalves (), Michael McCracken and Benoit Perron ()

No 2015-25, Working Papers from Federal Reserve Bank of St. Louis

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.

Keywords: factor models; out-of-sample forecasts; recursive estimation (search for similar items in EconPapers)
JEL-codes: C12 C32 C38 C52 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2015-09-14
New Economics Papers: this item is included in nep-ecm and nep-for
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Citations: View citations in EconPapers (2)

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Journal Article: Tests of equal accuracy for nested models with estimated factors (2017)
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DOI: 10.20955/wp.2015.025

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