Federal Reserve Bank of St. Louis
Tests of Equal Accuracy for Nested Models with Estimated Factors
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.
Cite this item
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.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
Keywords: factor model; out-of-sample forecasts; recursive estimation
This item with handle RePEc:fip:fedlwp:2015-025
is also listed on EconPapers
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