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Tests of Equal Forecast Accuracy and Encompassing for Nested Models

  • Todd E. Clark

    (Federal Reserve Bank of Kansas City)

  • Michael W. McCracken

    (Louisiana State University)

We examine the asymptotic and finite-sample properties of tests for equal forecast accuracy and encompassing applied to 1-step ahead forecasts from nested parametric models. We first derive the asymptotic distributions of two standard tests and one new test of encompassing. Tables of asymptotically valid critical values are provided. Monte Carlo methods are then used to evaluate the size and power of the tests of equal forecast accuracy and encompassing. The simulations indicate that post-sample tests can be reasonably well sized. Of the post-sample tests considered, the encompassing test proposed in this paper is the most powerful. We conclude with an empirical application regarding the predictive content of unemployment for inflation.

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Paper provided by Econometric Society in its series Econometric Society World Congress 2000 Contributed Papers with number 0319.

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Date of creation: 01 Aug 2000
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Handle: RePEc:ecm:wc2000:0319
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