Tests of Conditional Predictive Ability

By White Halbert and Giacomini Raffaella
Econometrica (2006)

  • Raffaella Giacomini

    University College London



October 1, 2013

Last update

March 12, 2014










This code proposes a general framework for out-of-sample predictive ability testing and forecast selection when the model can be misspecified. It can be applied to different types of forecasts issued from both nested and non-nested models using different estimation techniques for a general loss function (chosen by the user). It accommodates both conditional and unconditional evaluation objectives. The null is H0: E( Loss(model A) - Loss(model B) )= 0. The sign of the test-statistics indicates which forecast performs better: a positive test-statistic indicates that model A forecast produces larger average loss than the model B forecast (model B outperforms model A), while a negative sign indicates the opposite.

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