A0259
Title: Extending the scope of inference about predictive ability to machine learning methods
Authors: Juan Carlos Escanciano - Universidad Carlos III de Madrid (Spain) [presenting]
Abstract: Though out-of-sample forecast evaluation is routinely recommended with modern machine learning methods, and there exists a well-established classic inference theory for predictive ability, such theory is not directly applicable to modern machine learners such as the Lasso in the high dimensional setting. We investigate under which conditions such extensions are possible. Two key properties for standard asymptotic inference are: (i) a zero mean condition for the score of the loss function (a locally robust property); and (ii) a fast rate of convergence for the machine learner.
