Extending the Scope of Inference About Predictive Ability to Machine Learning Methods

EI seminar
Polak Building and autumn trees
Speaker
Juan Carlos Escanciano
Date
Thursday 16 May 2024, 12:00 - 13:00
Type
Seminar
Room
ET-14
Building
E Building
Location
Campus Woudestein
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Polak Building and autumn trees

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, see West (1996, Asymptotic Inference About Predictive Ability, Econometrica, 64. 1067-1084), 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 out-of-sample asymptotic inference with machine learning are: 

  • A zero mean condition for the score of the loss function
  • A fast rate of convergence for the machine learner

Monte Carlo simulations confirm our theoretical results. We illustrate the applicability of our results with a new out-of-sample test for the Martingale Difference Hypothesis (MDH). We argue that for the MDH problem, a "dense" approach is more suitable than a "sparsity" based approach. We obtain the asymptotic null distribution of our test and apply it to evaluate the MDH of some major daily exchange rates.

You can sign up for this seminar by sending an email to eb-secr@ese.eur.nl. The lunch will be provided (vegetarian option included).

Organiser

See also

Beyond Arbitrage: Deviations from Risk-Return

Benjamin Holcblat, (University of Luxembourg)

Variable aggregation in a Benders decomposition for the p-median problem

Rick Willemsen (Erasmus School of Economics)
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On changepoint detection in functional data using empirical energy distance

Lorenzo Trapani, (University of Leicester)

Saddlepoint techniques for the statistical analysis of time series

Davide La Vecchia (University of Geneva)
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The Anatomy of Machine Learning-Based Portfolio Performance

Christian Montes Schutte, Aarhus University
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More information

Do you want to know more about the event? Contact the secretariat Econometrics at eb-secr@ese.eur.nl.

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