Machine learning econometrics / Philippe Goulet Coulombe.

Format:

Book

Thesis/Dissertation

Author/Creator:

Coulombe, Philippe Goulet, author.

Contributor:

Diebold, Francis X., 1959- degree supervisor.

Schorfheide, Frank, degree supervisor.

University of Pennsylvania. Department of Economics, degree granting institution.

Language:

English

Subjects (All):

Statistics.

Computer science.

Artificial intelligence.

Economics--Penn dissertations.

Penn dissertations--Economics.

Local Subjects:

Statistics.

Computer science.

Artificial intelligence.

Economics--Penn dissertations.

Penn dissertations--Economics.

Genre:

Academic theses.

Physical Description:

1 online resource (174 pages)

Contained In:

Dissertations Abstracts International 82-12B.

Place of Publication:

[Philadelphia, Pennsylvania] : University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2021.

Language Note:

English

System Details:

Mode of access: World Wide Web.

text file

Summary:

Much of econometrics is based on a tight probabilistic approach to empirical modeling that dates back to Haavelmo (1944). This thesis explores a modern algorithmic view, and by doing so, finds solutions to classic problems while developing new avenues. In the first chapter, Kalman-filter based computations of random walk coefficients are replaced by a closed-form solution only second to least squares in the pantheon of simplicity. In the second chapter, random walk "drifting" coefficients are themselves dismissed. Rather, evolving coefficients are modeled and forecasted with a powerful machine learning algorithm. Conveniently, this generalization of time-varying parameters provides statistical efficiency and interpretability, which off-the-shelf machine learning algorithms cannot easily offer. The third chapter is about the to the fundamental problem of detecting at which point a learner stops learning and starts imitating. It answers "why can't Random Forest overfit?" The phenomenon is shown to be a surprising byproduct of randomized "greedy" algorithms - often deployed in the face of computational adversity. Then, the insights are utilized to develop new high-performing non-overfitting algorithms.

Notes:

Source: Dissertations Abstracts International, Volume: 82-12, Section: B.

Advisors: Diebold, Francis X.; Schorfheide, Frank.

Department: Economics.

Ph.D. University of Pennsylvania 2021.

Local Notes:

School code: 0175

ISBN:

9798738617959

Access Restriction:

Restricted for use by site license.

This item must not be sold to any third party vendors.