Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research

Document Type

Article

Source of Publication

Computational Economics

Publication Date

8-11-2023

Abstract

This paper provides evidence on the use of Random Regression Forests (RRF) for optimal lag selection. Using an extended sample of 144 data series, of various data types with different frequencies and sample sizes, we perform optimal lag selection using RRF and compare the results with seven “traditional” information criteria as well as with three other machine learning approaches. We show that the different information criteria produce differing outcomes in terms of optimal lag selection. To quantify performance, we compare the forecast errors on autoregressive models using the optimal lags selected by the criteria and demonstrate that RRF outperforms other approaches. We provide suggestions to researchers as to which approach to use, under different combinations of data type/data frequency and data type/sample size.

ISSN

0927-9974

Publisher

Springer Science and Business Media LLC

First Page

1

Last Page

38

Disciplines

Business

Keywords

Random regression forest, Optimal lag, Lasso, Ridge regression, Nayesian model averaging

Indexed in Scopus

no

Open Access

no

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