Partial Sample Regressions

Posted: 20 Nov 2019

See all articles by Megan Czasonis

Megan Czasonis

State Street Corporate

Mark Kritzman

Windham Capital Management

David Turkington

State Street Associates

Date Written: November 13, 2019

Abstract

Financial analysts assume that the reliability of predictions derived from regression analysis improves with sample size. This is generally true because larger samples tend to produce less noisy results than smaller samples. But this is not always the case. Some observations are more relevant than others, and it is often the case that one can obtain more reliable predictions by censoring observations that are not sufficiently relevant. The authors introduce a methodology for identifying relevant observations by recasting the prediction of a regression equation as a weighted average of the historical values of the dependent variable in which the weights are the relevance of the independent variables. This equivalence allows them to use a subset of more relevant observations to forecast the dependent variable. The authors apply their methodology to forecast factor returns from economic variables.

Keywords: Event-driven observations, Informativeness, Kernel smoothing, Mahalanobis distance, Multivariate similarity, Nadaraya-Watson kernel regression, Ordinary Least Squares, Regression analysis, Relevance, Relevance-weighted average

JEL Classification: C00, C01, C02, C10, C13, C18, C22, C24, C40, C50, C53, G00, G10 ,G17

Suggested Citation

Czasonis, Megan and Kritzman, Mark and Turkington, David, Partial Sample Regressions (November 13, 2019). MIT Sloan Research Paper No. 5894-19, Available at SSRN: https://ssrn.com/abstract=3489520 or http://dx.doi.org/10.2139/ssrn.3489520

Megan Czasonis

State Street Corporate ( email )

1 Lincoln Street
Boston, MA 02111
United States

Mark Kritzman (Contact Author)

Windham Capital Management ( email )

800 Boylston Street
30th Floor
Boston, MA 02199
United States
6174193900 (Phone)
6172365034 (Fax)

David Turkington

State Street Associates ( email )

United States

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