Partial Sample Regressions
Posted: 20 Nov 2019
Date Written: November 13, 2019
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
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