A New Test for Multiple Predictive Regression*

Xu, Ke-Li

doi:10.1093/jjfinec/nbac030

A New Test for Multiple Predictive Regression*

2022-08-12

2

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AI

DOI：10.1093/jjfinec/nbac030

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We consider inference for predictive regressions with multiple predictors. Extant tests for predictability (especially for joint predictability) may perform unsatisfactorily and tend to discover spurious predictability as the number of predictors increases. We propose a battery of new instrumental variables-based tests which involve enforcement or partial enforcement of the null hypothesis in variance estimation. A test based on the few-predictors-at-a-time parsimonious system approach is recommended. Empirical Monte Carlos demonstrates the remarkable finite-sample performance regardless of numerosity of predictors and their persistence properties. Empirical application to equity premium predictability is provided.

Keyword:

curse of dimensionality

Lagrange-multipliers test

persistence

predictive regression

return predictability

A New Test for Multiple Predictive Regression*

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