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A New Test for Multiple Predictive Regression

Ke-Li Xu () and Junjie Guo ()
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Ke-Li Xu: Department of Economics, Indiana University
Junjie Guo: School of Finance, Central University of Finance and Economics, Beijing, China

CAEPR Working Papers from Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington

Abstract: We consider inference for predictive regressions with multiple predictors. Extant tests for 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 demonstrate the remarkable finite-sample performance regardless of numerosity of predictors and their persistence properties. Empirical application to equity premium predictability is provided.

Keywords: Curse of dimensionality; Lagrange-multipliers test; persistence; predictive regression; return predictability (search for similar items in EconPapers)
Pages: 53 pages
Date: 2021-12
New Economics Papers: this item is included in nep-ecm and nep-ets
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https://caepr.indiana.edu/RePEc/inu/caeprp/CAEPR2022-001.pdf (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:inu:caeprp:2022001

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