Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258756 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 15 [Issue:] 1 [Article No.:] 32 [Publisher:] MDPI [Place:] Basel [Year:] 2022 [Pages:] 1-13
Publisher: 
MDPI, Basel
Abstract: 
Quantitative researchers often use Student's t-test (and its p-values) to claim that a particular regressor is important (statistically significantly) for explaining the variation in a response variable. A study is subject to the p-hacking problem when its author relies too much on formal statistical significance while ignoring the size of what is at stake. We suggest reporting estimates using nonlinear kernel regressions and the standardization of all variables to avoid p-hacking. We are filling an essential gap in the literature because p-hacking-related papers do not even mention kernel regressions or standardization. Although our methods have general applicability in all sciences, our illustrations refer to risk management for a cross-section of firms and financial management in macroeconomic time series. We estimate nonlinear, nonparametric kernel regressions for both examples to illustrate the computation of scale-free generalized partial correlation coefficients (GPCCs). We suggest supplementing the usual p-values by 'practical significance' revealed by scale-free GPCCs. We show that GPCCs also yield new pseudo regression coefficients to measure each regressor's relative (nonlinear) contribution in a kernel regression.
Subjects: 
kernel regression
standardized beta coefficients
partial correlation
JEL: 
C30
C51
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.