Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/309137 
Year of Publication: 
2024
Series/Report no.: 
Staff Reports No. 1132
Publisher: 
Federal Reserve Bank of New York, New York, NY
Abstract: 
We introduce a new regression diagnostic, tailored to time-series and panel-data regressions, which characterizes the sensitivity of the OLS estimate to distinct time-series variation at different frequencies. The diagnostic is built on the novel result that the eigenvectors of a random walk asymptotically orthogonalize a wide variety of time-series processes. Our diagnostic is based on leave-one-out OLS estimation on transformed variables using these eigenvectors. We illustrate how our diagnostic allows applied researchers to scrutinize regression results and probe for underlying fragility of the sample OLS estimate. We demonstrate the utility of our approach using a variety of empirical applications.
Subjects: 
leave-one-out frequency approach
regression diagnostic
relative contributions of different frequencies
high time-series persistence and spurious regressions
trigonometric basis functions
orthogonalization
JEL: 
C12
C13
C22
C23
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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