A Simple Diagnostic for Time-Series and Panel-Data Regressions

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Staff Reports

A Simple Diagnostic for Time-Series and Panel-Data Regressions

Number 1132
October 2024

JEL classification: C12, C13, C22, C23

Authors: Richard K. Crump, Nikolay Gospodinov, and Ignacio Lopez Gaffney

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.

Full Article

Author Disclosure Statement(s)

Richard K. Crump
I declare that I have no relevant or material financial interests that relate to the research described in my paper entitled “A Simple Diagnostic for Time-Series and Panel-Data Regressions,” joint with Nikolay Gospodinov and Ignacio Lopez Gaffney.

Nikolay Gospodinov
I declare that I have no relevant or material financial interests that relate to the research described in my paper entitled “A Simple Diagnostic for Time-Series and Panel-Data Regressions,” joint with Richard Crump and Ignacio Lopez Gaffney.

Ignacio Lopez Gaffney
I declare that I have no relevant or material financial interests that relate to the research described in my paper entitled “A Simple Diagnostic for Time-Series and Panel-Data Regressions,” joint with Richard Crump and Nikolay Gospodinov.

Suggested Citation:
Crump, Richard K., Nikolay Gospodinov, and Ignacio Lopez Gaffney. 2024. “A Simple Diagnostic for Time-Series and Panel-Data Regressions.” Federal Reserve Bank of New York Staff Reports, no. 1132, October. https://doi.org/10.59576/sr.1132

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