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 Revised October 2026

JEL classification: C13, C22, C23, C52, E32

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 builds on a new result showing how to transform many common types of time series into approximately uncorrelated observations without specifying a model for their dependence. Our diagnostic can be calculated simply as leave-one-out OLS estimation on a set of transformed variables. Using a variety of empirical applications, we show how the diagnostic helps applied researchers identify the sources of variation that inform their regression estimates.

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, revised October 2026. https://doi.org/10.59576/sr.1132

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