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A Simple Diagnostic for Time-Series and Panel-Data Regressions
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 ...
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A New Jackknife Variance Estimator for Time-Series and Panel Regressions
We introduce a new jackknife variance estimator for time-series and panel-data regressions. The novelty in our approach is that we first rotate the data using a particular choice of trigonometric basis functions. This rotation removes serial correlation in a broad class of time-series processes, including random walks, and enables the use of the conventional leave-one-out jackknife on the transformed space of the regressors and residuals. The procedure is tuning-parameter free and naturally adapts to the degree of persistence of the data. We prove the asymptotic validity of our variance ...