THE DISTRIBUTION OF ROLLING REGRESSION ESTIMATORS
Zongwu Cai and
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Zongwu Cai: Department of Economics, The University of Kansas, Lawrence, KS 66045, USA
Ted Juhl: School of Business, The University of Kansas, Lawrence, KS 66045, USA
No 202218, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics
We establish the asymptotic distribution for rolling linear regression models using various window widths. The limiting distribution depends on the width of the rolling window and on a "bias process" that is typically ignored in practice. Based on the asymptotic distribution, we tabulate critical values used to find uniform confidence intervals for the average values of regression parameters over the windows. We propose a corrected rolling regression technique that removes the bias process by rolling over smoothed parameter estimates. The procedure is illustrated using a series of Monte Carlo experiments. The paper includes an empirical example to show how the confidence bands suggest alternative conclusions about the persistence of inflation.
Keywords: Parameter instability; Nonparametric estimation; Rolling regressions; Uniform confidence intervals; Nonstationary (search for similar items in EconPapers)
JEL-codes: C14 C22 C5 (search for similar items in EconPapers)
Date: 2020-08, Revised 2022-12
New Economics Papers: this item is included in nep-big
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Persistent link: https://EconPapers.repec.org/RePEc:kan:wpaper:202218
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