THE DISTRIBUTION OF ROLLING REGRESSION ESTIMATORS

Zongwu Cai and Ted Juhl
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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 202013, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: We find the asymptotic distribution for rolling linear regression models various window widths. The limiting distribution depends on using the width of the rolling window, and on a “bias process†that is typically ignored in practice. Based on the 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 the how the confidence bands suggest alternative conclusions about the persistence of inflation.

Keywords: Asymptotic distribution; Bias correction; Nonparametric estimation; Rolling regressions; Time-varying parameters. (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 2020-08, Revised 2020-08
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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