Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/272972 
Year of Publication: 
2022
Series/Report no.: 
Working Paper No. 2022-02
Publisher: 
Bar-Ilan University, Department of Economics, Ramat-Gan
Abstract: 
The U.S. prewar output series exhibit smaller shock-persistence than postwar-series. Some studies suggest this may be due to linear interpolation used to generate missing prewar data. Monte Carlo simulations that support this view generate large standard-errors, making such inference imprecise. We assess analytically the effect of linear interpolation on a nonstationary process. We find that interpolation indeed reduces shock-persistence, but the interpolated series can still exhibit greater shock-persistence than a pure random walk. Moreover, linear interpolation makes the series periodically nonstationary, with parameters of the data generating process and the length of the interpolation time-segments affecting shock-persistence in conflicting ways.
Subjects: 
Linear Interpolation
Random Walk
Shock-Persistence
Nonstationary series
Periodic nonstationarity
Stationary series
Prewar US Time Series
JEL: 
C01
C02
E01
E30
N10
Document Type: 
Working Paper

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