Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/251467 
Year of Publication: 
2022
Citation: 
[Journal:] Economics Letters [ISSN:] 0165-1765 [Volume:] 213 [Article No.:] 110386 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2022
Publisher: 
Elsevier, Amsterdam
Abstract: 
The U.S. prewar output series exhibit smaller shock-persistence than postwar-series. Some studies suggest that 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 Time Series
Periodic Nonstationarity
Stationary Time Series
Prewar US Time Series
Prewar vs Postwar Business Cycles
JEL: 
C01
C02
E01
E30
N10
Published Version’s DOI: 
Document Type: 
Article
Document Version: 
Manuscript Version (Preprint)
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