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.
| Original language | English |
|---|---|
| Article number | 110386 |
| Journal | Economics Letters |
| Volume | 213 |
| DOIs | |
| State | Published - Apr 2022 |
Bibliographical note
Publisher Copyright:© 2022 Elsevier B.V.
Funding
We thank an anonymous reviewer for comments and the editor Eric Young for advice. Louis Johnston was very helpful in clarifying our inquiries about the use of linear interpolation in constructing historical time series. We are responsible for any remaining errors.
Keywords
- Linear interpolation
- Nonstationary series
- Periodic nonstationarity
- Prewar US time series
- Random walk
- Shock-persistence
- Stationary series