Interpolation and Prewar-Postwar Output Volatility and Shock-Persistence Debate: A Closer Look and New Results

TL;DR

This paper challenges the common belief that data interpolation artificially reduced prewar output volatility and shock persistence, showing instead that it increased shock persistence and decreased volatility, implying the true differences are larger than previously thought.

Contribution

It provides new analytical insights demonstrating that linear interpolation affects shock persistence and volatility in the opposite direction, refining understanding of historical output series differences.

Findings

Linear interpolation increases shock persistence in series.

Interpolation reduces observed volatility of output.

Actual prewar-postwar differences are likely greater than previously estimated.

Abstract

It is well established that the US prewar output was more volatile and less shock persistent than the postwar output. This is often attributed to the data interpolation employed to construct the prewar series. Our analytical results, however, indicate that commonly used linear interpolation has the opposite effect on shock persistence and volatility of a series - it increases shock persistence and reduces volatility. The surprising implication of this finding is that the actual differences between the volatility and shock persistence of the prewar and postwar output series are likely greater than the existing literature recognizes, and interpolation has dampened rather than magnified this difference. Consequently, the view that postwar output was more stable than prewar output because of the effectiveness of the postwar stabilization policies and institutional changes has considerable merit. Our results hold for parsimonious stationary and nonstationary time series commonly used to model macroeconomic time series