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Periodic properties of interpolated time series

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  • Dezhbakhsh, Hashem
  • Levy, Daniel
Abstract
Although linearly interpolated series are often used in economics, little has been done to examine the effects of interpolation on time series properties and on statistical inference. We show that linear interpolation of a trend stationary series superimposes a 'periodic' structure on the moments of the series. Using conventional time series methods to make inference about the interpolated series may therefore be invalid. Also, the interpolated series may exhibit more shock persistence than the original trend stationary series.
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  • Dezhbakhsh, Hashem & Levy, Daniel, 1994. "Periodic properties of interpolated time series," Economics Letters, Elsevier, vol. 44(3), pages 221-228.
    • Handle: RePEc:eee:ecolet:v:44:y:1994:i:3:p:221-228
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    Cited by:

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    2. Franses, Philip Hans, 2013. "Data revisions and periodic properties of macroeconomic data," Economics Letters, Elsevier, vol. 120(2), pages 139-141.
    3. Olivier Darné & Amélie Charles, 2012. "A note on the uncertain trend in US real GNP: Evidence from robust unit root tests," Economics Bulletin, AccessEcon, vol. 32(3), pages 2399-2406.
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    5. Daniel Levy & Haiwei Chen, 1994. "Estimates Of The Aggregate Quarterly Capital Stock For The Post‐War U.S. Economy," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 40(3), pages 317-349, September.
    6. Dezhbakhsh, Hashem & Levy, Daniel, 2022. "Interpolation and shock persistence of prewar U.S. macroeconomic time series: A reconsideration," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 213.
    7. Matthew Higgins & Daniel Levy & Andrew T. Young, 2003. "Growth and Convergence across the US: Evidence from County-Level Data," Working Papers 2003-03, Bar-Ilan University, Department of Economics.
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