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Periodic properties of interpolated time series

Author

Listed:
  • Hashem Dezhbakhsh

    (Emory University [Atlanta, GA])

  • Daniel Levy

    (Bar-Ilan University [Israël], Emory University [Atlanta, GA], RCEA - Rimini Center for Economic Analysis)

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.

Suggested Citation

  • Hashem Dezhbakhsh & Daniel Levy, 1994. "Periodic properties of interpolated time series," Post-Print hal-02382750, HAL.
  • Handle: RePEc:hal:journl:hal-02382750
    DOI: 10.1016/0165-1765(93)00378-2
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-02382750
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    References listed on IDEAS

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    Cited by:

    1. Young, Andrew T. & Higgins, Matthew J. & Levy, Daniel, 2013. "Heterogeneous Convergence," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 238-241.
    2. Levy, Daniel & Chen, Haiwei, 1994. "Estimates of the Aggregate Quarterly Capital Stock for the Post-War U.S. Economy," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 317-349.
    3. Franses, Philip Hans, 2013. "Data revisions and periodic properties of macroeconomic data," Economics Letters, Elsevier, vol. 120(2), pages 139-141.
    4. 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.
    5. Michael Ehrmann, 2000. "Comparing monetary policy transmission across European countries," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 136(1), pages 58-83, March.
    6. Daniel Levy, 2000. "Investment-Saving Comovement and Capital Mobility: Evidence from Century Long U.S. Time Series," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 3(1), pages 100-137, January.
    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.
    8. Matthew J. Higgins & Donald J. Lacombe & Briana S. Stenard & Andrew T. Young, 2021. "Evaluating the effects of Small Business Administration lending on growth," Small Business Economics, Springer, vol. 57(1), pages 23-45, June.
    9. Orlando, Giuseppe & Zimatore, Giovanna, 2018. "Recurrence quantification analysis of business cycles," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 82-94.

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    More about this item

    Keywords

    Linear Interpolation; Trend-Stationary Series; Shock Persistence; Periodic Properties of Time Series;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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