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Estimating persistence for irregularly spaced historical data

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  • Franses, Ph.H.B.F.

Abstract

This paper introduces to the literature on Economic History a measure of persistence which is particularly useful if the data are irregularly spaced. An illustration to 10 historical unevenly spaced data series for Holland of 1738 to 1779 showed the merits of the methodology

Suggested Citation

  • Franses, Ph.H.B.F., 2019. "Estimating persistence for irregularly spaced historical data," Econometric Institute Research Papers EI2020-03, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:124352
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    References listed on IDEAS

    as
    1. van Zanden, Jan Luiten & van Leeuwen, Bas, 2012. "Persistent but not consistent: The growth of national income in Holland 1347–1807," Explorations in Economic History, Elsevier, vol. 49(2), pages 119-130.
    2. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    3. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911, November.
    4. Robinson, P. M., 1977. "Estimation of a time series model from unequally spaced data," Stochastic Processes and their Applications, Elsevier, vol. 6(1), pages 9-24, November.
    5. Eltis, David & Engerman, Stanley L., 2000. "The Importance of Slavery and the Slave Trade to Industrializing Britain," The Journal of Economic History, Cambridge University Press, vol. 60(1), pages 123-144, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Irregularly spaced time series; Economic history; First order autoregression; Persistence;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • N01 - Economic History - - General - - - Development of the Discipline: Historiographical; Sources and Methods

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