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Measuring Conditional Persistence in Nonlinear Time Series

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  • George Kapetanios
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    Abstract

    The persistence properties of economic time series have been a primary object of investigation in a variety of guises since the early days of econometrics. Recently, work on nonlinear modelling for time series has introduced the idea that persistence of a shock at a point in time may vary depending on the state of the process at that point in time. This article suggests investigating the persistence of processes conditioning on their history as a tool that may aid parametric nonlinear modelling. In particular, we suggest that examining the nonparametrically estimated derivatives of the conditional expectation of a variable with respect to its lag(s) may be a useful indicator of the variation in persistence with respect to its past history. We discuss in detail the implementation of the measure and present a Monte Carlo investigation. We further apply the persistence analysis to real exchange rates. Copyright 2007 Blackwell Publishing Ltd and the Department of Economics, University of Oxford.

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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1468-0084.2006.00437.x
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    Bibliographic Info

    Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.

    Volume (Year): 69 (2007)
    Issue (Month): 3 (06)
    Pages: 363-386

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    Handle: RePEc:bla:obuest:v:69:y:2007:i:3:p:363-386

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    Cited by:
    1. Villani, Mattias & Kohn, Robert & Giordani, Paolo, 2009. "Regression density estimation using smooth adaptive Gaussian mixtures," Journal of Econometrics, Elsevier, vol. 153(2), pages 155-173, December.
    2. Fernandez, Viviana, 2010. "Commodity futures and market efficiency: A fractional integrated approach," Resources Policy, Elsevier, vol. 35(4), pages 276-282, December.

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