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Demand for Money: A Study in Testing Time Series for Long Memory and Nonlinearity

Author

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  • Derek Bond

    (University of Ulster)

  • Michael J. Harrison

    (Trinity College Dublin)

  • Edward J. O'Brien

    (European Central Bank)

Abstract

This paper draws attention to the limitations of the standard unit root/cointegration approach to economic and financial modelling, and to some of the alternatives based on the idea of fractional integration, long memory models, and the random field regression approach to nonlinearity. Following brief explanations of fractional integration and random field regression, and the methods of applying them, selected techniques are applied to a demand for money dataset. Comparisons of the results from this illustrative case study are presented, and conclusions are drawn that should aid practitioners in applied time-series econometrics.

Suggested Citation

  • Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2007. "Demand for Money: A Study in Testing Time Series for Long Memory and Nonlinearity," The Economic and Social Review, Economic and Social Studies, vol. 38(1), pages 1-24.
    • Handle: RePEc:eso:journl:v:38:y:2007:i:1:p:1-24
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    References listed on IDEAS

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    3. Gerlach-Kristen, Petra & O'Connell, Brian & O'Toole, Conor, 2013. "How do banking crises affect aggregate consumption? Evidence from international crisis episodes," Papers WP464, Economic and Social Research Institute (ESRI).

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