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

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Author Info
Derek Bond () (University of Ulster)
Michael J. Harrison () (Department of Economics, Trinity College)
Edward J. O'Brien () (Department of Economics, Trinity College and Central Bank of Ireland)

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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.

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Paper provided by Trinity College Dublin, Department of Economics in its series Trinity Economics Papers with number tep20021.

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Length: 34 pages
Date of creation: Oct 2005
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Handle: RePEc:tcd:tcduee:tep20021

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
E41 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Demand for Money

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