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The long range dependence paradigm for macroeconomics and finance

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Author Info
Marc Henry () (Columbia University - Department of Economics)
Paolo Zaffaroni () (Imperial College London - Tanaka Business School)
Abstract

The long range dependence paradigm appears to be a suitable description of the data generating process for many observed economic time series. This is mainly due to the fact that it naturally characterizes time series displaying a high degree of persistence, in the form of a long lasting effect of unanticipated shocks, yet exhibiting mean reversion. Whereas linear long range dependent time series models have been extensively used in macroeconomics, empirical evidence from financial time series prompted the development of nonlinear long range dependent time series models, in particular models of changing volatility. We discuss empirical evidence of long range dependence as well as the theoretical issues, both for economics and econometrics, such evidence has stimulated.

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Paper provided by Columbia University, Department of Economics in its series Discussion Papers with number 0102-19.

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Length: 25 pages
Date of creation: 2002
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Handle: RePEc:clu:wpaper:0102-19

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  1. Laura Mayoral, 2005. "Is the observed persistence spurious? A test for fractional integration versus short memory and structural breaks," Economics Working Papers 956, Department of Economics and Business, Universitat Pompeu Fabra. [Downloadable!]
  2. Christian Macaro, 2007. "The Impact of Vintage on the Persistence of Gross Domestic Product Shocks," CEIS Research Paper 101, Tor Vergata University, CEIS. [Downloadable!]
  3. Gadea, Maria & Mayoral, Laura, 2005. "The Persistence of Inflation in OECD Countries: A Fractionally Integrated Approach," MPRA Paper 815, University Library of Munich, Germany. [Downloadable!]
    Other versions:
  4. Terence Tai-Leung Chong, 2007. "Estimating the Fractionally Integrated Model with a Break in the Differencing Parameter," Economics Bulletin, Economics Bulletin, vol. 3(67), pages 1-10. [Downloadable!]
  5. Katsumi Shimotsu, 2002. "Exact Local Whittle Estimation of Fractional Integration with Unknown Mean and Time Trend," Economics Discussion Papers 543, University of Essex, Department of Economics. [Downloadable!]
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  6. Laura Mayoral, 2006. "Minimum Distance Estimation of stationary and non-stationary ARFIMA Processes," Economics Working Papers 959, Department of Economics and Business, Universitat Pompeu Fabra. [Downloadable!]
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  7. Melvin Hinich & Terence Chong, 2007. "A Class Test for Fractional Integration," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 11(2), pages 1382-1382. [Downloadable!] (restricted)
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