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The long range dependence paradigm for macroeconomics and finance

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  • 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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Bibliographic Info

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
Date of revision:
Handle: RePEc:clu:wpaper:0102-19

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Cited by:
  1. Katsumi Shimotsu, 2006. "Gaussian Semiparametric Estimation of Multivariate Fractionally Integrated Processes," Working Papers 1062, Queen's University, Department of Economics.
  2. Laura Mayoral, 2001. "A New Minimum Distance Estimation Procedure of ARFIMA Processes," Working Papers 100, Barcelona Graduate School of Economics.
  3. Shimotsu, Katsumi, 2010. "Exact Local Whittle Estimation Of Fractional Integration With Unknown Mean And Time Trend," Econometric Theory, Cambridge University Press, vol. 26(02), pages 501-540, April.
  4. Marcel Aloy & Gilles de Truchis, 2012. "Estimation and Testing for Fractional Cointegration," AMSE Working Papers 1215, Aix-Marseille School of Economics, Marseille, France.
  5. Daniela Osterrieder, 2013. "Interest Rates with Long Memory: A Generalized Affine Term-Structure Model," CREATES Research Papers 2013-17, School of Economics and Management, University of Aarhus.
  6. Per Frederiksen & Frank S. Nielsen, 2008. "Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood," CREATES Research Papers 2008-59, School of Economics and Management, University of Aarhus.
  7. Terence Tai-Leung Chong, 2007. "Estimating the Fractionally Integrated Model with a Break in the Differencing Parameter," Economics Bulletin, AccessEcon, vol. 3(67), pages 1-10.
  8. Laura Mayoral, 2006. "Is the Observed Persistence Spurious? A Test for Fractional Integration versus Short Memory and Structural Breaks," Working Papers 260, Barcelona Graduate School of Economics.
  9. Laura Mayoral, 2003. "Further Evidence on the Uncertain (Fractional) Unit Root in Real GNP," Working Papers 82, Barcelona Graduate School of Economics.
  10. Laura Mayoral, 2005. "The Persistence of Inflation in OECD Countries:a Fractionally Integrated Approach," Working Papers 259, Barcelona Graduate School of Economics.
  11. Rodrigo Mariscal & Andrew Powell, 2012. "Forecasting Inflation Risks in Latin America: A Technical Note," Research Department Publications 4785, Inter-American Development Bank, Research Department.
  12. Uwe Hassler & Paulo M.M. Rodrigues & Antonio Rubia, 2012. "Quantile regression for long memory testing: A case of realized volatility," Working Papers w201207, Banco de Portugal, Economics and Research Department.

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