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Bootstrapping the log-periodogram regression

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  • Arteche, J.
  • Orbe, J.

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Bibliographic Info

Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 86 (2005)
Issue (Month): 1 (January)
Pages: 79-85

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Handle: RePEc:eee:ecolet:v:86:y:2005:i:1:p:79-85

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Web page: http://www.elsevier.com/locate/ecolet

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  1. Andersson, Michael K. & Gredenhoff, Mikael P., 1998. "Robust Testing for Fractional Integration Using the Bootstrap," Working Paper Series in Economics and Finance 218, Stockholm School of Economics.
  2. Sowell, Fallaw, 1992. "Modeling long-run behavior with the fractional ARIMA model," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 277-302, April.
  3. Gil-Alana, L. A. & Robinson, P. M., 1997. "Testing of unit root and other nonstationary hypotheses in macroeconomic time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 241-268, October.
  4. Andersson, Michael K. & Gredenhoff, Mikael P., 1997. "Bootstrap Testing for Fractional Integration," Working Paper Series in Economics and Finance 188, Stockholm School of Economics.
  5. Diebold, Francis X. & Rudebusch, Glenn D., 1989. "Long memory and persistence in aggregate output," Journal of Monetary Economics, Elsevier, vol. 24(2), pages 189-209, September.
  6. Donald W.K. Andrews & Offer Lieberman, 2002. "Higher-order Improvements of the Parametric Bootstrap for Long-memory Gaussian Processes," Cowles Foundation Discussion Papers 1378, Cowles Foundation for Research in Economics, Yale University.
  7. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
  8. Haubrich, Joseph G, 1993. "Consumption and Fractional Differencing: Old and New Anomalies," The Review of Economics and Statistics, MIT Press, vol. 75(4), pages 767-72, November.
  9. Diebold, Francis X & Rudebusch, Glenn D, 1991. "Is Consumption Too Smooth? Long Memory and the Deaton Paradox," The Review of Economics and Statistics, MIT Press, vol. 73(1), pages 1-9, February.
  10. Andersson, Michael K. & Gredenhoff, Mikael P., 2000. "Improving Fractional Integration Tests With Bootstrap Distributions," Working Paper 74, National Institute of Economic Research.
  11. Velasco, Carlos, 1999. "Non-stationary log-periodogram regression," Journal of Econometrics, Elsevier, vol. 91(2), pages 325-371, August.
  12. Hassler, Uwe & Wolters, Jurgen, 1995. "Long Memory in Inflation Rates: International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 37-45, January.
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Cited by:
  1. repec:rdg:wpaper:em-dp2013-02 is not listed on IDEAS
  2. Beran, Jan & Shumeyko, Yevgen, 2012. "Bootstrap testing for discontinuities under long-range dependence," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 322-347.
  3. Gerolimetto, Margherita, 2006. "Frequency domain bootstrap for the fractional cointegration regression," Economics Letters, Elsevier, vol. 91(3), pages 389-394, June.
  4. Berenguer-Rico, Vanessa & Gonzalo, Jesús, 2014. "Summability of stochastic processes—A generalization of integration for non-linear processes," Journal of Econometrics, Elsevier, vol. 178(P2), pages 331-341.
  5. Franco, Glaura C. & Reisen, Valderio A., 2007. "Bootstrap approaches and confidence intervals for stationary and non-stationary long-range dependence processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 546-562.
  6. Vanessa Berenguer Rico & Jesus Gonzalo, 2011. "Summability of stochastic processes: a generalization of integration and co-integration valid for non-linear processes," Economics Working Papers we1115, Universidad Carlos III, Departamento de Economía.
  7. Saeed Heravi & Kerry Patterson, 2013. "Log-Periodogram Estimation of the Long-Memory Parameter: An Evaluation of Competing Estimators," Economics & Management Discussion Papers em-dp2013-02, Henley Business School, Reading University.
  8. Arteche, Josu & Orbe, Jesus, 2009. "Using the bootstrap for finite sample confidence intervals of the log periodogram regression," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1940-1953, April.

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