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Bandspectrum Cointegration

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Abstract

Economic theory commonly distinguishes between different time horizons such as the short run and the long run, each with its own relationships and its own dynamics. Engle (1974) proposed a bandspectrum regression to estimate such models. This paper proposes a new estimator for non-stationary panel data models, a bandspectrum cointegration estimator. The bandspectrum cointegration estimator uses first differenced data to avoid spurious results. Such estimates are, however, less efficient than estimates from a model with non-stationary data. Still, simulation results in the paper show that the bandspectrum cointegration estimator is more efficient than common time domain estimators, for example VECM and OLS levels estimators, if the data generating process contains more than one time horizon. The BSCE furthermore identifies all horizons in the data generating process and estimates an individual parameter vector for each, a property that neither time domain estimator possesses.

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Bibliographic Info

Paper provided by Lund University, Department of Economics in its series Working Papers with number 2008:18.

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Length: 34 pages
Date of creation: 02 Dec 2008
Date of revision:
Handle: RePEc:hhs:lunewp:2008_018

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Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund,Sweden
Phone: +46 +46 222 0000
Fax: +46 +46 2224613
Web page: http://www.nek.lu.se/en
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Keywords: Cointegration; Bandspectrum Regression; Simulations; Wavelets; Frequency domain;

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  1. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
  2. D Marinucci & Peter M. Robinson, 1998. "Semiparametric frequency domain analysis of fractional cointegration," LSE Research Online Documents on Economics 2258, London School of Economics and Political Science, LSE Library.
  3. Milton Friedman & Simon Kuznets, 1954. "Income from Independent Professional Practice," NBER Books, National Bureau of Economic Research, Inc, number frie54-1, October.
  4. Englund, P. & Persson, T. & Svensson, L.E.O., 1990. "Swedish Business Cyscles: 1861-1988," Papers 1990r, Uppsala - Working Paper Series.
  5. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
  6. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-80, November.
  7. Peter C.B. Phillips, 1999. "Discrete Fourier Transforms of Fractional Processes," Cowles Foundation Discussion Papers 1243, Cowles Foundation for Research in Economics, Yale University.
  8. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  9. Patrick M. Crowley, 2007. "A Guide To Wavelets For Economists ," Journal of Economic Surveys, Wiley Blackwell, vol. 21(2), pages 207-267, 04.
  10. Ramsey, James B. & Lampart, Camille, 1998. "Decomposition Of Economic Relationships By Timescale Using Wavelets," Macroeconomic Dynamics, Cambridge University Press, vol. 2(01), pages 49-71, March.
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