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Cointegration Versus Spurious Regression In Heterogeneous Panels

  • Giovanni Urga
  • Lorenzo Trapani

We consider the issue of cross sectional aggregation in nonstationary, heterogeneous panels where each unit cointegrates. We first derive the asymptotic properties of the aggregate estimate, and a necessary and sufficient condition for cointegration to hold in the aggregate relationship. We also develop an estimation and testing framework to verify whether the condition is met. Secondly, we analyze the case when cointegration doesn't carry through the aggregation process, investigating whether a mild violation can still lead to an aggregate estimator that summarizes the micro relationships reasonably well. We derive the asymptotic measure of the degree of non cointegration of the aggregated estimate and we provide estimation and testing procedures. A Monte Carlo exercise evaluates the small sample properties of the estimator.

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Paper provided by Royal Economic Society in its series Royal Economic Society Annual Conference 2004 with number 74.

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Date of creation: 17 Sep 2004
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Handle: RePEc:ecj:ac2004:74
Contact details of provider: Postal: Office of the Secretary-General, School of Economics and Finance, University of St. Andrews, St. Andrews, Fife, KY16 9AL, UK
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  1. Peter C.B. Phillips & Joon Y. Park, 1986. "Statistical Inference in Regressions with Integrated Processes: Part 1," Cowles Foundation Discussion Papers 811R, Cowles Foundation for Research in Economics, Yale University, revised Aug 1987.
  2. Phillips, P. C. B. & Ouliaris, S., 1988. "Testing for cointegration using principal components methods," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 205-230.
  3. Andy Snell, . "Testing For R Versus R-1 Cointegrating Vectors," Discussion Papers 1995-10, Edinburgh School of Economics, University of Edinburgh.
  4. Peter C.B. Phillips, 1985. "Understanding Spurious Regressions in Econometrics," Cowles Foundation Discussion Papers 757, Cowles Foundation for Research in Economics, Yale University.
  5. Clive W. J. Granger, 1988. "Aggregation of time series variables-a survey," Discussion Paper / Institute for Empirical Macroeconomics 1, Federal Reserve Bank of Minneapolis.
  6. Peter C.B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Cowles Foundation Discussion Papers 1222, Cowles Foundation for Research in Economics, Yale University.
  7. Hall, Stephen & Lazarova, Stepana & Urga, Giovanni, 1999. " A Principal Components Analysis of Common Stochastic Trends in Heterogeneous Panel Data: Some Monte Carlo Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 749-67, Special I.
  8. Peter C.B. Phillips & Joon Y. Park, 1986. "Statistical Inference in Regressions with Integrated Processes: Part 2," Cowles Foundation Discussion Papers 819R, Cowles Foundation for Research in Economics, Yale University, revised Feb 1987.
  9. Harris, D., 1996. "Principal Components Analysis of Cointegrated Time Series," Monash Econometrics and Business Statistics Working Papers 2/96, Monash University, Department of Econometrics and Business Statistics.
  10. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
  11. Lazarov , tep na & Trapani, Lorenzo & Urga, Giovanni, 2007. "Common Stochastic Trends And Aggregation In Heterogeneous Panels," Econometric Theory, Cambridge University Press, vol. 23(01), pages 89-105, February.
  12. Granger, C. W. J., 1993. "Implications of seeing economic variables through an aggregation window," Ricerche Economiche, Elsevier, vol. 47(3), pages 269-279, September.
  13. Gonzalo, J., 1992. "Cointegration and Aggregation," Papers 11, Boston University - Department of Economics.
  14. Ghose, Devajyoti, 1995. "Linear aggregation in cointegrated systems," Journal of Economic Dynamics and Control, Elsevier, vol. 19(5-7), pages 1011-1032.
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