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Cointegration Versus Spurious Regression In Heterogeneous Panels

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  • Giovanni Urga
  • Lorenzo Trapani

Abstract

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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Suggested Citation

  • Giovanni Urga & Lorenzo Trapani, 2004. "Cointegration Versus Spurious Regression In Heterogeneous Panels," Royal Economic Society Annual Conference 2004 74, Royal Economic Society.
    • Handle: RePEc:ecj:ac2004:74
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    1. Park, Joon Y. & Phillips, Peter C.B., 1989. "Statistical Inference in Regressions with Integrated Processes: Part 2," Econometric Theory, Cambridge University Press, vol. 5(1), pages 95-131, April.
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    7. Stephen Hall & Stepana Lazarova & Giovanni Urga, 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(S1), pages 749-767, November.
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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