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Spurious Regression and Trending Variables

Author

Listed:
  • Antonio E. Noriega

    () (Department of Economics and Finance, Universidad de Guanajuato)

  • Daniel Ventosa-Santaularia

    () (Department of Economics and Finance, Universidad de Guanajuato)

Abstract

This paper analyses the asymptotic and finite sample implications of different types of nonstationary behavior among the dependent and explanatory variables in a linear spurious regression model. We study cases when the nonstationarity in the dependent and explanatory variables is deterministic as well as stochastic. In particular, we derive the order in probability of the t-statistic in a linear regression equation under a variety of empirically relevant data generation processes, and show that he spurious regression phenomenon is present in all cases considered, when at least one of the variables behaves in a nonstationary way. Simulation experiments confirm our asymptotic results.

Suggested Citation

  • Antonio E. Noriega & Daniel Ventosa-Santaularia, 2006. "Spurious Regression and Trending Variables," Department of Economics and Finance Working Papers EM200701, Universidad de Guanajuato, Department of Economics and Finance, revised Jan 2007.
  • Handle: RePEc:gua:wpaper:em200701
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    References listed on IDEAS

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    Cited by:

    1. repec:wly:japmet:v:31:y:2016:i:7:p:1467-1477 is not listed on IDEAS
    2. Gomez Zaldivar, M. & Ventosa-Santaularia, D., 2009. "Bilateral Relationship between Consumption and GDP in Mexico and the USA: A Comment," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 9(1).
    3. Daniel Ventosa-Santaularia, 2007. "Spurious Instrumental Variables," Department of Economics and Finance Working Papers EM200704, Universidad de Guanajuato, Department of Economics and Finance, revised Mar 2009.
    4. Antonio E. Noriega & Daniel Ventosa-Santaulària, 2011. "A Simple Test for Spurious Regressions," Working Papers 2011-05, Banco de México.
    5. Chris Stewart, 2011. "A note on spurious significance in regressions involving I(0) and I(1) variables," Empirical Economics, Springer, vol. 41(3), pages 565-571, December.
    6. Daniela Osterrieder & Daniel Ventosa-Santaulària & J. Eduardo Vera-Valdés, 2015. "Unbalanced Regressions and the Predictive Equation," CREATES Research Papers 2015-09, Department of Economics and Business Economics, Aarhus University.
    7. repec:spr:compst:v:32:y:2017:i:3:d:10.1007_s00180-016-0687-x is not listed on IDEAS
    8. Kruse Robinson & Ventosa-Santaulària Daniel & Noriega Antonio E., 2017. "Changes in persistence, spurious regressions and the Fisher hypothesis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(3), pages 1-28, June.
    9. Antonio E. Noriega & Daniel Ventosa-Santaulària, 2010. "Spurious Long-Horizon Regression in Econometrics," Working Papers 2010-06, Banco de México.
    10. Gerdie Everaert & Hauke Vierke, 2016. "Demographics and Business Cycle Volatility: A Spurious Relationship?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1467-1477, November.
    11. Zhang, Lingxiang, 2013. "Partial unit root and linear spurious regression: A Monte Carlo simulation study," Economics Letters, Elsevier, vol. 118(1), pages 189-191.
    12. Ventosa-Santaulària, Daniel & Noriega, Antonio E., 2015. "Long-run monetary neutrality under stochastic and deterministic trends," Economic Modelling, Elsevier, vol. 47(C), pages 372-382.

    More about this item

    Keywords

    Trend Stationarity; Structural Breaks; Spurious Regression; Unit Roots; Trends;

    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
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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