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Spurious Regression and Trending Variables

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  • Noriega, Antonio E.
  • Ventosa-Santaulària, Daniel

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 the 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

  • Noriega, Antonio E. & Ventosa-Santaulària, Daniel, 2007. "Spurious Regression and Trending Variables," MPRA Paper 58775, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:58775
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    Citations

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    Cited by:

    1. 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.
    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. Noriega Antonio E. & Ventosa-Santaulària Daniel, 2011. "A Simple Test for Spurious Regressions," Working Papers 2011-05, Banco de México.
    4. 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.
    5. 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.
    6. Noriega Antonio E. & Ventosa-Santaulària Daniel, 2010. "Spurious Long-Horizon Regression in Econometrics," Working Papers 2010-06, Banco de México.
    7. Zhang, Lingxiang, 2013. "Partial unit root and linear spurious regression: A Monte Carlo simulation study," Economics Letters, Elsevier, vol. 118(1), pages 189-191.
    8. 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.
    9. 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.
    10. Yijie Fei, 2024. "A joint test of predictability and structural break in predictive regressions," Empirical Economics, Springer, vol. 67(3), pages 985-1013, September.
    11. Yu kun Wang & Li Zhang & We-me Ho, 2020. "The Priority of Exploiting Fiscal Revenue or Lessening Public Expenditure: Evidence from China," Applied Finance and Accounting, Redfame publishing, vol. 6(1), pages 54-65, February.
    12. Hao Jin & Si Zhang & Jinsuo Zhang, 2017. "Spurious regression due to neglected of non-stationary volatility," Computational Statistics, Springer, vol. 32(3), pages 1065-1081, September.
    13. 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.
    14. Simona Roxana Patarlageanu & Marius Constantin & Mihai Dinu, 2020. "Considerations Regarding The Horeca Industry In Bihor County. An Econometric Approach," Oradea Journal of Business and Economics, University of Oradea, Faculty of Economics, vol. 5(1), pages 7-17, March.

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    More about this item

    Keywords

    Spurious regression; trends; unit roots; trend stationarity; structural breaks;
    All these keywords.

    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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