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Spurious regression under deterministic and stochastic trends

Author

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

Abstract

This paper analyses the asymptotic and finite sample implications of a mixed nonstationary behavior among the dependent and explanatory variables in a linear spurious regression model. We study the cases when the nonstationarity in the dependent variable is deterministic (stochastic), while the nonstationarity in the explanatory variable is stochastic (deterministic). In particular, we derive the asymptotic distribution of statistics in a spurious regression equation when one variable follows a difference stationary process (a random walk with and without drift), while the other is characterized by deterministic nonstationarity (a linear trend model with and without structural breaks in the trend function). We find that the divergence rate is sensitive to the assumed mixture of nonstationarity in the data generating process, and the phenomenon of spurious regression itself, contrary to previous findings, depends on the presence of a linear trend in the regression equation. Simulation experiments and real data confirm our asymptotic results.

Suggested Citation

  • Noriega, Antonio E. & Ventosa-Santaulària, Daniel, 2005. "Spurious regression under deterministic and stochastic trends," MPRA Paper 58772, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:58772
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    References listed on IDEAS

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    1. Noriega, Antonio E. & de Alba, Enrique, 2001. "Stationarity and structural breaks -- evidence from classical and Bayesian approaches," Economic Modelling, Elsevier, vol. 18(4), pages 503-524, December.
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    3. Entorf, Horst, 1997. "Random walks with drifts: Nonsense regression and spurious fixed-effect estimation," Journal of Econometrics, Elsevier, vol. 80(2), pages 287-296, October.
    4. Antonio E. Noriega & Daniel Ventosa‐Santaulària, 2006. "Spurious Regression Under Broken‐Trend Stationarity," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 671-684, September.
    5. Nunzio Cappuccio & Diego Lubian, 1997. "Spurious regressions between I(1) processes with long memory errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 18(4), pages 341-354, July.
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    7. Francesc Marmol, 1995. "SPURIOUS REGRESSIONS BETWEEN I(d) PROCESSES," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(3), pages 313-321, May.
    8. Perron, Pierre, 1997. "Further evidence on breaking trend functions in macroeconomic variables," Journal of Econometrics, Elsevier, vol. 80(2), pages 355-385, October.
    9. Marmol, Francesc, 1996. "Correlation theory of spuriously related higher order integrated processes," Economics Letters, Elsevier, vol. 50(2), pages 169-173, February.
    10. Mehl, Arnaud, 2000. "Unit root tests with double trend breaks and the 1990s recession in Japan," Japan and the World Economy, Elsevier, vol. 12(4), pages 363-379, December.
    11. Marmol, Francesc, 1998. "Spurious regression theory with nonstationary fractionally integrated processes," Journal of Econometrics, Elsevier, vol. 84(2), pages 233-250, June.
    12. Kim, Tae-Hwan & Lee, Young-Sook & Newbold, Paul, 2004. "Spurious regressions with stationary processes around linear trends," Economics Letters, Elsevier, vol. 83(2), pages 257-262, May.
    13. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    14. Robin L. Lumsdaine & David H. Papell, 1997. "Multiple Trend Breaks And The Unit-Root Hypothesis," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 212-218, May.
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    More about this item

    Keywords

    Unit roots; Trend stationarity; Structural breaks; Spurious regression;
    All these keywords.

    JEL classification:

    • 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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