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The Effectiveness of Information Criteria in Determining Unit Root and Trend Status

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  • Hacker, Scott

    () (Jonkoping International Business School)

Abstract

This paper compares the performance of using an information criterion, such as the Akaike information criterion or the Schwarz (Bayesian) information criterion, rather than hypothesis testing in consideration of the presence of a unit root for a variable and, if unknown, the presence of a trend in that variable. The investigation is performed through Monte Carlo simulations. Properties considered are frequency of choosing the unit root status correctly, predictive performance, and frequency of choosing an appropriate subsequent action on the examined variable (first differencing, detrending, or doing nothing). Relative performance is considered in a minimax regret framework. The results indicate that use of an information criterion for determining unit root status and (if necessary) trend status of a variable can be competitive to alternative hypothesis testing strategies.

Suggested Citation

  • Hacker, Scott, 2010. "The Effectiveness of Information Criteria in Determining Unit Root and Trend Status," Working Paper Series in Economics and Institutions of Innovation 213, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
  • Handle: RePEc:hhs:cesisp:0213
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    References listed on IDEAS

    as
    1. Hacker, Scott & Hatemi-J, Abdulnasser, 2010. "The Properties of Procedures Dealing with Uncertainty about Intercept and Deterministic Trend in Unit Root Testing," Working Paper Series in Economics and Institutions of Innovation 214, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    2. Bunzel, Helle & Vogelsang, Timothy J., 2005. "Powerful Trend Function Tests That Are Robust to Strong Serial Correlation, With an Application to the Prebisch-Singer Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 381-394, October.
    3. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    4. Perron, Pierre, 1988. "Trends and random walks in macroeconomic time series : Further evidence from a new approach," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 297-332.
    5. Ayat, Leila & Burridge, Peter, 2000. "Unit root tests in the presence of uncertainty about the non-stochastic trend," Journal of Econometrics, Elsevier, vol. 95(1), pages 71-96, March.
    6. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
    7. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    8. Dolado, Juan J & Jenkinson, Tim & Sosvilla-Rivero, Simon, 1990. " Cointegration and Unit Roots," Journal of Economic Surveys, Wiley Blackwell, vol. 4(3), pages 249-273.
    9. McQuarrie, Allan & Shumway, Robert & Tsai, Chih-Ling, 1997. "The model selection criterion AICu," Statistics & Probability Letters, Elsevier, vol. 34(3), pages 285-292, June.
    10. Timothy J. Vogelsang, 1998. "Trend Function Hypothesis Testing in the Presence of Serial Correlation," Econometrica, Econometric Society, vol. 66(1), pages 123-148, January.
    11. John Elder & Peter E. Kennedy, 2001. "Testing for Unit Roots: What Should Students Be Taught?," The Journal of Economic Education, Taylor & Francis Journals, vol. 32(2), pages 137-146, January.
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    More about this item

    Keywords

    Unit Root; Stationarity; Model Selection; Minimax regret; Information Criteria;

    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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