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

  • Hacker, Scott

    ()

    (Jonkoping International Business School)

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.

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Paper provided by Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies in its series Working Paper Series in Economics and Institutions of Innovation with number 213.

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Length: 33 pages
Date of creation: 11 Feb 2010
Date of revision:
Handle: RePEc:hhs:cesisp:0213
Contact details of provider: Postal: CESIS - Centre of Excellence for Science and Innovation Studies, Royal Institute of Technology, SE-100 44 Stockholm, Sweden
Phone: +46 8 790 95 63
Web page: http://www.infra.kth.se/cesis/
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  1. Perron, P., 1986. "Trends and Random Walks in Macroeconomic Time Series: Further Evidence From a New Approach," Cahiers de recherche 8650, Universite de Montreal, Departement de sciences economiques.
  2. Helle Bunzel & Timothy Vogelsang, 2003. "Powerful Trend Function Tests That are Robust to Strong Serial Correlation with an Application to the Prebisch Singer Hypothesis," Econometrics 0304002, EconWPA.
  3. Peter C.B. Phillips, 1985. "Understanding Spurious Regressions in Econometrics," Cowles Foundation Discussion Papers 757, Cowles Foundation for Research in Economics, Yale University.
  4. McQuarrie, Allan & Shumway, Robert & Tsai, Chih-Ling, 1997. "The model selection criterion AICu," Statistics & Probability Letters, Elsevier, vol. 34(3), pages 285-292, June.
  5. Denis Kwiatkowski & Peter C.B. Phillips & Peter Schmidt, 1991. "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?," Cowles Foundation Discussion Papers 979, Cowles Foundation for Research in Economics, Yale University.
  6. Dolado, Juan J & Jenkinson, Tim & Sosvilla-Rivero, Simon, 1990. " Cointegration and Unit Roots," Journal of Economic Surveys, Wiley Blackwell, vol. 4(3), pages 249-73.
  7. 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.
  8. Ayat, L. & Burridge, P., 1996. "Unit Root Tests in the presence of Uncertainty about the Non-Stochastic Trends," Discussion Papers 96-28, Department of Economics, University of Birmingham.
  9. 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.
  10. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
  11. Timothy J. Vogelsang, 1998. "Trend Function Hypothesis Testing in the Presence of Serial Correlation," Econometrica, Econometric Society, vol. 66(1), pages 123-148, January.
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