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Unit roots versus trend stationarity in growth rate estimation

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  • John Baffes
  • Jean-Charles Le Vallee

Abstract

This paper estimates growth rates of quantity and trade variables of 32 primary commodities for the periods 1970-1980, 1980-1990, and 1990-1999 through log-linear trend regressions. The properties of the models were assessed through conventional and stationarity statistics. Based on Monte Carlo experiments, of the 105 regressions examined, in only 35 cases the error term was found to be stationary at the 5% level of significance, implying that in many cases the stochastic component is far more important in explaining the behaviour of the series under consideration, in turn implying that the validity of growth rates should be interpreted with some degree of caution.

Suggested Citation

  • John Baffes & Jean-Charles Le Vallee, 2003. "Unit roots versus trend stationarity in growth rate estimation," Applied Economics Letters, Taylor & Francis Journals, vol. 10(1), pages 9-14.
    • Handle: RePEc:taf:apeclt:v:10:y:2003:i:1:p:9-14
    DOI: 10.1080/13504850210165874
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    Cited by:

    1. Chatterji, Monojit & Choudhury, Homagni, 2010. "The Changing Inter-Industry Wage Structure of the Organised Manufacturing Sector in India, 1973-74 to 2003-04," SIRE Discussion Papers 2010-89, Scottish Institute for Research in Economics (SIRE).
    2. Luis A. Gil-Alana & Antonio Moreno & Seonghoon Cho, 2012. "The Deaton paradox in a long memory context with structural breaks," Applied Economics, Taylor & Francis Journals, vol. 44(25), pages 3309-3322, September.
    3. Monojit Chatterji & Homagni Choudhury, 2010. "Growth Rate Estimation in the presence of Unit Roots," Dundee Discussion Papers in Economics 245, Economic Studies, University of Dundee.
    4. Galip Altinay, 2003. "Estimating growth rate in the presence of serially correlated errors," Applied Economics Letters, Taylor & Francis Journals, vol. 10(15), pages 967-970.

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