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Common Trends and Common Cycles in Latin America: A 2-step vs an Iterative Approach

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  • Alain W. HECQ

Abstract

We are interested in determining the number of common trends and common cycles in the output of a set of Latin American countries. In order to work with homogeneous and reasonably good series however, we should rely on annual data. Consequently, the number of variables is relatively large compared to the number of observations to blindly trust the asymptotics. For several years, the panel data literature proposes tools to tackle this problem, mainly for the study of long-run co-movements. We take another road here and we test for cointegration and common cyclical features in a time series framework using an iterative strategy that maximizes the likelihood function by successively imposing long and short-run restrictions until convergence is achieved. Monte Carlo simulations stress advantages of this approach over the two-step one. In practice however, the cost of implementing this more complicated procedure must be evaluated with the expected benefits. Overall, simple adjustments for the degrees of freedom and the use of information criteria are helpful "cheap" alternatives

Suggested Citation

  • Alain W. HECQ, 2005. "Common Trends and Common Cycles in Latin America: A 2-step vs an Iterative Approach," Computing in Economics and Finance 2005 258, Society for Computational Economics.
    • Handle: RePEc:sce:scecf5:258
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    References listed on IDEAS

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    Cited by:

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    2. Adom, Assandé Désiré & Sharma, Subhash C. & Morshed, A.K.M. Mahbub, 2010. "Economic integration in Africa," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 245-253, August.

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    More about this item

    Keywords

    Common trends; Common Cycles; Monte Carlo;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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