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Convergence and Long Run Uncertainty

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  • Pablo Pincheira

Abstract

In this paper the neoclassical convergence hypothesis is tested for the thirteen regions of Chile using crosssection and time-series techniques. Cross-section analysis in combination with a Bayesian Modeling Averaging strategy supports the convergence hypothesis, despite of some instability detected in the estimated speed of convergence. When applying time-series based tests, the no convergence null hypothesis cannot be rejected at usual significance levels. When clustering the Chilean regions into three different groups, however, evidence of cointegration within these groups is found, indicating that the regional growth process in Chile is driven by a lower number of common trends. The implementation of both cross-section and time-series tests allows coverage of two different situations: economies in transition dynamics and economies in stationary distribution. Because cross-section and time-series tests place different implications on the data one can claim that under the assumption that Chilean regions are in transition towards a stationary distribution, the convergence hypothesis is supported by the data. If one assumes, however, that Chilean regions already achieved their limiting distribution, the convergence hypothesis is not supported by the data.

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  • Pablo Pincheira, 2006. "Convergence and Long Run Uncertainty," Working Papers Central Bank of Chile 391, Central Bank of Chile.
  • Handle: RePEc:chb:bcchwp:391
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    Cited by:

    1. Jorge Dresdner & Carlos Sanhueza, 2009. "NOTA TECNICA: Estimación de Series de Salarios Regionales en Chile," Estudios de Economia, University of Chile, Department of Economics, vol. 36(2 Year 20), pages 243-261, December.
    2. World Bank, 2008. "Chile," World Bank Publications - Reports 28202, The World Bank Group.
    3. Bentancor, A. & Modrego, F. & Berdegué, J., 2008. "Crecimiento y Distribución del Ingreso como Determinantes de la Reducción de la Pobreza en Comunas Rurales de Chile," Working papers 014, Rimisp Latin American Center for Rural Development.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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