Convergence and Long Run Uncertainty

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.