A Unified Growth Model for Independent Chile

This article analyzes long-term patterns of growth of the Chilean economy. Examining 200 years of data, it shows evidence in favor of using a neoclassical growth model to conduct the empirical analysis. It presents a formal analysis of structural breaks in the Chilean growth process, finding structural changes in 1929 and 1971/1981. A further analysis of the country’s economic history indicates that fiscal policy, external shocks and trade policy are plausible explanations for these breaks. When these variables are included in the empirical model, the hypothesis of no breaks during these 200 years cannot be rejected.Chile, structural breaks, growth, fiscal policies, external shocks

A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields

Storm surge, the onshore rush of sea water caused by the high winds and low pressure associated with a hurricane, can compound the effects of inland flooding caused by rainfall, leading to loss of property and loss of life for residents of coastal areas. Numerical ocean models are essential for creating storm surge forecasts for coastal areas. These models are driven primarily by the surface wind forcings. Currently, the gridded wind fields used by ocean models are specified by deterministic formulas that are based on the central pressure and location of the storm center. While these equations incorporate important physical knowledge about the structure of hurricane surface wind fields, they cannot always capture the asymmetric and dynamic nature of a hurricane. A new Bayesian multivariate spatial statistical modeling framework is introduced combining data with physical knowledge about the wind fields to improve the estimation of the wind vectors. Many spatial models assume the data follow a Gaussian distribution. However, this may be overly-restrictive for wind fields data which often display erratic behavior, such as sudden changes in time or space. In this paper we develop a semiparametric multivariate spatial model for these data. Our model builds on the stick-breaking prior, which is frequently used in Bayesian modeling to capture uncertainty in the parametric form of an outcome. The stick-breaking prior is extended to the spatial setting by assigning each location a different, unknown distribution, and smoothing the distributions in space with a series of kernel functions. This semiparametric spatial model is shown to improve prediction compared to usual Bayesian Kriging methods for the wind field of Hurricane Ivan.Comment: Published at http://dx.doi.org/10.1214/07-AOAS108 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

On the determinants of the Chilean Economic Growth

This paper presents several methodologies for understanding the Chilean growth process. By using univariate time series representations, we find that the Chilean data is more consistent with exogenous than with endogenous growth models. Growth accounting exercises show that the mild growth rates of the sixties are mainly due to the accumulation of human and physical capital, while the booms of the mid seventies and the one from 1985 until 1998 are mainly due to TFP growth. We also find that among the most important determinants of the evolution of TFP are the evolution of terms of trade, improvements on the quality of capital, and the presence of distortions. In fact, distortions do not only eliminate the positive effects of improvements on the quality of capital, but also precede the evolution of technology shocks and increase their volatility. A dynamic stochastic general equilibrium model that explicitly incorporates the relative price of investment with respect to consumption goods, terms of tra de, and distortionary taxes is able to successfully replicate the impulse-response functions found on the data. This exercise suggests that distortions play a key role in explaining the growth dynamics of the Chilean experience.