Dedollarization, Indexation and Nominalization: The Chilean Experience

This paper revisits the Chilean experience with dollarization, indexation and nominalization in the 1958-2003 period. The purpose is to understand how Chile generally avoided dollarization and actually dedollarized in the 80s in order to draw some lessons for other countries. We find that many policies that Chile pursued are not easy to implement elsewhere. Some key characteristics of the Chilean process are related to initial institutional conditions and developments, whereas others are connected to macroeconomic performance and specific regulations. Indexation plays a key role in explaining how dollarization can be avoided.

New Frontiers for Monetary Policy in Chile

This paper assesses the efficiency of the current Inflation Targeting (IT) scheme in place at the Central Bank of Chile. Using a small macroeconomic model of the Chilean economy, our main results are as follows: (i) an efficient monetary policy requires a bias towards output stabilization around its long run trend; (ii) the switch to forecast-targeting, implicit in the current IT scheme in Chile, results in an efficiency gain; (iii) targeting core inflation is not efficient; (iv) ceteris paribus, efficiency could be enhanced if monetary policy leans against the wind when facing shocks to the cost of international finance.

Utilización de textos y gráficos en la enseñanza asistida por ordenador

Se realiza un análisis de los aspectos que rigen la utilización de textos, gráficos y animaciones en el desarrollo de materiales didácticos orientados a la Enseñanza Asistida por Ordenador, con el objetivo de obtener mayor eficiencia y calidad en la confección de lecciones. Se destaca la importancia de la imagen y el texto durante el proceso de aprendizaje y se analiza la posibilidad de combinar el ordenador con sistemas electrónicos que aumentan su potencialidad y constituyen nuevas vías de interacción con el estudiante

Utilización de textos y gráficos en la Enseñanza Asistida por Ordenador

Se realiza un análisis de los aspectos que rigen la utilización de textos, gráficos y animaciones en el desarrollo de materiales didácticos orientados a la Enseñanza Asistida por Ordenador, con el objetivo de obtener mayor eficiencia y calidad en la conf

Fuzzy heterogeneous neurons for imprecise classification problems

In the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing missing information and uncertainty. In this paper, a general class of neuron models accepting heterogeneous inputs in the form of mixtures of continuous (crisp and/or fuzzy) and discrete quantities admitting missing data is presented. From these, several particular models can be derived as instances and different neural architectures constructed with them. Such models deal in a natural way with problems for which information is imprecise or even missing. Their possibilities in classification and diagnostic problems are here illustrated by experiments with data from a real-world domain in the field of environmental studies. These experiments show that such neurons can both learn and classify complex data very effectively in the presence of uncertain information.Peer ReviewedPostprint (author's final draft

Fuzzy heterogeneous neural networks for signal forecasting

Fuzzy heterogeneous neural networks are recently introduced models based on neurons accepting heterogeneous inputs (i.e. mixtures of numerical and non-numerical information possibly with missing data) with either crisp or imprecise character, which can be coupled with classical neurons. This paper compares the effectiveness of this kind of networks with time-delay and recurrent architectures that use classical neuron models and training algorithms in a signal forecasting problem, in the context of finding models of the central nervous system controllers.Peer ReviewedPostprint (author's final draft