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This paper describes a procedure based on maximum likelihood technique in two phases for estimating the parameters in mean reversion processes when the long-term trend is defined by a continued deterministic function. Closed formulas for the estimators that depend on observations of discrete paths and an estimation of the expected value of the process are obtained in the first phase. In the second phase, a reestimation scheme is proposed when a priori knowledge exists of the long-term trend. Some experimental results using simulated data sets are graphically illustrated.

This paper describes a procedure based on maximum likelihood technique in two phases for estimating the parameters in mean reversion processes when the long-term trend is defined by a continued deterministic function. Closed formulas for the estimators that depend on observations of discrete paths and an estimation of the expected value of the process are obtained in the first phase. In the second phase, a reestimation scheme is proposed when a priori knowledge exists of the long-term trend. Some experimental results using simulated data sets are graphically illustrated.

The asymptotic efficiency of indirect estimation methods, such as the efficient method of moments and indirect inference, depends on the choice of the auxiliary model. To date, this choice has been somewhat ad hoc and based on an educated guess. In this article we introduce a class of information criteria that helps the user to optimize the choice between nested and non–nested auxiliary models. They are the indirect analogues of the widely used Akaike–type criteria. A thorough Monte Carlo study based on two simple and illustrative models shows the usefulness of the criteria, La eficiencia asintótica de los estimadores de inferencia indirecta, tales como el método eficiente de los momentos, depende de la elección del modelo auxiliar. Hasta ahora, esta elección era ad hoc y basada en criterios subjetivos. En este artículo introducimos una clase de criterios de información que ayuda al usuario a escoger entre modelos anidados y no anidados. Esta clase es la análoga a los ampliamente utilizados criterios del tipo Akaike. Un detallado estudio de Monte Carlo basado en dos modelos simples e ilustrativos muestran la utilidad de los criterios