TAR modeling with missing data when the white noise process is not Gaussian
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Zhang, Hanwen
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En esta investigación, proponemos tres familias de modelos TAR: (1) Modelos TAR con ruidos t, (2) Modelos TAR para el logaritmo de series positivas, y (3) Modelos TAR donde el proceso del ruido tiene distribución Gamma estandarizada. Para cada uno de estos modelos, proponemos un procedimiento de tres etapas que consiste en: (1) La identificación del número de regímenes y los correspondientes umbrales, (2) La identificación de los órdenes autoregresivos en los regímenes, y (3) La estimación de los parámetros no estructurales, estos son, los coeficientes autoregresivos, las varianzas condicionales tipo II y demás parámetros que cada modelo particular pueda tener.
Abstract. In this work, we proposed three families of TAR models: (1) TAR models with t-distributed noise process, (2) TAR models for logarithm of positive series, and (3) TAR models with standardized Gamma distributed noise process. For each one of these models, we proposed a three-stage procedure which consists of: (1) identifying the number of regimes and the corresponding thresholds, (2) identifying the autoregressive order in each regime, and (3) estimating the non-structural parameters, i.e., the autoregressive coefficients and the type II conditional variance in each regime, and other parameters that each particular model may contain.
Abstract. In this work, we proposed three families of TAR models: (1) TAR models with t-distributed noise process, (2) TAR models for logarithm of positive series, and (3) TAR models with standardized Gamma distributed noise process. For each one of these models, we proposed a three-stage procedure which consists of: (1) identifying the number of regimes and the corresponding thresholds, (2) identifying the autoregressive order in each regime, and (3) estimating the non-structural parameters, i.e., the autoregressive coefficients and the type II conditional variance in each regime, and other parameters that each particular model may contain.