Palabras clave
Bayesian inference
recursive algorithms
modelos de equilibrio general
inferencia bayesiana
algoritmos recursivos
Cómo citar
Resumen
En este trabajo se describe un procedimiento general para hacer inferencia bayesiana basados en la evaluación de la verosimilitud de los modelos de equilibrio general estocásticos (MEGE) a través de los métodos de Monte Carlo por Cadenas de Markov (MCMC). La metodología propuesta requiere log linealizar los modelos, transformarlos en la forma espacio estado, luego utilizar el filtrode Kalman para evaluar la función de verosimilitud y finalmente aplicar el algoritmo Metropolis Hastings para estimar los parámetros de la distribución a posteriori. Se ilustra la técnica mediante el uso del modelo básico de crecimiento estocástico, considerando datos trimestrales de la economía venezolana comprendidos entre el primer trimestre de (1984) hasta el tercer trimestre de (2004). El análisis empírico realizado nos permite concluir que los algoritmos utilizados para estimar los parámetros del modelo trabajan de manera eficiente y a bajo costo computacional, las estimaciones obtenidas son consistentes, es decir, los estimados de las predicciones reflejan adecuadamente el comportamiento del producto, el empleo, el consumo y la inversión per capita del país. En las gráficas de los histogramas estimados se observa que tienen comportamientos bimodales y distribuciones asimétricas.
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