Trend-cycle decomposition for peruvian GDP :application of an alternative method
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2013
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Pontificia Universidad Católica del Perú. Departamento de Economía
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Perron y Wada (2009) proponen un nuevo método de descomposición del PIB en sus componentes de tendencia y ciclo, que supere los problemas de identificación de los modelos de componentes no observables y modelos ARIMA y, al mismo tiempo, admite no linealidades y asimetrías en el ciclo. El método asume que la producción puede ser representado por un modelo no lineal de componentes no observados, donde las perturbaciones consisten en una mezcla de distribuciones normales. En este documento, se aplica este algoritmo al PIB peruano con datos trimestrales desde 1980 hasta 2011. Como resultado de este análisis, se elige el modelo UC-CN, que presenta una mezcla de las normales en las perturbaciones de la tendencia y el componente de ciclo de la producción. La tendencia obtenida refleja claramente el cambio estructural experimentado a principios de 1990. Después de una fuerte disminu ción de la tendencia o PIB potencial como resultado de las medidas de ajuste drásticos, la producción creció de una manera más estable en los años siguientes. De la misma manera, se puede observar un aumento en la tasa de crecimiento potencial del PIB a partir de 2002, lo que coincide con las reformas monetarias que tuvieron lugar en ese momento. Por último, los ciclos obtenidos son consistentes con la evolución de la economía peruana y de períodos de recesión que se han identificado tradicionalmente. También se proporciona una comparación con otros métodos de descomposición.
Perron and Wada (2009) propose a new method of decomposition of the GDP in its trend and cycle components, which overcomes the identification problems of models of unobserved components (UC) and ARIMA models and at the same time, admits non linearities and asymmetries in cycles. The method assumes that output can be represented by a non linear model of unobserved components, where disturbances consist of a mixture of normal distributions. In this document, we apply this algorithm to Peruvian GDP using quarterly data from 1980 until 2011. As a result of this analysis, we choose the UC-CN model, which presents a mixture of normals in the disturbances of the trend and cycle component of output. The obtained trend clearly reflects the structural change undergone in the early 1990s. After a steep decrease of the trend or potential GDP as a result of drastic adjustment measures, output grew in a more stable way in the following years. In the same way, one can observe an increase in the growth rate of potential GDP from 2002 onwards, which coincides with the monetary reforms that took place at the time. Finally, the obtained cycles are consistent with the evolution of the Peruvian economy and of recession periods that have been traditionally identified. A comparison with other methods of decomposition is also provided.
Perron and Wada (2009) propose a new method of decomposition of the GDP in its trend and cycle components, which overcomes the identification problems of models of unobserved components (UC) and ARIMA models and at the same time, admits non linearities and asymmetries in cycles. The method assumes that output can be represented by a non linear model of unobserved components, where disturbances consist of a mixture of normal distributions. In this document, we apply this algorithm to Peruvian GDP using quarterly data from 1980 until 2011. As a result of this analysis, we choose the UC-CN model, which presents a mixture of normals in the disturbances of the trend and cycle component of output. The obtained trend clearly reflects the structural change undergone in the early 1990s. After a steep decrease of the trend or potential GDP as a result of drastic adjustment measures, output grew in a more stable way in the following years. In the same way, one can observe an increase in the growth rate of potential GDP from 2002 onwards, which coincides with the monetary reforms that took place at the time. Finally, the obtained cycles are consistent with the evolution of the Peruvian economy and of recession periods that have been traditionally identified. A comparison with other methods of decomposition is also provided.
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Tendencia, Ciclo, Mezcla de normales, Asimetrías, No linealidades, Recesión, Filtros
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