Este documento muestra un procedimiento para construir un predictor de corto plazo del nivel de actividad económica. Para ello se utiliza el filtro de Baxter & King para descomponer la serie de PBI mensual en sus tres componentes: estacional, cíclico y tendencial. Posteriormente el componente cíclico es estimado y pronosticado a partir de un conjunto de variables líderes que adelantan al PBI. Se propone que las relaciones entre estas variables y el ciclo del PBI se dan a través de un modelo no lineal de redes neuronales. Los demás componentes son estimados utilizando modelos econométricos estándar. Finalmente, se agregan los tres componentes para obtener un indicador de la evolución futura del PBI. La predicción que se obtiene muestra un nivel razonable de confiabilidad, por lo que el índice propuesto puede ser una herramienta para la toma de decisiones dada su pronta disponib...

This paper presents a procedure for constructing a short-term predictor of the level of economic activity. To do so, the Baxter-King filter is used to decompose the monthly GDP series into its three components: seasonal, cyclical, and trend. The cyclical component is then estimated and forecasted using a set of leading variables that lead GDP. It is proposed that the relationships between these variables and the GDP cycle are determined through a nonlinear neural network model. The remaining components are estimated using standard econometric models. Finally, the three components are aggregated to obtain an indicator of future GDP developments. The resulting prediction demonstrates a reasonable level of reliability, making the proposed index a useful tool for decision-making given its ready availability relative to official statistics.

This paper shows a procedure to constmct a short run predictor for the GDP. We use theBaxter & King filter to decompose the monthly GDP on its three components: seasonal, business cycle and iong-run trend. Furthermore we estimate and forecast the businesscycle using a set of leading economic variables. We propose that the complicated relationshipsamong this variables and the business cycle are well captured by a non linearartificial neural network model. The other components are estimated using standardeconometric techniques. Finally, the three components are added to obtain an indicatorfor the future behavior of the GDP. The prediction shows an aceptable leve1 of reliability,so the index can be used to take decisions in the private or public sector. The mainadvantage of the index is its faster availability relative to the oficial statistics.

Conferencia realizada por la carrera de Economía de Economía y Finanzas el 14 de junio de 2018 desde el auditorio Bustamante en el Campus San Isidro de la Universidad Peruana de Ciencias Aplicadas (UPC).