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Indicadores líderes, redes neuronales y predicción de corto plazo

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  • Javier Kapsoli Salinas

    (Departamento de Economía- Pontificia Universidad Católica del Perú)

  • Brigitt Bencich Aguilar

Abstract

This paper shows a procedure to construct a short run predictor for the GDP. We use the Baxter & King filter to decompose the monthly GDP on its three components: seasonal, business cycle and long-run trend. Furthermore we estimate and forecast the business cycle using a set of leading economic variables. We propose that the complicated relationships among this variables and the business cycle are well captured by a non linear artificial neural network model. The other components are estimated using standard econometric techniques. Finally, the three components are added to obtain an indicator for the future behavior of the GDP. The prediction shows an acceptable level of reliability, so the index can be used to take decisions in the private or public sector. The main advantage of the index is its faster availability relative to the official statistics.

Suggested Citation

  • Javier Kapsoli Salinas & Brigitt Bencich Aguilar, 2002. "Indicadores líderes, redes neuronales y predicción de corto plazo," Documentos de Trabajo / Working Papers 2002-213, Departamento de Economía - Pontificia Universidad Católica del Perú.
    • Handle: RePEc:pcp:pucwps:wp00213
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