BIBLIOTECA MANUEL BELGRANO - Facultad de Ciencias Económicas - UNC

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Comparing the DSGE model with the factor model : an out-of-sample forecasting experiment / Mu-Chun Wang.

Por: Tipo de material: TextoTextoSeries Discussion paper (Deutsche Bundesbank). Series 1: economic studies ; no. 04/2008Detalles de publicación: Frankfurt am Main : Deutsche Bundesbank, 2008Descripción: 33 pISBN:
  • 9783865583710
Tema(s): Clasificación CDD:
  • 21 330.0112
Recursos en línea: Resumen: In this paper, we put DSGE forecasts in competition with factor forecasts. We focus on these two models since they represent nicely the two opposing forecasting philosophies. The DSGE model on the one hand has a strong theoretical economic background; the factor model on the other hand is mainly data-driven. We show that by incooperating large information set using factor analysis can indeed improve the short horizon predictive ability, as claimed by manyresearchers. The micro founded DSGE model can provide reasonable forecasts for inflation, especially with growing forecast horizons. To a certain extent, our results are consistent with the prevailling view that simple time series models should be used in short-horizon forecasting and structural models should be used in long-horizon forecasting. Our paper compareds both state-of-the art data-driven and theory-based modelling in a rigorous manner.
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Bibliografía: p. 20-21.

In this paper, we put DSGE forecasts in competition with factor forecasts. We focus on these two models since they represent nicely the two opposing forecasting philosophies. The DSGE model on the one hand has a strong theoretical economic background; the factor model on the other hand is mainly data-driven. We show that by incooperating large information set using factor analysis can indeed improve the short horizon predictive ability, as claimed by manyresearchers. The micro founded DSGE model can provide reasonable forecasts for inflation, especially with growing forecast horizons. To a certain extent, our results are consistent with the prevailling view that simple time series models should be used in short-horizon forecasting and structural models should be used in long-horizon forecasting. Our paper compareds both state-of-the art data-driven and theory-based modelling in a rigorous manner.

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