Template-Type: ReDIF-Paper 1.0 Series: Tinbergen Institute Discussion Papers Creation-Date: 2011-04-07 Number: 11-063/4 Author-Name: Siem Jan Koopman Author-Email: firstname.lastname@example.org Author-Workplace-Name: VU University Amsterdam Author-Name: Michel van der Wel Author-Email: email@example.com Author-Workplace-Name: Erasmus University Rotterdam Title: Forecasting the U.S. Term Structure of Interest Rates using a Macroeconomic Smooth Dynamic Factor Model Abstract: This discussion paper led to a publication in the International Journal of Forecasting, 2013, 29($), 676-694. See also the publication in the 'Journal of Applied Econometrics', 2014, 29(1), 65-90.
We extend the class of dynamic factor yield curve models for the inclusion of macro-economic factors. We benefit from recent developments in the dynamic factor literature for extracting the common factors from a large panel of macroeconomic series and for estimating the parameters in the model. We include these factors into a dynamic factor model for the yield curve, in which we model the salient structure of the yield curve by imposing smoothness restrictions on the yield factor loadings via cubic spline functions. We carry out a likelihood-based analysis in which we jointly consider a factor model for the yield curve, a factor model for the macroeconomic series, and their dynamic interactions with the latent dynamic factors. We illustrate the methodology by forecasting the U.S. term structure of interest rates. For this empirical study we use a monthly time series panel of unsmoothed Fama-Bliss zero yields for treasuries of different maturities between 1970 and 2009, which we combine with a macro panel of 110 series over the same sample period. We show that the relation between the macroeconomic factors and yield curve data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Finally, we perform an extensive out-of-sample forecasting study. Our main conclusion is that macroeconomic variables can lead to more accurate yield curve forecasts. Classification-JEL: C32, C51, E43 Keywords: Fama-Bliss data set, Kalman filter, Maximum likelihood, Yield curve File-Url: http://papers.tinbergen.nl/11063.pdf File-Format: application/pdf File-Size: 511390 bytes Handle: RePEc:tin:wpaper:20110063