Predicting Interest Rates Using Shrinkage Methods, Real-Time Diffusion Indexes, and Model Combinations

In the context of predicting the term structure of interest rates, we explore the marginal predictive content of real-time diffusion indexes extracted from a “data rich” real-time dataset, when used in dynamic Nelson-Siegel (NS) models of the variety discussed in Diebold and Li (DNS: 2007) and Svensson (NSS: 1994). We find that the indexes have significant predictive content for sample periods ranging from 2001 through 2010. Additionally, DNS and NSS type models that include these indexes are the mean square forecast error (MSFE) “best” performers, when compared with various other econometric specifications, for the same period. In our top ranked models, diffusion indexes are sometimes optimally constructed used un-targeted principal component analysis (PCA), and sometimes using targeted PCA in conjunction with elastic net and least absolute shrinkage operators. The news is not all good for models utilizing indexes constructed from real-time datasets, however. In particular, after 2010 relatively few “data-rich” prediction models “beat” a random walk benchmark. Also, forecast combinations that utilize models that exclude real-time diffusion indexes yield the lowest overall MSFEs, dominating all other combination and individual models, across all sample periods, forecast horizons and bond maturities. Two key conclusions from our analysis are the following. First, fully revised data may have an important confounding effect upon results obtained when instead carrying out real-time prediction experiments. Second, real-time diffusion indexes matter when comparing the predictive performance of individual models, indicating the presence of unspanned macroeconomic risks in the term structure of interest rates. However, there appear to be two different ways to “capture” these unspanned risks. One is to use data rich real-time diffusion indexes, and another is to simply combine predictions from many non-data rich models