Term structure forecasting: no-arbitrage restrictions versus large information set

This paper addresses the issue of forecasting the term structure. We provide a unified state-space modeling framework that encompasses different existing discrete-time yield curve models. Within such framework we analyze the impact on the forecasting performance of two modeling choices, namely the imposition of no-arbitrage restrictions and the size of the information set used to extract factors. Using US yield curve data, we find that both no-arbitrage and large information help in forecasting but no model uniformly dominates the other. No-arbitrage models are more useful at shorter horizon for shorter maturities. Large information sets are more useful at longer horizons and longer maturities. We also find evidence for a significant feedback from yield curve models to macroeconomic variables that could be exploited for macroeconomic forecasting.