predictive power by allowing for time-varying risk premia. For example, changes in the term structure largely reflect changes in risk premia in the canonical affine no-arbitrage term structure models augmented with macroeconomic variables. This is related in part to shifts in the perception of inflation risk, with risk premia guiding, albeit imperfectly, macroeconomic and financial developments (Gürkaynak and Wright (2012)).<sup>74</sup> Some studies report that risk premia are affected by macroeconomic factors. Using a dynamic term structure model, Joslin et al (2014) find that macroeconomic variables have significant predictive power over and above the level, slope and curvature of the yield curve. Specifically, they report that macroeconomic risks that cannot be hedged by financial variables (so called “unspanned risks”) explain a substantial portion of the variation of the forward term premium, with unspanned real economic growth being the key driving factor.<sup>75</sup> Baele et al (2010) show that macroeconomic factors are important in explaining bond return volatility. This research suggests that macroeconomic factors affect not only the level of shortterm interest rates but also the term structure and other moments of the yield curve.

There are also limits to the predictive ability of the yield curve, which depend on the time horizon, country-specific circumstances and external factors. In particular, predictive ability is largely relevant for up to one year in advance, especially in forecasting the timing of recessions. The predictive value also varies across time periods (Bordo and Haubrich (2004)), with its power possibly having declined over time (Stock and Watson (2003)). In related research, Mody and Taylor (2003) find evidence of predictive power in the 1970s and 1980s in the United States, possibly due to high and volatile inflation but not in the 1990s and 1960s. Chinn and Kucko (2015) report that predictive power has deteriorated in the United States and in some European countries in recent years. Bonser-Neal and Morley (1997) find that the yield spread explains 30% to 50% of future real economic activity in Canada, Germany and the US, and less than 10% in Switzerland and Japan.

Predictive power also seems to depend on global financial market conditions. The close relationship between the risk premium in swap markets and real activity, for example, breaks down around the year 2000 and during the 2006–07 period (Joslin et al (2014)). Since these were periods of elevated financial stress, it suggests that predictive ability results from developments in the financial system rather than from the direct effects of changes in interest rates on activity (see also Adrian (2017) for a review).

74 For empirical evidence, see Diebold et al (2005, 2006) and Rudebusch and Wu (2008). Affine models are a special class of arbitrage-free term structure models, in which bond yields are affine (constant plus-linear) functions of some (vector of) state variables (see further Rudebusch (2010)). Campbell et al (2014, 2017), Christiansen and Ranaldo (2007), David and Veronesi (2013), Guidolin and Timmermann (2007) and Viceira (2011) further examine the drivers of bond returns. For additional reviews of the literature on the linkages between the nominal and real term structures of interest rates and macroeconomic outcomes, see Dai and Singleton (2003), Gürkaynak and Wright (2012), Duffee (2013) and Adrian (2017).

75 In addition to the models that add macroeconomic variables to the canonical arbitrage-free term structure model, Rudebusch (2010) identifies two additional strands: those that examine the financial implications of bond pricing in DSGE models and those that use the Arbitrage-Free Nelson-Siegel (AFNS) model. The first strand augments the standard RBC model with habit or recursive preferences (Epstein and Zin (1989) and Hansen and Sargent (2008)) but has difficulty in replicating the size and volatility of bond premia. Furthermore, the financial sector in these models remains very rudimentary in terms of frictions and intermediation. The second class, AFNS-models, lacks theoretical foundations but can be easily estimated and often exhibits a superior forecasting record. These models are extensively used by policy institutions.

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