Using a unique data set of individual professional forecasts, we document disagreement about the future path of monetary policy, particularly at longer horizons. The stark differences in short rate forecasts imply strong disagreement about the risk-return trade-off of longer-term bonds. Longerhorizon short rate disagreement co-moves with term premiums. We estimate an affine term structure model in which investors hold heterogeneous beliefs about the long-run level of rates. Our model fits U.S. Treasury yields and the short rate paths predicted by different groups of professional forecasters very well. About one-third of the variation in term premiums is driven by short rate disagreement.

In this paper we model and predict the term structure of US interest rates in a data-rich and unstable environment. The dynamic Nelson-Siegel factor model is extended to allow the model dimension and the parameters to change over time, in order to account for both model uncertainty and sudden structural changes, in one setting. The proposed specification performs better than several alternatives, since it incorporates additional macrofinance information during hard times, while it allows for more parsimonious models to be relevant during normal periods. A dynamic variance decomposition measure constructed from our model shows that parameter uncertainty and model uncertainty regarding different choices of predictors explain a large proportion of the predictive variance of bond yields.

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In this paper we study the exchange rate predictability across a range of investment horizons by proposing a generalized (term structure) model to capture the risk premium component of exchange rates with a broad set of variables meanwhile handle both parameter and model uncertainty. We demonstrate the existence of time-varying term-structural effect and model disagreement effect of exchange rate predictors as well as the projections of predictive information over the term structure. We further utilize the time-variation in the probability weighting to identify the scapegoat drivers of customer order flows. Our findings suggest that heterogeneous agents learn to forecast exchange rates and switch trading rules over time, resulting in the dynamic country-specific and global exposures of exchange rates to shortrun non-fundamental risk and long-run business cycle risk. Hedging pressure and liquidity are identified to contain predictive information that is common to a range of forecasting horizons. Policy-related predictors are important for short-run forecasts up to 3 months while crash risk indicators matter for long-run forecasts from 9 months to 12 months. We further comprehensively evaluate both statistical and economic significance of the model allowing for a full spectrum of currency investment management, and find that the model generates substantial performance fees of 6.5% per annum. The outperformance is mainly due to (i) the relaxing of restrictions imposed on structural parameters via model generalization, and (ii) the use of factor structure to extract common useful information from noisy data and reduce estimation errors.