We show how the Joslin, Singleton, and Zhu (2011) factor extraction approach to estimating the Gaussian term structure model can be modified to handle the interest rate lower bound without the approximations used in other approaches. This drastically reduces the computation time and produces more robust estimates of the lower bound parameter and the shadow rate. It makes feasible the extensive specification search necessary to allow for unspanned factors as in Joslin, Priebsch, and Singleton (2014), allowing the term structure model to be used to better assess the effects of policy on the term premium and market expectations.

This paper develops a multi-country macro-finance model to study international economic and financial linkages. This approach models the economy and financial markets jointly using both types of data to throw light on such issues. The world economy is modelled using data for the US and aggregate OECD economies as well as the US Treasury bond market using latent variables to represent a common inflation trend and a US real interest rate factor. We find strong evidence of global effects on both the US and UK, calling into question the standard closed economy macro-finance specification. These economic linkages also help to explain the co-movement of yields in the US and UK Treasury bond markets.

We advocate the use of excess returns rather than yields or log prices in analysing the risk neutral dynamics of the term structure. We show that under standard assumptions, excess returns are affine in the risk neutral innovations in the factors. This framework has several important advantages. First, it allows for an easy estimation of models that are more flexible than the AR(1). Indeed, we estimate models with more general dynamics, like ARFIMA(p, d, q), almost as easily as AR(1). Second, within our framework the dimension of the unrestricted model is the same for the AR(1) as it is for the richer models, and does not expand in line with the state vector as it does in a yield or log price framework. This makes it appropriate to test all of these risk neutral dynamic specifications against the same OLS unrestricted alternative. Our results for the US Treasury bond market show that the unrestricted model is preferred to the AR(1) by the Bayesian Information Criterion, but the opposite conclusion is reached for more flexible models. A final advantage of the excess returns framework is that the pricing errors are much lower than for the equivalent log price system.