This paper investigates the source of predictability of bond risk premia by means of long-term forward interest rates. We show that the predictive ability of forward rates could be due to the high serial correlation and cross-correlation of bond prices. We show that the predictive ability of...

We examine the forecasting performance of a range of time-series models of the daily US effective federal funds (FF) rate recently proposed in the literature. We find that: (i) most of the models and predictor variables considered produce satisfactory one-day-ahead forecasts of the FF rate; (ii)...

This paper tests the expectations hypothesis (EH) using US monthly data for bond yields spanning the 1952-2003 sample period and ranging in maturity from 1 month to 10 years. We apply the Lagrange multiplier test developed by Bekaert and Hodrick (2001) and extend it to increase the test power:...

This paper revisits the predictability of bond excess returns by means of long-term forward interest rates. We assess the economic value of out-of-sample forecasting ability of empirical models based on forward rates in a dynamic asset allocation strategy. Our results show that the information...

This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy applied to direct, multi-step predictions from both non-nested and nested linear regression models. In contrast to earlier work in the literature, our asymptotics take account of the real-time,...

This paper presents analytical, Monte Carlo, and empirical evidence on combining recursive and rolling forecasts when linear predictive models are subject to structural change. Using a characterization of the bias-variance tradeoff faced when choosing between either the recursive and rolling...

Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. The uncertainty inherent in any single...

Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper...

Motivated by the common finding that linear autoregressive models often forecast better than models that incorporate additional information, this paper presents analytical, Monte Carlo, and empirical evidence on the effectiveness of combining forecasts from nested models. In our analytics, the...

This paper develops the theory of multi-step ahead forecasting for vector time series that exhibit temporal nonstationarity and co-integration. We treat the case of a semi-infinite past, developing the forecast filters and the forecast error filters explicitly, and also provide formulas for...