This paper develops a flexible approach to combine forecasts of future spot rates with forecasts from time-series models or macroeconomic variables. We find empirical evidence that accounting for both regimes in interest rate dynamics and combining forecasts from different models helps improve...

We address one interesting case — the predictability of excess US asset returns from macroeconomic factors within a flexible regime switching VAR framework — in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After having documented that...

We perform a comprehensive examination of the recursive, comparative predictive performance of a number of linear and non-linear models for UK stock and bond returns. We estimate Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STR) regime switching models,...

Using data on international, on-line media coverage and tone of the Brexit referendum, we test whether it is media coverage or tone to provide the largest forecasting performance improvements in the prediction of the conditional variance of weekly FTSE 100 stock returns. We find that versions of...

This paper argues that inferring long-horizon asset-return predictability from the properties of vector autoregressive (VAR) models on relatively short spans of data is potentially unreliable. We illustrate the problems that can arise by re-examining the findings of Bekaert and Hodrick (1992),...

As part of the Fed's daily operating procedure, the Federal Reserve Bank of New York, the Board of Governors, and the Treasury make a forecast of that day's Treasury balance at the Fed. These forecasts are an integral part of the Fed's daily operating procedure. Errors in these forecasts can...

We construct a parsimonious model of the U.S. macro economy using a state space representation and recursive estimation. At the core of the estimation procedure is a prediction/correction algorithm based on a recursive least squares estimation with exponential forgetting. The algorithm is a...

We distinguish between three different ways of using real-time data to estimate forecasting equations and argue that the most popular approach should generally be avoided. The point is illustrated with a model that uses monthly industrial production, employment, and retail sales data to predict...