Decision makers, both public and private, use forecasts of economic growth and inflation to make plans and implement policies. In many situations, reasonably good forecasts can be made with simple rules of thumb that are extrapolations of a single data series. In principle, information about...

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...

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...

Address before the Charlotte Economics Club, Charlotte, N.C., Feb. 25, 2004