Predicting Real Growth Using the Yield Curve

Abstract

This article builds on a wide range of previous research, but, taking an eclectic approach, differs from the earlier work in a variety of ways. These differences show up mainly in the way we judge forecast performance. Like the important early work of Harvey (1989, 1991, 1993) and Hu (1993), we use outof -sample forecasts and compare yield curve forecasts with other predictions (including professional forecasts), but we extend our data set to the mid-1990s. In addition, we consider how adding the yield curve improves (or reduces) the accuracy of other forecasts. In this, we follow Estrella and Hardouvelis (1991), who do not, however, use out-of-sample forecasts. Finally, building on the recent work of Estrella and Mishkin (1995, 1996), we consider how well the yield curve predicts the severity of recessions, not just their probability, and compare the forecasts with a wider range of alternatives. n 1 Yield curve reports appear in the "Credit Markets" section of The Wall Street Journal and the "Business Day" section of The New York Times. 27 The most distinguishing feature of this paper, however, is that it documents the decline in the yield curve's predictive ability over the past decade (1985--95) and discusses possible reasons for this phenomenon. By some measures, the yield curve should be an even better predictor now than it has been in the past. Widespread use of the yield curve makes assessing its accuracy a worthwhile exercise for economists. But policymakers, too, need an accurate and timely predictor of future economic growth. The ready availability of term-structure data (as opposed to, say, quarterly GDP numbers) ensures a timely prediction, but accuracy is another question. Central bankers have an added incentive to understand the yield curve, since the fe...