Because asset prices are forward-looking, they constitute a class of potentially useful predictors of inflation and output growth. The premise that interest rates and asset

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This paper revisits the yield spread’s usefulness for predicting future real GDP growth. We show that the contribution of the spread can be decomposed into the effect of expected future changes in short rates and the effect of the term premium. We find that both factors are relevant for predicting real GDP growth but the respective contributions differ. We investigate whether the cyclical behavior of interest rate volatility could account for either or both effects. We find that while volatility displays important correlations with both the term structure of interest rates and GDP, it does not appear to account for the yield spread's usefulness for predicting GDP growth.

Predicting economic activity is important for numerous reasons. It is important for business firms because it aids in deciding how much capacity will be needed to meet future demand. It is important for various government agencies when forecasting budgetary surpluses or deficits. And it is important for the Federal Reserve (the Fed) in deciding the stance of current monetary policy. One set of variables that are potentially useful in forecasting economic activity are financial variables. Financial market participants are forward-looking, and as a result the prices of various securities embody expectations of future economic activity. This pricing behavior implies that data from financial markets may reasonably be expected to help forecast the growth rate of the economy. Using financial variables to aid in economic projections, therefore, is fairly commonplace. In particular, the yield curve spread between long- and short-term interest rates has received a lot of recent attention. Although not the first to consider the implications that the spread has for predicting economic activity, Stock and

This study uses Markov-switching models to evaluate the informational content of the term structure as a predictor of recessions in eight OECD countries. The empirical results suggest that for all countries the term spread is sensibly modelled as a two-state regimeswitching process. Moreover, our simple univariate model turns out to be a filter that transforms accurately term spread changes into turning point predictions. The term structure is confirmed to be a reliable recession indicator. However, the results of probit estimations show that the markov-switching filter does not significantly improve the forecasting ability of the spread.

w o r k i n g

In this chapter we provide a guide for the construction, use and evaluation of leading indicators, and an assessment of the most relevant recent developments in this field of economic forecasting. To begin with, we analyze the problem of indicator selection, choice of filtering methods, business cycle dating procedures to transform a continuous variable into a binary expansion/recession indicator, and methods for the construction of composite indexes. Next, we examine models and methods to transform the leading indicators into forecasts of the target variable. Finally, we consider the evaluation of the resulting leading indicator based forecasts, and review the recent literature on the forecasting performance of leading indicators.

This paper revisits the yield spread's usefulness for predicting future real GDP growth. We show that the contribution of the spread can be decomposed into the effect of expected future changes in short rates and the effect of the term premium. We find that both factors are relevant for predicting real GDP growth but the respective contributions differ. We account for part of the contribution of the term premium using a logarithmic term structure model with a time-varying variance of the inflation forecast error. We find that the variance of inflation is a significant component of the term premium and part of the reason that the term premium helps predict real GDP growth. . JEL Classification: E32, E37, E43 * We thank Marjorie Flavin, Wouter den Haan, Valerie Ramey and seminar participants at the UCSD macroeconomics workshop for helpful comments. 2 1. Introduction A large literature has examined variables that help predict the business cycle. Interest rates and interest rate spread...

We evaluate various economic models ’ relative performance in forecasting future US output growth and inflation on a monthly basis. Our approach takes into account the possibility that the models ’ relative performance can be varying over time. We show that the models’ relative performance has, in fact, changed dramatically over time, both for revised and real-time data, and investigate possible factors that might explain such changes. In addition, this paper establishes two empirical stylized facts. Namely, most predictors for output growth lost their predictive ability in the mid-1970s, and became essentially useless in the last two decades. When forecasting inflation, instead, fewer predictors are significant, and their predictive ability significantly worsened around the time of the Great Moderation.

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