This paper examines whether recessions and booms are forecastable under the assumption that equity prices, housing prices, import prices, exports, and random shocks are not. Each of the 214 eight-quarter periods within the overall 1954:1–2009:1 period is examined regarding predictions of output growth and in ation. The results for low output growth vary by recession— there is no common pattern. Of the eight recessions, three are forecast well. For four of the ve that are not, the main reason for each is not knowing: 1) the random shocks, 2) import prices and equity prices, 3) exports, and 4) exports and equity prices. For the fth—the last one—all ve components are large contributors, including housing prices: a perfect storm. 1

Guéné for introducing me to the topic and for the supervision. Special thanks to Jesper Berg, Angela Maddaloni, Antonello D'Agostino and especially John Fell for useful comments and discussions. I also wish to thanks an anonymous referee and the Editorial Board of the ECB's Working paper series for helpful and constructive comments. However, any errors remain the responsibility of the author.The views expressed in this paper are solely those of the author and not necessarily those of the European Central Bank.This paper can be downloaded without charge from

This article surveys recent research on the usefulness of the term spread (i.e., the difference between the yields on long-term and short-term Treasury securities) for predicting changes in economic activity. Most studies use linear regression techniques to forecast changes in output or dichotomous choice models to forecast recessions. Others use time-varying parameter models, such as Markov-switching models and smooth transition models, to account for structural changes or other nonlinearities. Many studies find that the term spread predicts output growth and recessions up to one year in advance, but several also find its usefulness varies across countries and over time. In particular, many studies find that the ability of the term spread to forecast output growth has diminished in recent years, although it remains a reliable predictor of recessions.

NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

revised:May13,2010 This paper surveys efforts to automate the dating of business cycle turning points. Doing this on a real time, out-of-sample basis is a bigger challenge than many academics might presume due to factors such as data revisions and changes in economic relationships over time. The paper stresses the value of both simulated real-time analysis — looking at what the inference of a proposed model would have been using data as they were actually released at the time — and actual real-time analysis, in which a researcher stakes his or her reputation on publicly using the model to generate out-of-sample, real-time predictions. The immediate publication capabilities of the internet make the latter a realistic option for researchers today, and many are taking advantage of it. The paper reviews a number of approaches to dating business cycle turning points and emphasizes the fundamental trade-off between parsimony — trying to keep the model as simple and robust as possible — and making full use of available information. Different approaches have different advantages, and the paper concludes that there may be gains from combining the best features of several different approaches.

This paper examines whether recessions and booms are forecastable under the assumption that equity prices, housing prices, import prices, exports, and random shocks are not. Each of the 214 eight-quarter periods within the overall 1954:1–2009:1 period is examined regarding predictions of output growth and in ation. The results for low output growth vary by recession— there is no common pattern. Of the eight recessions, three are forecast well. For four of the ve that are not, the main reason for each is not knowing: 1) the random shocks, 2) import prices and equity prices, 3) exports, and 4) exports and equity prices. For the fth—the last one—all ve components are large contributors, including housing prices: a perfect storm. 1

Several studies have established the predictive power of the yield curve in terms of real economic activity. In this paper we use data for a variety of E.U. countries: both

I propose a framework for the assessment of current business conditions using a factor-augmented autoregressive probit model where the dependent variable is the state of the economy as defined by the NBER. Results show that latent common factors estimated by principal components analysis from a large number of macroeconomic series have important predictive power for NBER recession dates. The models generate in-sample recession probabilities that almost perfectly reproduce NBER dates. A pseudo out-of-sample forecasting exercise, designed to approximate real time conditions, shows that predicted recession probabilities consistently rise during subsequently declared NBER recession dates. With the appropriate classification rule, the models exhibit good performance as real time dating algorithms. The latent variable in the probit model can be interpreted as an index of business conditions which is used to assess the strength of an expansion or the depth of a recession.

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This paper estimates, using stochastic simulation and a multicountry macroeconometric model, the fraction of the forecast-error variance of output changes and the fraction of the forecast-error variance of inflation that are due to unpredictable asset-price changes. The results suggest that between about 25 and 37 percent of the forecast-error variance of output growth over 8 quarters is due to asset-price changes and between about 33 and 60 percent of the forecast-error variance of inflation over 8 quarters is due to asset-price changes. These estimates provide limits to the accuracy that can be expected from macroeconomic forecasting. 1