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It is common practice to evaluate fixed-event forecast revisions in macroeconomics by regressing current forecast revisions on one-period lagged forecast revisions. Under weak-form (forecast) efficiency, the correlation between the current and one-period lagged revisions should be zero. The empirical findings in the literature suggest that this null hypothesis of zero correlation is rejected frequently, where the correlation can be either positive (which is widely interpreted in the literature as “smoothing”) or negative (which is widely interpreted as “over-reacting”). We propose a methodology to interpret such non-zero correlations in a straightforward and clear manner. Our approach is based on the assumption that numerical forecasts can be decomposed into both an econometric model and random expert intuition. We show that the interpretation of the sign of the correlation between the current and one-period lagged revisions depends on the process governing intuition, and the current and lagged correlations between intuition and news (or shocks to the numerical forecasts). It follows that the estimated non-zero correlation cannot be given a direct interpretation in terms of smoothing or overreaction.

Nowcasting has been a challenge in the recent economic crisis. We introduce the Toll Index, a new monthly indicator for business cycle forecasting and demonstrate its relevance using German data. The index measures the monthly transportation activity performed by heavy transport vehicles across the country and has highly desirable availability properties (insignificant revisions, short publication lags) as a result of the innovative technology underlying its data collection. It is coincident with production activity due to the prevalence of just-in-time delivery. The Toll Index is a good early indicator of production as measured for instance by the German Production Index, provided by the German Statistical Office, which is a well-known leading indicator of the Gross National Product. The proposed new index is an excellent example of technological, innovation-driven economic telemetry, which we suggest should be established more around the world.

We analyze forecasts of inflation and GDP growth contained in Banco de México's Survey of Professional Forecasters for the period 1995-2009. The forecasts are for the current and the following year, comprising an unbalanced three-dimensional panel with multiple individual forecasters, target years, and forecast horizons. The fixed-event nature of the forecasts enables us to examine efficiency by looking at the revision process. The panel structure allows us to control for aggregate shocks and to construct a measure of the news that impacted expectations in the period under study. The results suggest that respondents seem to rely for longer than appears to be optimal on their previous forecasts, and that they do not seem to use past information in an efficient manner. In turn, this means there are areas of opportunity to improve the accuracy of the forecasts, for instance, by taking into account the positive autocorrelation found in forecast revisions.

In this paper, we use survey data to analyze the accuracy, unbiasedness, and the efficiency of professional macroeconomic forecasts. We analyze a large panel of individual forecasts that has not been analyzed in the literature so far. We provide evidence on the properties of forecasts for all G7 counties and for four diffierent macroeconomic variables. Our results show a high degree of dispersion of forecast accuracy across forecasters. We also find that there are large diffierences in the performance of forecasters not only across countries but also across diffierent macroeconomic variables. In general, forecasts tend to be biased in situations where forecasters have to respond to large structural shocks or gradual changes in the trend of a variable. Furthermore, while a sizable fraction of forecasters seem to smooth their GDP forecasts significantly, this does not apply to forecasts made for other macroeconomic variables.

In this paper, we use survey data to analyze the rationality of professional macroeconomic forecasts. We analyze both individual forecasts and average forecasts. We provide evidence on the properties of forecasts for all the G7-counties and four different macroeconomic variables. Furthermore, we present a modification to the structural model which is commonly used to model the forecast errors of fixed event forecasts in the literature. Our results confirm that average forecasts should be used with caution, since even if all individual forecasts are rational the hypothesis of rationality is often rejected by the aggregate forecasts. We find that there are not only large differences in the performance of forecasters across countries but also across different macroeconomic variables; in general, forecasts tend to be biased in situations where forecasters have to learn about large structural shocks or gradual changes in the trend of a variable.

Clark and McCracken (2008) argue that combining real-time point forecasts from VARs of output, prices and interest rates improves point forecast accuracy in the presence of uncertain model instabilities. In this paper, we generalize their approach to consider forecast density combinations and evaluations. Whereas Clark and McCracken (2008) show that the point forecast errors from particular equal-weight pairwise averages are typically comparable or better than benchmark univariate time series models, we show that neither approach produces accurate real-time forecast densities for recent US data. If greater weight is given to models that allow for the shifts in volatilities associated with the Great Moderation, predictive density accuracy improves substantially.