Estimating Fundamental Cross-Section Dispersion from Fixed Event Forecasts

A couple of recent papers have shifted the focus towards disagreement of professional forecasters. When dealing with survey data that is sampled at a frequency higher than annual and that includes only fixed event forecasts, e.g. expectation of average annual growth rates measures of disagreement across forecasters naturally are distorted by a component that mainly reflects the time varying forecast horizon. We use data from the Survey of Professional Forecasters, which reports both fixed event and fixed horizon forecasts, to evaluate different methods for extracting the "fundamental" component of disagreement. Based on the paper's results we suggest two methods to estimate dispersion measures from panels of fixed event forecasts: a moving average transformation of the underlying forecasts and estimation with constant forecast-horizon- effects. Both models are easy to handle and deliver equally well performing results, which show a surprisingly high correlation (up to 0:94) with the true dispersion.Survey data, dispersion, disagreement, fixed event forecasts

Estimating fundamental cross-section dispersion from fixed event forecasts

A couple of recent papers have shifted the focus towards disagreement of professional forecasters. When dealing with survey data that is sampled at a frequency higher than annual and that includes only fixed event forecasts, e.g. expectation of average annual growth rates measures of disagreement across forecasters naturally are distorted by a component that mainly reflects the time varying forecast horizon. We use data from the Survey of Professional Forecasters, which reports both fixed event and fixed horizon forecasts, to evaluate different methods for extracting the ``fundamental'' component of disagreement. Based on the paper's results we suggest two methods to estimate dispersion measures from panels of fixed event forecasts: a moving average transformation of the underlying forecasts and estimation with constant forecast-horizon-effects. Both models are easy to handle and deliver equally well performing results, which show a surprisingly high correlation (up to 0.94) with the true dispersion.survey data, dispersion, disagreement, fixed event forecasts

Prediction intervals for economic fixed-event forecasts

The fixed-event forecasting setup is common in economic policy. It involves a sequence of forecasts of the same (`fixed') predictand, so that the difficulty of the forecasting problem decreases over time. Fixed-event point forecasts are typically published without a quantitative measure of uncertainty. To construct such a measure, we consider forecast postprocessing techniques tailored to the fixed-event case. We develop regression methods that impose constraints motivated by the problem at hand, and use these methods to construct prediction intervals for gross domestic product (GDP) growth in Germany and the US

Analyzing Fixed-event Forecast Revisions

It is common practice to evaluate fixed-event forecast revisions in macroeconomics by regressing current revisions on one-period lagged revisions. Under weak-form efficiency, the correlation between the current and one-period lagged revisions should be zero. The empirical findings in the literature suggest that the null hypothesis of zero correlation between the current and one-period lagged revisions is rejected quite frequently, where the correlation can be either positive or negative. In this paper we propose a methodology to be able to interpret such non-zero correlations in a straightforward manner. Our approach is based on the assumption that forecasts can be decomposed into both an econometric model and expert intuition. The interpretation of the sign of the correlation between the current and one-period lagged revisions depends on the process governing intuition, and the correlation between intuition and news.Evaluating forecasts, Macroeconomic forecasting, Rationality, Intuition, Weak-form efficiency, Fixed-event forecasts.

Analyzing Fixed-event Forecast Revisions

It is common practice to evaluate fixed-event forecast revisions in macroeconomics by regressing current revisions on one-period lagged revisions. Under weak-form efficiency, the correlation between the current and one-period lagged revisions should be zero. The empirical findings in the literature suggest that the null hypothesis of zero correlation between the current and one-period lagged revisions is rejected quite frequently, where the correlation can be either positive or negative. In this paper we propose a methodology to be able to interpret such non-zero correlations in a straightforward manner. Our approach is based on the assumption that forecasts can be decomposed into both an econometric model and expert intuition. The interpretation of the sign of the correlation between the current and one-period lagged revisions depends on the process governing intuition, and the correlation between intuition and news.Evaluating forecasts; Macroeconomic forecasting; Rationality; Intuition; Weak-form efficiency; Fixed-event forecasts

Accuracy, Unbiasedness and Efficiency of Professional Macroeconomic Forecasts: An empirical Comparison for the G7

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.Evaluating forecasts, Macroeconomic Forecasting, Rationality, Survey Data, Fixed-Event Forecasts

What Can we Learn from our Mistakes? Evaluating the Benefits of Correcting Inefficiencies in USDA Cotton Forecasts.

This study investigated the magnitude of forecast improvements resulting from correction of inefficiencies in USDA cotton forecasts over 1999/00 to 2008/09 marketing years. The aspects of forecast performance included in this study were 1) bias and trends in bias, 2) correlation between forecast error and forecast level, 3) autocorrelation in forecast errors, 4) correlation in forecast revisions. Overall the results of this study demonstrated that some corrections of forecast inefficiencies, such as correction of correlation of error with forecast levels and correlation of error with previous year’s error resulted in consistent improvement of USDA cotton forecasts, while correction for correlation in forecast revisions did not benefit the forecasts. Correction for bias yielded mixed results likely because USDA has already been applying those corrections to some of the categories and thus our analysis resulted in over-correcting. The framework developed in this study can be used by USDA and other agencies to monitor and improve the performance of their forecasts.Commodity, Forecast evaluation, Fixed-event forecasts, Government forecasting, Forecast improvement, Agribusiness, Demand and Price Analysis, E37, E3, Q13,

Analyzing Fixed-event Forecast Revisions

It is common practice to evaluate fixed-event forecast revisions in macroeconomics by regressing current revisions on one-period lagged revisions. Under weak-form efficiency, the correlation between the current and one-period lagged revisions should be zero. The empirical findings in the literature suggest that the null hypothesis of zero correlation between the current and one-period lagged revisions is rejected quite frequently, where the correlation can be either positive or negative. In this paper we propose a methodology to be able to interpret such non-zero correlations in a straightforward manner. Our approach is based on the assumption that forecasts can be decomposed into both an econometric model and expert intuition. The interpretation of the sign of the correlation between the current and one-period lagged revisions depends on the process governing intuition, and the correlation between intuition and news.Evaluating forecasts, Macroeconomic forecasting, Rationality, Intuition, Weak-form efficiency, Fixed-event forecasts.

Forecast revisions of Mexican inflation and GDP growth

We analyze the forecasts of inflation and GDP growth contained in the Banco de México’s Survey of Professional Forecasters for the period 1995–2009. The forecasts are for the current and the following year, and comprise 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 their 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. We find that respondents anchor to their initial forecasts, updating their revisions smoothly as they receive more information. In addition, they do not seem to use publicly-known information in an efficient manner. These inefficiencies suggest clear areas of opportunity for improving the accuracy of the forecasts, for instance by taking into account the positive autocorrelation found in forecast revisions