It is obvious that forecasts are of great importance and widely used in economics and finance. Quite simply, good forecasts lead to good decisions. The importance of forecast evaluation and combination techniques follows immediately-- forecast users naturally have a keen interest in monitoring and improving forecast performance. More generally, forecast evaluation figures prominently in many questions in empirical economics and finance, such as: Are expectations rational? (e.g., Keane and Runkle, 1990; Bonham and Cohen, 1995) Are financial markets efficient? (e.g., Fama, 1970, 1991) Do macroeconomic shocks cause agents to revise their forecasts at all horizons, or just at short- and medium-term horizons? (e.g., Campbell and Mankiw, 1987; Cochrane, 1988) Are observed asset returns "too volatile"? (e.g., Shiller, 1979; LeRoy and Porter, 1981) Are asset returns forecastable over long horizons? (e.g., Fama and French, 1988; Mark, 1995)

This paper develops generalized method-of-moments tests for the rationality of earnings per share forecasts made by individual stock analysts. We fail to reject the hypothesis of rationality as long as we take into account two complications: (1) the correlation in a given period of analysts ’ forecast errors in predicting earnings for firms in the same industry and (2) discretionary asset write-downs, which affect earnings but are intentionally ignored by analysts when they make earnings forecasts. Our results challenge earlier work by De Bondt and Thaler and by Abarbanell and Bernard that found irrationality in analysts ’ forecasts. I.

This paper focusses on survey expectations and discusses their uses for testing and modeling of expectations. Alternative models of expectations formation are reviewed and the importance of allowing for heterogeneity of expectations is emphasized. A weak form of the rational expectations hypothesis which focusses on average expectations rather than individual expectations is advanced. Other models of expectations formation, such as the adaptive expectations hypothesis, are briefly discussed. Testable implications of rational and extrapolative models of expectations are reviewed and the importance of the loss function for the interpretation of the test results is discussed. The paper then provides an account of the various surveys of expectations, reviews alternative methods of quantifying the qualitative surveys, and discusses the use of aggregate and individual survey responses in the analysis of expectations and for forecasting.

This article will focus more on the details of the survey and its value in research.

Empirical studies using survey data on expectations have frequently observed that forecasts are biased and have concluded that agents are not rational. We establish that existing rationality tests are not robust to even small deviations from symmetric loss and hence have little ability to tell whether the forecaster is irrational or the loss function is asymmetric. We quantify the exact trade-off between forecast inefficiency and asymmetric loss leading to identical outcomes of standard rationality tests and explore new and more general methods for testing forecast rationality jointly with flexible families of loss functions that embed quadratic loss as a special case. An empirical application to survey data on forecasts of nominal output growth demonstrates the empirical significance of our results and finds that rejections of rationality may largely have been driven by the assumption of symmetric loss.

The asymptotic refinements attributable to the block bootstrap for time series are not as large as those of the nonparametric iid bootstrap or the parametric bootstrap. One reason is that the independence between the blocks in the block bootstrap sample does not mimic the dependence structure of the original sample. This is the joinpoint problem. In this paper, we propose a method of solving this problem. The idea is not to alter the block bootstrap. Instead, we alter the original sample statistics to which the block bootstrap is applied. We introduce block statistics that possess join-point features that are similar to those of the block bootstrap versions of these statistics. We refer to the application of the block bootstrap to block statistics as the block–block bootstrap. The asymptotic refinements of the block–block bootstrap are shown to be greater than those obtained with the block bootstrap and close to those obtained with the nonparametric iid bootstrap and parametric bootstrap.

This paper carries out the task of evaluating inflation forecasts from the Livingston survey and Survey of Professional Forecasters, using the Real- Time Data Set for Macroeconomists as a source of real-time data. We examine the magnitude and patterns of revisions to the inflation rate based on the output price indexand describe what data to use as "actuals" in evaluating forecasts. We then run tests on the forecasts from the surveys to see how good they are, using a variety of actuals. We find that much of the empirical work from 20 years ago was a misleading guide to the quality of forecasts because of unique events during the earlier sample period. Repeating that empirical work over a longer sample period shows no bias or other problems in the forecasts. The use of real-time data also matters for some key tests on some variables. If a forecaster had used the empirical results from the late 1970s and early 1980s to adjust survey forecasts of inflation, forecast errors would have increased substantially. AN EVALUATION OF INLATION FORECASTS FROM SURVEYS USING REAL-TIME DATA An earlier paper, Croushore (2005) examined forecasts of consumer price inflation to test whether such forecasts from surveys (Michigan, Livingston, and Survey of Professional Forecasters) were biased and inefficient, as researchers found in the early 1980s. That paper ran the same types of tests that were performed 20 years ago on an updated sample, and found that the forecasts were neither biased nor inefficient. Arguably, consumer price inflation is not the best measure of inflation because of index construction problems. Better measures of trend inflation come from other variables, such as the GDP deflator. But forecasts of the inflation rate in the GDP deflator are more difficult to evaluate bec...

this paper was simple. As an editor of Econometrica 1 Sims was handling a paper by Brown and Maital (1981) on assessing the e#ciency of multi-period forecasts. The multi-period nature induced temporal dependence in the disturbance term. Sims urged me to proceed quickly to get something written up as a paper that could be cited and used. Hodrick (my colleague at Carnegie-Mellon at the time) made me aware of similar problems in the literature on the study for forward exchange rates as predictors of future spot rates. When the forward contract interval exceeds the sampling interval, temporal dependence is induced. Given the serial correlation in the disturbance term, many applied researchers at the time thought the right thing to do was to use a standard GLS (generalized least squares) type correction. In fact, while least squares remains consistent, the lack of strict exogeneity of the regressors prevents GLS from being consistent. This tripped up several researchers and was the impetus for my original least squares paper. In addition to the Brown and Maital paper, which with Sims' influence proceeded correctly, Hodrick quickly informed authors of the papers submitted to the Journal of Political Economy that the GLS style correction they used was invalid

Essay 4—The accuracy of European growth and inflation forecasts Abstract. One-year-ahead forecasts by the OECD and by national institutes of GDP growth and inflation in 13 European countries are analysed. RMSE was large: 1.9 % for growth and 1.6 % for inflation. Six (11) OECD and 10 (7) institute growth forecast records were significantly better than an average growth forecast (the current year forecast). All full record-length inflation forecasts were significantly better than both naive alternatives. There was no significant difference in accuracy between the forecasts of the OECD and the institutes. Two forecasts were found to be biased and one had auto correlated errors. Directional forecasts were significantly better than a naive alternative in one-half of the cases. Overall, inflation forecasts were significantly more accurate than growth forecasts, and in contrast to growth forecasts, they generally improved over time. This has implications for economic policy. Positively biased revisions reveal large errors in data.

the project and provide a non-technical summary, a full report and a list of papers produced on the project. 1 Aims of the Project 1. To estimate disaggregate models of employment, prices and wages for the major industrial sectors of the UK, emphasising the role of expectations and the interactions that exist between these sectors in the determination of aggregate employment level and the overall rate of inflation. 2. To consider different expectations formation hypotheses in the context of these disaggregated models, noting the significance of the process of expectations formation in the transmission of output and inflation effects from one sector of the economy to another. 3. To investigate empirically competing hypotheses on the workings of the labour market using direct observations on expectations. Specifically, we will consider the role of expected output levels in employment equations, the role of inflation expecations and other variables, both internal and external to the industry, in wage equations, and the role of demand and cost expectations in the determination of the markup over the cycle in price equations, all estimated at the disaggregated level. 4. To investigate the potential in the UK for influencing the expectations formation process by