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)

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This article discusses the history underlying the new literature, describes some of what has been learned thus far, and looks ahead towards making further progress

Reserve Board, and George Washington University, as well as those at the Midwest

In the presence of principal-agent problems, published macroeconomic forecasts by professional economists may not measure expectations. Forecasters may use their forecasts in order to manipulate beliefs about their ability. I test a cross-sectional implication of models of reputation and information-revelation. I find that as forecasters become older and more established, they produce more radical forecasts. Since these more radical forecasts are in general less accurate, ex post forecast accuracy grows significantly worse as forecasters become older and more established. These findings are consistent with reputational factors at work in professional macroeconomic forecasts.

Recently, several central banks have lost their bank supervisory responsibilities, in part because it has not been shown that supervisory authority improves the conduct of monetary policy. This paper finds that confidential bank supervisory information could help the Board staff more accurately forecast important macroeconomic variables and is used by FOMC members to guide monetary policy. These findings suggest that the complementarity between supervisory responsibilities and monetary policy should be an important consideration when evaluating the structure of the central bank.

This paper develops and compares two theories of strategic behavior of professional forecasters. The first theory posits that forecasters compete in a forecasting contest with pre-specified rules. In equilibrium of a winner-take-all contest, forecasts are excessively differentiated. According to the alternative reputational cheap talk theory, forecasters aim at convincing the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts and the realized state. If the market expects forecaster honesty, forecasts are shaded toward the prior mean. With correct market expectations, equilibrium forecasts are imprecise but not shaded.

Even in a world with rational expectations, it has been well established theoretically that if the central bank possesses information superior to that available to the public, there is room for effective and socially beneficial countercyclical monetary policy. This paper tests whether confidential information from bank supervisors could be one source of any such informational advantage. In particular, we examine whether information gained from bank supervision activities could substantially improve the forecasts of macroeconomic variables important for guiding monetary policy. We find that confidential supervisory information on bank ratings significantly improves private forecasts of inflation and unemployment rates, thus providing an informational advantage to the Federal Reserve.

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.

Household surveys often elicit respondents' intentions or predictions of future outcomes. The survey questions may ask respondents to choose among a selection of (ordered) response categories. If panel data or repeated cross-sections are available, predictions may be compared with realized outcomes. The categorical nature of the predictions data, however, complicates this comparison. Generalizing previous findings on binary intentions data, we derive bounds on features of the empirical distribution of realized outcomes under the "best-case" hypothesis that respondents have rational expectations and that reported expectations are best predictions of future outcomes. These bounds are shown to depend on the assumed model of how respondents form their "best prediction" when forced to choose among (ordered) categories. An application to data on income change expectations and realized income changes illustrates how alternative response models may be used to test the best-case hyp...