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This paper argues for a careful (re)consideration of the expectations formation process and a more systematic inclusion of real-time expectations through survey data in macroeconomic analyses. While the rational expectations revolution has allowed for great leaps in macroeconomic modeling, the surveyed empirical microevidence appears increasingly at odds with the full-information rational expectation assumption. We explore models of expectation formation that can potentially explain why and how survey data deviate from full-information rational expectations. Using the New Keynesian Phillips curve as an extensive case study, we demonstrate how incorporating survey data on inflation expectations can address a number of otherwise puzzling shortcomings that arise under the assumption of full-information rational expectations.

We propose a new approach to test the full-information rational expectations hypothesis which can identify whether rejections of the null arise from information rigidities. This approach quantifies the economic significance of departures from the null and the underlying degree of information rigidity. Applying this approach to US and international data of professional forecasters and other agents yields pervasive evidence consistent with the presence of information rigidities. These results therefore provide a set of stylized facts which can be used to calibrate imperfect information models. Finally, we document evidence of state-dependence in the expectations formation process.

This paper develops a new theory of fluctuations—one that helps accommodate the notions of \"animal spirits\" and \"market sentiment\" in unique-equilibrium, rational-expectations, macroeconomic models. To this goal, we limit the communication that is embedded in a neoclassical economy by allowing trading to be random and decentralized. We then show that the business cycle may be driven by a certain type of extrinsic shocks which we call sentiments. These shocks formalize shifts in expectations of economic activity without shifts in the underlying preferences and technologies; they are akin to sunspots, but operate in unique-equilibrium models. We further show how communication may help propagate these shocks in a way that resembles the spread of fads and rumors and that gives rise to boom-and-bust phenomena. We finally illustrate the quantitative potential of our insights within a variant of the RBC model.

We analyze time series of investor expectations of future stock market returns from six data sources between 1963 and 2011. The six measures of expectations are highly positively correlated with each other, as well as with past stock returns and with the level of the stock market. However, investor expectations are strongly negatively correlated with model-based expected returns. The evidence is not consistent with rational expectations representative investor models of returns.

Investors' subjective capital gains expectations are a key element explaining stock price fluctuations. Survey measures of these expectations display excessive optimism (pessimism) at market peaks (troughs). We formally reject the hypothesis that this is compatible with rational expectations. We then incorporate subjective price beliefs with such properties into a standard asset-pricing model with rational agents (internal rationality). The model gives rise to boom-bust cycles that temporarily delink stock prices from fundamentals and quantitatively replicates many asset-pricing moments. In particular, it matches the observed strong positive correlation between the price dividend ratio and survey return expectations, which cannot be matched by rational expectations.

A key open question for theories of reference-dependent preferences is: what determines the reference point? One candidate is expectations: what people expect could affect how they feel about what actually occurs. In a real-effort experiment, we manipulate the rational expectations of subjects and check whether this manipulation influences their effort provision. We find that effort provision is significantly different between treatments in the way predicted by models of expectation-based, reference-dependent preferences: if expectations are high, subjects work longer and earn more money than if expectations are low.

A large body of empirical evidence suggests that beliefs systematically deviate from perfect rationality. Much of the evidence implies that economic agents tend to form forecasts that are excessively influenced by recent changes. We present a parsimonious quasi-rational model that we call natural expectations, which falls between rational expectations and (naïve) intuitive expectations. (Intuitive expectations are formed by running growth regressions with a limited number of right-hand-side variables, and this leads to excessively extrapolative beliefs in certain classes of environments). Natural expectations overstate the long-run persistence of economic shocks. In other words, agents with natural expectations turn out to form beliefs that don't sufficiently account for the fact that good times (or bad times) won't last forever. We embed natural expectations in a simple dynamic macroeconomic model and compare the simulated properties of the model to the available empirical evidence. The model's predictions match many patterns observed in macroeconomic and financial time series, such as high volatility of asset prices, predictable up-and-down cycles in equity returns, and a negative relationship between current consumption growth and future equity returns.

We formalize the Keynesian insight that aggregate demand driven by sentiments can generate output fluctuations under rational expectations. When production decisions must be made under imperfect information about demand, optimal decisions based on sentiments can generate stochastic self-fulfilling rational expectations equilibria in standard economies without persistent informational frictions, externalities, nonconvexities, or strategic complementarities in production. The models we consider are deliberately simple, but could serve as benchmarks for more complicated equilibrium models with additional features.

Home price expectations are believed to play an important role in housing dynamics, yet we have limited understanding of how they are formed and how they affect behaviour. Using a unique “information experiment” embedded in an online survey, this article investigates how consumers’ home price expectations respond to past home price growth, and how they impact investment decisions. After eliciting respondents’ priors about past and future local home price changes, we present a random subset of them with factual information about past (one- or five-year) changes, and then re-elicit expectations. This unique “panel” data allows us to identify causal effects of the information, and provides insights on the expectation formation process. We find that, on average, year-ahead home price expectations are revised in a way consistent with short-term momentum in home price growth, though respondents tend to underpredict the strength of momentum. Revisions of longer-term expectations show that respondents do not expect the empirically-occurring mean reversion in home price growth. These patterns are in line with recent behavioural models of housing cycles. Finally, we show that home price expectations causally affect investment decisions in a portfolio choice experiment embedded in the survey.

We review the main identification strategies and empirical evidence on the role of expectations in the New Keynesian Phillips curve, paying particular attention to the issue of weak identification. Our goal is to provide a clear understanding of the role of expectations that integrates across the different papers and specifications in the literature. We discuss the properties of the various limited-information econometric methods used in the literature and provide explanations of why they produce conflicting results. Using a common dataset and a flexible empirical approach, we find that researchers are faced with substantial specification uncertainty, as different combinations of various a priori reasonable specification choices give rise to a vast set of point estimates. Moreover, given a specification, estimation is subject to considerable sampling uncertainty due to weak identification. We highlight the assumptions that seem to matter most for identification and the configuration of point estimates. We conclude that the literature has reached a limit on how much can be learned about the New Keynesian Phillips curve from aggregate macroeconomic time series. New identification approaches and new datasets are needed to reach an empirical consensus.