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We survey New Zealand firms and document novel facts about their macroeconomic beliefs. There is widespread dispersion in beliefs about past and future macroeconomic conditions, especially inflation. This dispersion in beliefs is consistent with firms’ incentives to collect and process information. Using experimental methods, we find that firms update their beliefs in a Bayesian manner when presented with new information about the economy and that changes in their beliefs affect their decisions. Inflation is not generally perceived as being important to business decisions so firms devote few resources to collecting and processing information about inflation.

We use a unique design feature of a survey of Italian firms to study the causal effect of inflation expectations on firms’ economic decisions. In the survey, a randomly chosen subset of firms is repeatedly treated with information about recent inflation whereas other firms are not. This information treatment generates exogenous variation in inflation expectations. We find that higher inflation expectations on the part of firms leads them to raise their prices, increase demand for credit, and reduce their employment and capital. However, when policy rates are constrained by the effective lower bound, demand effects are stronger, leading firms to raise their prices more and no longer reduce their employment.

We present a model of credit cycles arising from diagnostic expectations—a belief formation mechanism based on Kahneman and Tversky's representativeness heuristic. Diagnostic expectations overweight future outcomes that become more likely in light of incoming data. The expectations formation rule is forward looking and depends on the underlying stochastic process, and thus is immune to the Lucas critique. Diagnostic expectations reconcile extrapolation and neglect of risk in a unified framework. In our model, credit spreads are excessively volatile, overreact to news, and are subject to predictable reversals. These dynamics can account for several features of credit cycles and macroeconomic volatility.

We study the out-of-sample and post-publication return predictability of 97 variables shown to predict cross-sectional stock returns. Portfolio returns are 26% lower out-of-sample and 58% lower post-publication. The out-of-sample decline is an upper bound estimate of data mining effects. We estimate a 32% (58%-26%) lower return from publication-informed trading. Post-publication declines are greater for predictors with higher in-sample returns, and returns are higher for portfolios concentrated in stocks with high idiosyncratic risk and low liquidity. Predictor portfolios exhibit post-publication increases in correlations with other published-predictor portfolios. Our findings suggest that investors learn about mispricing from academic publications.

Using novel survey data, we document that individuals extrapolate from recent personal experiences when forming expectations about aggregate economic outcomes. Recent locally experienced house price movements affect expectations about future U.S. house price changes and higher experienced house price volatility causes respondents to report a wider distribution over expected U.S. house price movements. When we exploit within-individual variation in employment status, we find that individuals who personally experience unemployment become more pessimistic about future nationwide unemployment. The extent of extrapolation is unrelated to how informative personal experiences are, is inconsistent with risk adjustment, and is more pronounced for less sophisticated individuals.

We revisit La Porta's finding that returns on stocks with the most optimistic analyst long-term earnings growth forecasts are lower than those on stocks with the most pessimistic forecasts. We document the joint dynamics of fundamentals, expectations, and returns of these portfolios, and explain the facts using a model of belief formation based on the representativeness heuristic. Analysts forecast fundamentals from observed earnings growth, but overreact to news by exaggerating the probability of states that have become more likely. We find support for the model's predictions. A quantitative estimation of the model accounts for the key patterns in the data.

To explore the impact of coronavirus disease 2019 (COVID-19) on small businesses, we conducted a survey of more than 5,800 small businesses between March 28 and April 4, 2020. Several themes emerged. First, mass layoffs and closures had already occurred—just a few weeks into the crisis. Second, the risk of closure was negatively associated with the expected length of the crisis. Moreover, businesses had widely varying beliefs about the likely duration of COVID-related disruptions. Third, many small businesses are financially fragile: The median business with more than $10,000 in monthly expenses had only about 2 wk of cash on hand at the time of the survey. Fourth, the majority of businesses planned to seek funding through the Coronavirus Aid, Relief, and Economic Security (CARES) Act. However, many anticipated problems with accessing the program, such as bureaucratic hassles and difficulties establishing eligibility. Using experimental variation, we also assess take-up rates and business resilience effects for loans relative to grants-based programs.

Commonly used instruments for the identification of monetary policy disturbances are likely to combine the true policy shock with information about the state of the economy due to the information disclosed through the policy action. We show that this signaling effect of monetary policy can give rise to the empirical puzzles reported in the literature, and propose a new high-frequency instrument for monetary policy shocks that accounts for informational rigidities. We find that a monetary tightening is unequivocally contractionary, with deterioration of domestic demand, labor and credit market conditions as well as of asset prices and agents’ expectations.

Households’ and firms’ subjective inflation expectations play a central role in macroeconomic and intertemporal microeconomic models. We discuss how subjective inflation expectations are measured, the patterns they display, their determinants, and how they shape households’ and firms’ economic choices in the data and help us make sense of the observed heterogeneous reactions to business-cycle shocks and policy interventions. We conclude by highlighting the relevant open questions and why tackling them is important for academic research and policymaking.

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.