We develop a novel bounded rationality model of imperfect reasoning as the interaction between automatic (System 1) and analytical (System 2) thinking. In doing so, we formalize the empirical consensus of cognitive psychology using a structural, constrained-optimal economic framework of mental information acquisition about the unknown optimal policy function. A key result is that agents reason less (more) when facing usual (unusual) states of the world, producing state- and history-dependent behavior. Our application is an otherwise standard incomplete-markets model with no a priori behavioral biases. The ergodic distribution of actions and beliefs is characterized by endogenous learning traps, where locally stable state dynamics generate familiar regions of the state space within which behavior appears to follow memory-based heuristics. This results in endogenous behavioral biases that have many empirically desirable properties: the marginal propensity to consume is high even for unconstrained agents, hand-to-mouth status is more frequent and persistent, and there is more wealth inequality than in the standard model.

We show that the formalization of representativeness (Kahneman and Tversky (1972)) developed by Gennaioli and Shleifer (2010) features an intrinsic connection between uncertainty and overreaction. In the time series domain, we develop this connection in a smooth version of Diagnostic Expectations (DE), where overreaction varies smoothly with uncertainty. Intuitively, under smoothness, lower uncertainty leaves less room for representativeness to distort beliefs. Smooth DE provides a joint, parsimonious micro-foundation for key features of survey data: overreaction to news, stronger overreaction at longer horizons, overconfidence in subjective uncertainty, and a new stylized fact documented here—overreaction intensifies with higher uncertainty. An analytical Real Business Cycle model with Smooth DE accounts for survey overreaction and overconfidence, as well as three salient business cycle properties: asymmetry, countercyclical macro volatility, and countercyclical micro volatility.

A large psychology literature argues that, due to selective memory recall, decision-makers’ forecasts of the future are overly influenced by the perceived news. We adopt the diagnostic expectations (DE) paradigm [Bordalo et al. (2018), Journal of Finance, 73, 199–227] to capture this feature of belief formation, develop a method to incorporate DE in business cycle models, and study the implications for aggregate dynamics. First, we address (1) the theoretical challenges associated with modelling the feedback between optimal actions and agents’ DE beliefs and (2) the time-inconsistencies that arise under distant memory (i.e. when news is perceived with respect to a more distant past than just the immediate one). Second, we show that under distant memory the interaction between actions and DE beliefs naturally generates repeated boom–bust cycles in response to a single initial shock. We also propose a portable solution method to study DE in dynamic stochastic general equilibrium models and use it to estimate a quantitative DE New Keynesian model. Both endogenous states and distant memory play a critical role in successfully replicating the boom–bust cycle observed in response to a monetary policy shock.

This paper studies a New Keynesian business cycle model with agents who are averse to ambiguity (Knightian uncertainty). Shocks to confidence about future TFP are modeled as changes in ambiguity. To assess the size of those shocks, our estimation uses not only data on standard macro variables, but also incorporates the dispersion of survey forecasts about growth as a measure of confidence. Our main result is that TFP and confidence shocks together can explain roughly two thirds of business cycle frequency movements in the major macro aggregates. Confidence shocks account for about 70 percent of this variation.

We propose a new theory of price rigidity based on firms' Knightian uncertainty about their competitive environment. This uncertainty has two key implications. First, firms learn about the shape of their demand function from past observations of quantities sold. This learning gives rise to kinks in the expected profit function at previously observed prices, making those prices both sticky and more likely to reoccur. Second, uncertainty about the relationship between aggregate and industry‐level inflation generates nominal rigidity. We prove the main insights analytically and quantify the effects of our mechanism. Our estimated quantitative model is consistent with a wide range of micro‐level pricing facts that are typically challenging to match jointly. It also implies significantly more persistent monetary non‐neutrality than in standard models, allowing it to generate large real effects from nominal shocks.

Concave hiring rules imply that firms respond more to bad shocks than to good shocks. They provide a unified explanation for several seemingly unrelated facts about employment growth in macro- and microdata. In particular, they generate countercyclical movement in both aggregate conditional “macro” volatility and cross-sectional “micro” volatility, as well as negative skewness in the cross section and in the time series at different levels of aggregation. Concave establishment-level responses of employment growth to total factor productivity shocks estimated from census data induce significant skewness, movements in volatility, and amplification of bad aggregate shocks.

This article estimates a business cycle model with endogenous financial asset supply and ambiguity averse investors. Firms’ shareholders choose not only production and investment, but also capital structure and payout policy subject to financial frictions. An increase in uncertainty about profits lowers stock prices and leads firms to substitute away from debt as well as reduce shareholder payout. This mechanism parsimoniously accounts for the postwar comovement in investment, stock prices, leverage, and payout, at both business cycle and medium term cycle frequencies. Ambiguity aversion permits a Markov-switching VAR representation of the model, while preserving the effect of uncertainty shocks on the time variation in the equity premium.