The evolution of the U.S. economy over the past 55 years is examined through the lens of a micro-founded model that allows for changes in the behaviour of the Federal Reserve and in the volatility of structural shocks. Agents are aware of the possibility of regime changes and their beliefs matter for the law of motion underlying the macroeconomy. Monetary policy is identified by repeated fluctuations between a Hawk and a Dove regime, with the latter prevailing in the 1970s and during the recent crisis. To explore the role of agents' beliefs I introduce a new class of counterfactual simulations: beliefs counterfactuals. If, in the 1970s, agents had anticipated the appointment of an extremely conservative Chairman, inflation would have been lower and the inflation-output trade-off more favourable. The large drop in inflation and output at the end of 2008 would have been mitigated if agents had expected the Federal Reserve to be exceptionally active in the near future.

We measure the nature and severity of a variety of belief distortions in market reactions to hundreds of economic news events by synthesizing structural estimation with algorithmic machine learning to quantify bias. We find that investors systematically overreact to perceptions about multiple fundamental shocks, a phenomenon we show often dampens rather than amplifies market volatility via a “shock composition effect.” Such effects imply that the stock market can underreact to news, even when investors overreact to all shocks.

A micro-founded model that allows for changes in the monetary/fiscal policy mix and in the volatility of structural shocks is fit to US post-WWII data. Agents are aware of the possibility of regime changes and their beliefs have an impact on the law of motion of the macroeconomy. The results show that the '60s and the '70s were characterized by a prolonged period of active fiscal policy and passive monetary policy. The appointment of Volcker marked a change in the conduct of monetary policy, but it took almost ten years for the fiscal authority to start accommodating this regime change.

We develop and estimate a general equilibrium model to assess the effects and welfare implications of central bank transparency. Monetary policy can deviate from active inflation stabilization, and agents conduct Bayesian learning about the nature of these deviations. Under constrained discretion, only short deviations occur, agents’ uncertainty about the macroeconomy remains contained, and welfare is high. However, if a deviation persists, uncertainty eventually accelerates and welfare declines. Announcing that inflation stabilization will be temporarily abandoned raises uncertainty. However, these announcements lower policy uncertainty and curb inflationary beliefs at the end of the policy. For the United States, enhancing transparency raises welfare.

We show that policy uncertainty about how the rising public debt will be stabilized accounts for the lack of deflation in the US economy at the zero lower bound. We first estimate a Markov-switching VAR to highlight that a zero-lower-bound regime captures most of the comovements during the Great Recession: a deep recession, no deflation, and large fiscal imbalances. We then show that a microfounded model that features policy uncertainty accounts for these stylized facts. Finally, we highlight that policy uncertainty arises at the zero lower bound because of a trade-off between mitigating the recession and preserving long-run macroeconomic stability.

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 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.

We develop a new class of general-equilibrium models with partially unfunded debt to propose a fiscal theory of persistent inflation. In response to business cycle shocks, the monetary authority controls inflation and the fiscal authority stabilizes debt. However, the central bank accommodates unfunded fiscal shocks, causing persistent movements in inflation, output, and real interest rates. In an estimated quantitative model, fiscal inflation accounts for the bulk of inflation dynamics. In the aftermath of the pandemic, unfunded fiscal shocks sustain the recovery but also cause a persistent increase in inflation. The model is able to predict the inflationary effects of the American Rescue Plan Act fiscal stimulus out of sample and with real-time data.

We use a high-frequency identification approach to document that individual politicians affect asset prices. We exploit the regular flow of viewpoints contained in Congress members’ tweets. Supportive (critical) tweets increase (decrease) the stock prices of the targeted firm and the corresponding industry in minutes around the tweet. The bulk of the stock price effects is concentrated in the tweets revealing news about future legislative action. The effects are amplified around committee meeting days, especially when the tweet originates from committee members and influential politicians. Overall, we show that Congress members’ social media accounts are an important source of political news.