[Draft coming soon] Abstract. This study examines the impact of interest rate disagreement between the Federal Reserve, markets, and professional forecasters on the transmission of U.S. monetary policy. I demonstrate that market expectations, as reflected in tradable asset prices, generally outperform the Fed’s internal forecasts, with minor variations over time horizons. Empirical analysis employing lag-augmented local projections and proxy SVARs demonstrates that disagreement weakens monetary policy efficacy, attenuating its impact on output and offering an intuitive rationale for apparent anomalies as the “activity puzzle.” I show that standard New-Keynesian frameworks with incomplete information contain a latent disagreement structure, which, when studied explicitly, yields a parsimonious rationalization of the documented evidence. The model generates disagreement from three distinct sources—fundamentals, policy-rule, and credibility— each altering monetary transmission differently. A new identification through IRFs exercise, which selects mechanisms that replicate the empirical impulse responses, rules out fundamentals-based disagreement —at the core of the information channel— and supports policy-rule misperceptions and limited Fed credibility as empirically relevant channels.

Abstract. This study proposes a novel approach to interpreting conventional tests of the Full Information Rational Expectations (FIRE) hypothesis, based on the distinctions between forecasts and expectations. First, I argue that these two objects coincide under highly restrictive conditions that are unrealistic in the context of professional forecasting. Evidence from reduced-form analysis suggests that forecasters pursue strategic incentives when responding to surveys, contaminating their responses and making their use as measures of expectations misleading. Second, I leverage this distinction to introduce a new parsimonious model of forecast formation that is related to rational inattention and sparsity-based models of bounded rationality, but that is exempt from their complications, including the dependence on Gaussian information. The proposed framework employs a global game structure featuring public and private information, as well as the strategic behavior of forecasters, and demonstrates that strategy can explain the anomalies in Coibion and Gorodnichenko (2015)-type regressions. Finally, I exploit the model's transparency to develop and apply a formal test that validates the strategic channel, advising caution in using surveys to elicit expectations.

[Draft coming soon] – Abstract. This study proposes a two-step method to identify and quantify military spending news shocks, applicable to broader macroeconomic contexts. Combining narrative identification with Large Language Model searches, we compile the set of events (2001–2023) that altered the path of U.S. defense expenditure. Using cross-sectional regressions of defense contractors’ stock returns on their pre-event reliance on military revenues, we estimate market-implied measures of expected defense spending changes. This model-implied regression both validates each event and quantifies each shock transparently. Applying these shocks in a shift-share analysis yields a one-year MSA-level GDP multiplier of approximately 1.3 for U.S. military build-ups.

[Draft coming soon] – Abstract. We explore two major surveys of macroeconomic forecasts to highlight new patterns of heterogeneity in the cross-section of forecasting performance. We identify a range of new stylized features of forecast accuracy that, we show, are robust across horizons, variables, and surveys. We show that these properties cannot be accommodated by existing models of noisy or sticky information, and develop a parsimonious heterogeneous-agent hybrid model that nests both frameworks while offsetting the limitations inherent in each. When we take this novel, tractable model to the data, we demonstrate that it better fits the patterns of heterogeneity that emerged empirically. 

Abstract. This note reassesses the “orthogonalized monetary policy surprises” series proposed by Bauer and Swanson (2023a). Their approach aggregates daily FOMC surprises to the monthly level and only afterwards orthogonalizes them with respect to a subset of contemporaneous “news” variables. Instead, I purge each FOMC-day surprise from all same-day news releases before aggregation. The fully orthogonalized series loses instrumental strength and yields no significant responses in any of the macroeconomic variables under analysis, casting doubt either on the usefulness of the measure or, if taken seriously, on monetary non-neutrality. A simple model featuring explicit timing of the orthogonalization rationalizes the stark empirical differences.

[Draft coming soon] – Abstract. What portion of the movements in real estate prices cannot be rationalized by the most commonly imputed economic factors? How much should we care about the "animal spirits'', intended as fluctuations in agents' beliefs, when studying the origination of financial crises? The goal of this paper is to assess the role of confidence in understanding the boom-bust dynamics of credit and house prices in the United States, with a focus on the Great Financial Crisis. We identify confidence shocks in a structural VAR as exogenous innovations to the University of Michigan Sentiment Index. A positive shock to confidence increases significantly and prolongedly house prices, and accounts for almost 30% of their variance after 12 months, remaining significant after five years. We embed confidence shocks in a DSGE model with households’ heterogeneity and financial frictions. Confidence shocks are modeled as exogenous shifts in beliefs that are unrelated to current and future economic fundamentals, and that can generate waves of optimism or pessimism about current economic activity. Results from a Bayesian estimation of the model show that innovations to confidence account for half of the volatility of observed house prices. Our estimated confidence series shows a strong positive correlation with the Sentiment Index, suggesting that belief fluctuations in the model resemble empirical measures of confidence.

[Draft coming soon] – Abstract. Economic models with input-output networks assume that firm or sector growth is driven by a combination of trade partners’ growth and idiosyncratic shocks. This assumption generates unrealistic restrictions on network weights. Allowing for correlated shocks exposes units to additional risk that captures their ability to substitute away from supply and demand shocks. We provide evidence that substitutability between trade partners is related to technological and product dispersion that is not captured by standard firm and industry definitions. We propose a production-based asset pricing model in which supply chain substitutability depends on product/technology dispersion and shock correlation driven by shared suppliers and customers. The model predicts that assets positively exposed to upstream and downstream shocks are useful hedges and earn lower average risk premia than less exposed peers. This is confirmed by estimated return spreads of -11.4% and -4.2% and a negative association with aggregate growth.  

Abstract. In regressions à la Coibion-Gorodnichenko, the distinction between the consensus and individual level of analysis is first-order: while at the consensus level the empirical results are straightforward to interpret due to the theoretical guidance offered by models of information rigidities, as sticky or noisy information, at the individual level we lack such mapping from the data moments to their structural counterparts. This paper advances a more general, unified framework to interpret individual coefficients without resorting to ad-hoc behavioral or non-rational explanations.

[Draft coming soon]