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Belief Formation in Macro and Asset Pricing

Paper Session

Sunday, Jan. 7, 2024 1:00 PM - 3:00 PM (CST)

Grand Hyatt, Travis C
Hosted By: Econometric Society
  • Chair: Rosen Valchev, Boston College

Simple Models and Biased Forecasts

Pooya Molavi
,
Northwestern University

Abstract

This paper proposes a framework in which agents are constrained to use simple models to forecast economic variables and characterizes the resulting biases. It considers agents who can only entertain state-space models with no more than d states, where d measures the intertemporal complexity of a model. Agents are boundedly rational in that they can only consider models that are too simple to nest the true process, yet they use the best model among those considered. I show that using simple models adds persistence to forward-looking decisions and increases the comovement among them. I then explain how this insight can bring the predictions of three workhorse macroeconomic models closer to data. In the new-Keynesian model, forward guidance becomes less powerful. In the real business cycle model, consumption responds more sluggishly to productivity shocks. The Diamond-Mortensen-Pissarides model exhibits more internal propagation and more realistic comovement in response to productivity and separation shocks.

Biased Surveys

Luca Gemmi
,
University of Lausanne
Rosen Valchev
,
Boston College

Abstract

We provide evidence suggesting that surveys of professional forecasters are biased by strategic incentives. First, we find that individual professional forecasts over-react to private information but under-react to public information. Second, we show that this bias is not present in forecasts data that is not subject to strategic incentives, such as central bank forecasts. We show that our evidence are consistent with a theory of strategic diversification incentives in forecast reporting, in which forecasters rationally report a biased measure of their true expectations. This has two effects. First, reported forecasts display ``over-reaction'', which the previous literature has instead ascribed to behavioral biases. Second, reported forecasts display less information rigidity than the actual forecasters' honest beliefs. Overall, our results caution against the use of survey of professional forecasters as a direct measure of expectations, and suggest that the true underlying beliefs suffer from a much larger degree of imperfect information than previously estimated. This has particularly profound implications for monetary policy, where the stickiness of inflation expectations play a key role.

Granular Sentiments

Rustam Jamilov
,
University of Oxford
Alexandre Kohlhas
,
University of Oxford
Sasha Talavera
,
University of Birmingham
Mao Zhang
,
University of St. Andrews

Abstract

We propose an empirically-consistent theory of business cycles, driven by fluctuations in sentiment towards a small number of firms. We measure firm-level sentiment with standard methods from computational linguistics. We find that 50 firms account for over 70% of the unconditional variation in U.S. sentiment and output over the period 2006-2021. The ``granular sentiment residual’’, measuring sentiment towards the 50 firms, is dominated by firms that are closer to the final consumer, i.e. are more downstream. To rationalize our findings, we embed endogenous information choice into a general equilibrium model with heterogeneous upstream and downstream firms. We show that attention centers on downstream firms, as they act as natural “information agglomerators”. When calibrated to match select moments of U.S. data, orthogonal shocks to the sentiment of downstream firms explain around 25% of business fluctuations.
JEL Classifications
  • C1 - Econometric and Statistical Methods and Methodology: General