About

Pooya Molavi is an Assistant Professor of Managerial Economics & Decision Sciences at Kellogg School of Management, with a secondary appointment as an Assistant Professor of Economics. He conducts research in macroeconomics, economic theory, and behavioral economics. He previously held the position of Saieh Family Fellow in Economics at the Becker Friedman Institute of the University of Chicago. Molavi received a PhD in Economics from MIT in 2019 and a PhD in Electrical and Systems Engineering from the University of Pennsylvania in 2013.

Research topics

• Computer Science
• Economics
• Macroeconomics
• Econometrics
• Sociology
• Political Science
• Mathematics
• Law
• Psychology
• Political economy
• Positive economics
• Public relations
• Social psychology
• Microeconomics
• Statistics

Selected publications

• Learning and the Emergence of Nonlinearity in Financial Markets

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Model Complexity, Expectations, and Asset Prices

  The Review of Economic Studies · 2023 · 13 citations

  1st authorCorresponding
  • Computer Science
  • Economics
  • Econometrics

  Abstract This paper analyses how limits to the complexity of statistical models used by market participants can shape asset prices. We consider an economy in which the stochastic process that governs the evolution of economic variables may not have a simple representation, and yet, agents are only capable of entertaining statistical models with a certain level of complexity. As a result, they may end up with a lower-dimensional approximation that does not fully capture the intertemporal complexity of the true data-generating process. We first characterize the implications of the resulting departure from rational expectations and relate the extent of return and forecast-error predictability at various horizons to the complexity of agents’ models and the statistical properties of the underlying process. We then apply our framework to study violations of uncovered interest rate parity in foreign exchange markets. We find that constraints on the complexity of agents’ models can generate return predictability patterns that are simultaneously consistent with the well-known forward discount and predictability reversal puzzles.

  PublisherDOI

• Simple Models and Biased Forecasts

  arXiv (Cornell University) · 2022 · 5 citations

  1st authorCorresponding
  • Computer Science
  • Economics
  • Econometrics

  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 capture the true process, yet they use the best model among those considered. Using simple models adds persistence to forward-looking decisions and increases the comovement among them. This mechanism narrows the gap between business-cycle theory and data. In a new neoclassical synthesis model, the assumption that agents use simple models fits the data much better than the rational-expectations hypothesis. Moreover, simple models simultaneously resolve the Barro-King and forward guidance puzzles while improving the propagation of TFP shocks.

  PublisherOA PDFDOI

• Informational Autocrats, Diverse Societies

  arXiv (Cornell University) · 2022 · 5 citations

  Senior authorCorresponding
  • Political Science
  • Sociology
  • Positive economics

  This paper presents a theoretical model of an autocrat who controls the media in an attempt to persuade society of his competence. We base our analysis on a Bayesian persuasion framework in which citizens have heterogeneous preferences and beliefs about the autocrat. We characterize the autocrat's information manipulation strategy when society is monolithic and when it is divided. When the preferences and beliefs in society are more diverse, the autocrat engages in less information manipulation. Our findings thus suggest that the diversity of attitudes and opinions can act as a bulwark against information manipulation by hostile actors.

  PublisherOA PDFDOI

• Model Complexity, Expectations, and Asset Prices

  SSRN Electronic Journal · 2021-01-01 · 4 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Model Complexity, Expectations, and Asset Prices

  National Bureau of Economic Research · 2021-01-01 · 13 citations

  reportOpen access1st authorCorresponding

  This paper analyzes how limits to the complexity of statistical models used by market participants can shape asset prices. We consider an economy in which agents can only entertain models with at most k factors, where k may be distinct from the true number of factors that drive the economy's fundamentals. We first characterize the implications of the resulting departure from rational expectations for return dynamics and relate the extent of return predictability at various horizons to the number of factors in the agents' models and the statistical properties of the underlying data-generating process. We then apply our framework to two applications in asset pricing: (i) violations of uncovered interest rate parity at different horizons and (ii) momentum and reversal in equity returns. We find that constraints on the complexity of agents' models can generate return predictability patterns that are consistent with the data.

  PublisherDOI

• Tests of Bayesian Rationality

  arXiv (Cornell University) · 2021-09-01

  preprintOpen access1st authorCorresponding

  What are the testable restrictions imposed on the dynamics of an agent's belief by the hypothesis of Bayesian rationality, which do not rely on the additional assumption that the agent has an objectively correct prior? In this paper, I argue that there are essentially no such restrictions. I consider an agent who chooses a sequence of actions and an econometrician who observes the agent's actions but not her signals and is interested in testing the hypothesis that the agent is Bayesian. I argue that -- absent a priori knowledge on the part of the econometrician on the set of models considered by the agent -- there are almost no observations that would lead the econometrician to conclude that the agent is not Bayesian. This result holds even if the set of actions is sufficiently rich that the agent's action fully reveals her belief about the payoff-relevant state and even if the econometrician observes a large number of identical agents facing the same sequence of decision problems.

  Publisher

• The Empirical Content of Bayesianism

  arXiv (Cornell University) · 2021-09-14

  preprintOpen access1st authorCorresponding

  This paper characterizes the conditions under which the observed beliefs of a group of agents are consistent with Bayesian updating. Beliefs are consistent with Bayesianism if they arise from the application of Bayes' rule given some subjective distribution for the state and the signals agents observe between periods. The paper's main finding is that beliefs are consistent with Bayesianism if and only if the mean of the distribution of posteriors is uniformly absolutely continuous with respect to the prior. Furthermore, the paper shows that the existing results on the empirical content of Bayesianism rely on additional restrictions on permissible subjective distributions, such as the requirement that agents have correct beliefs about the distribution of signals.

  PublisherOA PDFDOI

• Model Complexity, Expectations, and Asset Prices

  SSRN Electronic Journal · 2021-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Macroeconomics with Learning and Misspecification: A General Theory and Applications

  2019 Meeting Papers · 2019-01-01 · 39 citations

  article1st authorCorresponding

  This paper explores a form of bounded rationality where agents learn about the economy with possibly misspecified models. I consider a recursive general-equilibrium framework that nests a large class of macroeconomic models. Misspecification is represented as a constraint on the set of beliefs agents can entertain. I introduce the solution concept of constrained-rational expectations equilibrium (CREE), in which each agent selects the belief from her constrained set that is closest to the endogenous distribution of observables in the Kullback–Leibler divergence. If the set of permissible beliefs contains the rational-expectations equilibria (REE), then the REE are CREE; otherwise, they are not. I show that a CREE exists, that it arises naturally as the limit of adaptive and Bayesian learning, and that it incorporates a version of the Lucas critique. I then apply CREE to a particular novel form of bounded rationality where beliefs are constrained to factor models with a small number of endogenously chosen factors. Misspecification leads to amplification or dampening of shocks and history dependence. The calibrated economy exhibits hump-shaped impulse responses and co-movements in consumption, output, hours, and investment that resemble business-cycle fluctuations.

  Publisher

Frequent coauthors

• Ali Jadbabaie

  33 shared

• Alireza Tahbaz-Salehi

  Northwestern University

  16 shared

• Ceyhun Eksin

  14 shared

• Alejandro Ribeiro

  California University of Pennsylvania

  14 shared

• Alvaro Sandroni

  5 shared

• Sergio Barbarossa

  Sapienza University of Rome

  4 shared

• Andrea Vedolin

  4 shared

• Anna Scaglione

  Cornell University

  4 shared

Awards & honors

• Saieh Family Fellow in Economics at the Becker Friedman Inst…