Pooya Molavi

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

Selected publications

• Foundations of Non-Bayesian Social Learning

  SSRN Electronic Journal · 2015-01-01 · 45 citations

  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 endoge…

  Publisher

• Model Complexity, Expectations, and Asset Prices

  The Review of Economic Studies · 2023 · 13 citations

  1st authorCorresponding

  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 complexi…

  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…

  PublisherDOI

• Simple Models and Biased Forecasts

  arXiv (Cornell University) · 2022 · 5 citations

  1st authorCorresponding

  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 pe…

  PublisherOA PDFDOI

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…

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