About

Alireza Tahbaz-Salehi is the William L. Ford Professor of Managerial Economics and Decision Sciences at the Kellogg School of Management, Northwestern University, where he joined the faculty in 2017. Prior to his current position, he was the Daniel W. Stanton Associate Professor of Business at Columbia Business School. His research focuses on the implications of network economies for information aggregation, business cycle fluctuations, and financial stability. Tahbaz-Salehi's academic background includes a PhD in Electrical and Systems Engineering from the University of Pennsylvania, along with a master's degree in Economics and a master's in Electrical Engineering from the same university, and a bachelor's degree in Electrical Engineering from Sharif University of Technology. His scholarly contributions have been recognized through awards such as the Excellence in Refereeing Award for the Review of Economic Studies and the American Economic Review, as well as the Pew Presidential Prize from the Department of Economics at the University of Pennsylvania. He has held editorial positions as an Associate Editor for the Journal of Economic Theory and the American Economic Review.

Research topics

• Computer Science
• Economics
• Macroeconomics
• Monetary economics
• Operations management
• Microeconomics
• Geography
• Statistics
• Economy
• Meteorology
• Mathematics
• Econometrics
• Market economy
• Business

Selected publications

• The Macroeconomics of Supply Chain Disruptions

  The Review of Economic Studies · 2024-04-08 · 55 citations

  articleSenior authorCorresponding

  Abstract This paper develops a model to study the macroeconomic implications of supply chain disruptions with three key ingredients: (i) a firm-level network of customized supplier–customer links that generate relationship-specific productivity gains; (ii) bargaining over these relationship-specific surpluses; and (iii) an extensive margin of adjustment, whereby firms decide to form or sever relations with suppliers and customers. We establish equilibrium existence and uniqueness, provide characterization results, and present a number of comparative statics that show how supply chains and aggregate output respond to shocks. We also show that equilibrium supply chains are inefficient and exhibit an inherent fragility: small shocks can lead to discontinuous changes in output, even though the efficient allocation is always continuous in the same shocks. We explore several macroeconomic implications of this fragility.

  PublisherDOI

• Model Complexity, Expectations, and Asset Prices

  The Review of Economic Studies · 2023 · 13 citations

  • 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

• Rigid production networks

  Journal of Monetary Economics · 2023-05-04 · 18 citations

  articleOpen accessSenior authorCorresponding

  • A model of production networks with quantity adjustment and informational frictions. • The interaction between the two frictions dampens the impact of shocks. • It increases the inflationary effect of positive aggregate demand shocks. This paper studies a production network model with quantity rigidities and informational frictions, where (i) firms may be restricted in how effectively they can adjust (some or all of) their intermediate input quantities in response to changes in the economic environment and (ii) they need to choose their quantities under incomplete information about the realizations of shocks. Our characterization results show that these two frictions lead to a reduction in aggregate output, as firms may find it optimal to rely more heavily on less volatile suppliers, even if it comes at the cost of forgoing more efficient ones. We also find that the interaction between informational frictions and quantity rigidities dampens the impact of productivity and aggregate demand shocks on aggregate output, while increasing the inflationary effects of positive shocks to nominal aggregate demand. The magnitudes of these effects depend on the distribution of the two frictions over the production network.

  PublisherDOI

• Optimal Monetary Policy in Production Networks

  Econometrica · 2022 · 94 citations

  Senior authorCorresponding
  • Computer Science
  • Economics
  • Monetary economics

  This paper studies the optimal conduct of monetary policy in a multisector economy in which firms buy and sell intermediate goods over a production network. We first provide a necessary and sufficient condition for the monetary policy's ability to implement flexible‐price equilibria in the presence of nominal rigidities and show that, generically, no monetary policy can implement the first‐best allocation. We then characterize the optimal policy in terms of the economy's production network and the extent and nature of nominal rigidities. Our characterization result yields general principles for the optimal conduct of monetary policy in the presence of input‐output linkages: it establishes that optimal policy stabilizes a price index with greater weights assigned to larger, stickier, and more upstream industries, as well as industries with less sticky upstream suppliers but stickier downstream customers. In a calibrated version of the model, we find that implementing the optimal policy can result in quantitatively meaningful welfare gains.

  PublisherOA PDFDOI

• Guest Editorial Special Issue on Dynamics and Behaviors in Social Networks

  IEEE Transactions on Control of Network Systems · 2022-08-24

  editorialOpen accessSenior author

  Mathematical models of social networks, i.e., communities of interacting individuals, have existed for more than 50 years and have been used extensively by sociologists, behavioral scientists, and economists. The traditional focus has been on obtaining models that capture sociological effects like interpersonal influence (tendency of individuals to be influenced by others), homophily (tendency to associate with other individuals of similar behavior, opinions, and characteristics), polarization (tendency of a community to split into opposite factions), crowd effects (tendency to follow the opinion of the majority), echo chambers (tendency of an isolated community to self-amplify their beliefs), and so on. With the advent of online social media, the breadth and scope of the research in social network theory has scaled drastically in both size and accuracy, as numbers of interacting individuals have soared, and recorded data streams have rendered the analysis of individual behaviors, preferences, and interpersonal relationships more quantitative than ever before. Alongside the graph-theoretical notions drawn from network science, such as centrality, connectivity, and resilience, tools from dynamical systems and control prove very useful when trying to capture and understand the emerging behavior of such complex systems. Indeed, the last few years have witnessed a steep increase in the number of works studying social networks from a control systems perspective. This Special Issue of IEEE Transactions on Control of Network Systems(TCNS) consolidates this trend and gathers original contributions that identify and solve some of the emerging challenges in the field. It contains 14 articles addressing a wide range of topics ranging from non-Bayesian social learning to evolutionary dynamics in population games, social influence and opinion formation processes, their coupling with epidemic processes and recommendation systems, as well as optimal targeting problems. We believe that this Special Issue presents an excellent—although by no means exhaustive—cross section of current research focused on the mathematical modeling, analysis, and control of social systems. We hope that it will be of great interest to the broad readership of TCNS.

  PublisherOA PDFDOI

• Skewness and Time-Varying Second Moments in a Nonlinear Production Network: Theory and Evidence

  National Bureau of Economic Research · 2021-11-01 · 17 citations

  preprint

  This paper studies asymmetry in economic activity over the business cycle. It develops a tractable multisector model of the economy in which complementarity across inputs causes aggregate activity to be left skewed with countercyclical volatility. We then examine implications of the model regarding the time-series skewness of activity at the sector level, cyclicality of dispersion and skewness across sectors, and the conditional covariances of sector growth rates, finding support for each in the data. The empirical skewness of employment growth, industrial production growth, and stock returns increases with the level of aggregation, which is consistent with the model's implication that it is the nonlinearity in the production structure of the economy that generates the skewness. Other prominent models of asymmetry are not able to simultaneously match the range of empirical facts that the production network model can.

  PublisherDOI

• Model Complexity, Expectations, and Asset Prices

  SSRN Electronic Journal · 2021-01-01

  articleOpen access
  PublisherDOI

• Skewness and Time-Varying Second Moments in a Nonlinear Production Network: Theory and Evidence

  SSRN Electronic Journal · 2021-01-01 · 5 citations

  articleOpen access
  PublisherDOI

• Model Complexity, Expectations, and Asset Prices

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

  articleOpen access
  PublisherDOI

• Model Complexity, Expectations, and Asset Prices

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

  reportOpen access

  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

Frequent coauthors

• Daron Acemoğlu

  Massachusetts Institute of Technology

  100 shared

• Philippe Mueller

  68 shared

• Asuman Ozdaglar

  60 shared

• Ali Jadbabaie

  51 shared

• Vasco M. Carvalho

  36 shared

• Andrea Vedolin

  23 shared

• Pooya Molavi

  Northwestern University

  16 shared

• Alvaro Sandroni

  14 shared

Awards & honors

• Excellence in Refereeing Award for The Review of Economic St…
• Excellence in Refereeing Award for the American Economic Rev…
• Pew Presidential Prize, Department of Economics, University…
• International Economic Review Fellowship, Department of Econ…
• Judith Rodin Fellowship, School of Arts and Sciences, Univer…