Macroeconomic Modeling and Systemic Risk Research Initiative

Project Directors 

  • Lars Peter Hansen, University of Chicago

  • Andrew W. Lo, Massachusetts Institute of Technology

The financial crisis of 2007–2009 revealed serious gaps in our ability to define, measure, and manage financial sector activities that pose risks to the macroeconomy as a whole. Current macroeconomic models typically used for quantitative and empirical investigations are not well designed to account for important financial sector influences on the aggregate economy.

To address these deficiencies, the Becker Friedman Institute has launched an initiative to develop and assess more ambitious macroeconomic models.

Building new models is a long-term venture that requires a broad-based, collective perspective. This three-year initiative establishes the Macro Financial Modeling (MFM) Group, a network of prominent researchers working together to develop the next generation of policy tools. 

These enhanced models will be rich enough to study the impact of shocks that are either initially large or build endogenously over time. 

Project Details

The working group meets regularly to discuss and critique current and proposed models. Its members report findings and disseminate relevant research online. The group provides research assistance and data to support efforts to develop new models, including software for solving and evaluating these models. The group also provides support for young scholars under the guidance of a working group.

The project is expected to generate:

  • papers and an online compendium of research related to better measurement of systemic risk

  • new and improved software for macroeconomic modeling
  • new knowledge in the form of dissertations and journal articles that explore linkages between economic sectors

The group will focus on assessing the current landscape of risk measurement, exploring questions like:

  • What are the virtues and potential pitfalls of existing measures? What new measures will be revealing?
  • What data is available to effectively measure risk? What data is lacking?
  • How do we address data confidentiality issues?