Is the U.S. in Recession? CBS Experts Weigh in on the Economic Outlook
New data has sparked a debate about the state of the economy. Here’s what some of our faculty members had to say.
New data has sparked a debate about the state of the economy. Here’s what some of our faculty members had to say.
There is perhaps no topic that is more important for the functioning of a market economy than competition policy. The theorems and analyses stating that market economies deliver benefits in the form of higher living standards and lower prices are all based on the assumption that there is effective competition in the market. At the same time when Adam Smith emphasised that competitive markets deliver enormous benefits, he also emphasised the tendency of firms to suppress competition.
The veteran economist and CBS professor joined Professor Brett House to explore how erratic policymaking, rising tariffs, and politicized institutions are shaking global confidence in the U.S. economy.
During a recent Distinguished Speakers Series event, the Senior Partner and Chair of North America at McKinsey shared leadership insights on AI business strategy, climate innovation, and the future of work.
Insights from Columbia Business School faculty explain how the president’s “Liberation Day” tariffs are fueling market volatility, undermining global economic stability, and impacting the Fed's ability to lower interest rates.
A Columbia Business School study shows that experiencing a recession in young adulthood leads to lasting support for wealth redistribution—but mostly for one’s own group.
We study games of incomplete information as both the information structure and the extensive-form vary. An analyst may know the payoff-relevant data but not the players' private information, nor the extenstive-form that governs their play. Alternatively, a designer may be able to build a mechanism from these ingredients. We characterize all outcomes that can arise in an equilibrium of some extensive-form with some information structure.
We analyze methods for selecting topics in news articles to explain stock returns. We find, through empirical and theoretical results, that supervised Latent Dirichlet Allocation (sLDA) implemented through Gibbs sampling in a stochastic EM algorithm will often overfit returns to the detriment of the topic model. We obtain better out-of-sample performance through a random search of plain LDA models. A branching procedure that reinforces effective topic assignments often performs best. We test these methods on an archive of over 90,000 news articles about S&P 500 firms.