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.
Bayesian learning implies decreasing weights on prior beliefs and increasing weights on the accuracy of the analyst?s past forecast record, as the number of forecast errors comprising her forecast record (its length) increases. Consistent with this model of investor learning, empirical tests show that investors? reactions to forecast news are increasing in the product of the accuracy and length of analysts? forecast records. Moreover, the Bayesian learning predicted by our model is more descriptive of investor reactions than is a static model which predicts that investors?
A general approach to improving simulation accuracy uses information about auxiliary control variables with known expected values to improve the estimation of unknown quantities. We analyze weighted Monte Carlo estimators that implement this idea by applying weights to independent replications. The weights are chosen to constrain the weighted averages of the control variables. We distinguish two cases (unbiased and biased), depending on whether the weighted averages of the controls are constrained to equal their expected values or some other values.