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 investigate the ability of a tax-based fundamental –the ratio of tax-to-book income– to predict earnings growth and stock returns and to explain the earnings-price ratio. This tax fundamental reflects both temporary and permanent book-tax differences as well as tax accruals, such as changes in the tax valuation allowance. We find that the tax-to-book income ratio predicts subsequent five-year earnings changes, both before and after the implementation of Statement of Financial Accounting Standards (SFAS) No. 109 in 1993. For the pre-SFAS No.
This article pursues a statistical study of the Hough transform, the celebrated computer vision algorithm used to detect the presence of lines in a noisy image. We first study asymptotic properties of the Hough transform estimator, whose objective is to find the line that "best" fits a set of planar points. In particular, we establish strong consistency and rates of convergence, and characterize the limiting distribution of the Hough transform estimator.