Research is a signature component of the Kenan Scholars program. It drives innovation and is critical to conducting business in today’s data-driven economy. Employers are looking for leaders who can...
Join the Kenan Institute of Private Enterprise and SAS this November for the AI Innovations Forum, where thought leaders from academia, industry and government will convene to discuss and debate the most challenging issues in artificial intelligence today, setting the agenda for future research and policy.
From small towns to big cities and everywhere in between, there is still a long road ahead to address the current economic crisis spurred by the coronavirus pandemic and adapt to the new normal, but NCGrowth and SmartUp have been hosting webinars to provide communities with key resources. On Wednesday, May 20, three panelists offered their perspectives to explore the economic impacts of COVID-19.
The UNC Entrepreneurship Center will host its fourth and final fireside chat for the fall 2020 semester with Bill Spruill on Wednesday, Nov. 11. Fireside chats are a continuing series of talks hosted by Launch Chapel Hill and The Entrepreneurship Center. These conversations seek to showcase a broad range of entrepreneurs who are making an impact in their field, as well as introduce and connect these people to the Launch Chapel Hill and Triangle community.
A panel recap from last month's Future of Digital Assets Symposium analyzed how fintech may be able to help create a more inclusive financial system.
Fellow, The Brookings Institution, and 2025 Kenan Institute Distinguished Fellow
We analyze the impact of the introduction of credit default swaps (CDSs) on real decision-making within the firm. Our structural model predicts that CDS introduction increases debt capacity more when uncertainty about the credit events that trigger CDS payment is lower.
In this special issue, we review 14 articles published in Organization Science over the past 25 years examining large-scale collaborations (LSCs) tasked with knowledge dissemination and innovation. LSCs involve sizeable pools of participants carrying out a common mission such as developing open-source software, detector technologies, complex architecture, encyclopedias, medical cures, or responses to climate change.
Traditional instruments of market analysis are no longer enough for the big markets of the 21st century. Data Science creates new opportunities to understand competitors as well.
Tax audits are a necessary component of the tax system, but policymakers and others have expressed concerns about their potentially adverse real effects. Understanding the causal effects of tax audits has been hampered by lack of data and because typically tax audits are not randomly assigned. We use administrative data from random tax audits of small businesses to examine the real effects of being subject to a tax audit.
When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002).
We discuss firm-level evidence based on UK data showing that within-firm pay inequality--wage differentials between top- and bottom-level jobs--increases with firm size. Moreover, within-firm pay inequality rises as firms grow larger over time. Lastly, using wage data from 15 developed countries, we document a positive association between aggregate wage inequality at the country level and growth by the largest firms in the country. We conclude that part of what may be perceived as a global trend toward more wage inequality may be driven by an increase in the size of the largest firms in the economy.
This paper surveys the recent advances in machine learning method for economic forecasting. The survey covers the following topics: nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validation, classification with economic losses.
In this paper, we develop new methods for analyzing high-dimensional tensor datasets. A tensor factor model describes a high-dimensional dataset as a sum of a low-rank component and an idiosyncratic noise, generalizing traditional factor models for panel data. We propose an estimation algorithm, called tensor principal component analysis (PCA), which generalizes the traditional PCA applicable to panel data.
Real estate private equity (REPE) funds are often differentiated by risk class: core, value-added, or opportunistic. Fund class is used by investors and managers to allocate funds and to describe investment policies. In this paper, we use REPE fund cash flow data from Burgiss that allow us to calculate a variety of performance metrics. For a subset of the data, we also observe characteristics of underlying fund holdings. Despite evidence that Value-Added and Opportunistic funds differ in investment composition, we show that class does not do a good job of predicting differences in performance. Unsurprisingly, greater investment in development (as assessed ex post), predicts poor performance for funds raised just before the Great Recession.
Commercial real estate is a major asset class, with an estimated value of more than $12 trillion in the U.S. alone. But the stay-at-home orders and business closures precipitated by the COVID-19 pandemic have the potential to negatively – and disastrously – affect commercial properties. What will the short- and long-term impacts be, which types of properties will be hardest hit and what policies can be put in place to help stem the tide of losses? UNC Kenan-Flagler Business School Professor and Leonard W. Wood Center for Real Estate Studies Faculty Advisor Andra Ghent and her colleagues examine these issues in this week’s Kenan Insight.
The data-generating process of productivity growth includes both trend and business-cycle shocks, generating many counterfactuals for prices under full-information. In practice, agents cannot immediately distinguish between the two shocks, leading to "rational confusion": each shock inherits properties of its counterpart.
We introduce easy to implement regression-based methods for predicting quarterly real economic activity that use daily financial data. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of the current (nowcasting) and future quarters.