Time series regression analysis in econometrics typically involves a framework relying on a set of mixing conditions to establish consistency and asymptotic normality of parameter estimates and HAC-type estimators of the residual long-run variances to conduct proper inference. This article introduces structured machine learning regressions for high-dimensional time series data using the aforementioned commonly used setting.
This paper provides the first study of compensation and pay-for-performance for top executives at non-profit endowments. Using a detailed breakdown of compensation from IRS filings over the 2009-2017 period, we find that pay packages of Chief Investment Officers (CIOs) depend more heavily on bonuses than do those for other non-profit executives.
E-commerce platforms, such as Amazon, Alibaba and Flipkart, that match sellers and consumers at an unprecedented scale, operate their internal search engines to help buyers find relevant products from a large number of sellers, and also allow sellers to advertise to consumers for positions in the search listing. Determining an optimal ranking of products in response to a search query is a challenging problem for the platform because sellers have certain private information about their products that the platform does not have. Using a theoretical model, we show that sellers’ bids in ad auctions, through which sponsored slots are typically allocated, can reveal (some of) this private information to the platform (“information effect”), which it can optimally combine with information that it has about consumers to improve the placement of organic results, a practice we call “strategic listing”.
We study multi-period sales-force incentive contracting where salespeople can engage in effort gaming, a phenomenon that has extensive empirical support. Focusing on a repeated moral hazard scenario with two independent periods and a risk-neutral agent with limited liability, we conduct a theoretical investigation to understand which effort profiles the firm can expect under the optimal contract. We show that various effort profiles that may give the appearance of being sub-optimal, such as postponing effort exertion (“hockey stick”) and not exerting effort after a bad or a good initial demand outcome (“giving up” and “resting on laurels,” respectively) may indeed be induced optimally by the firm.
What makes an asset institutional-quality? This paper proposes that one reason is the existing concentration of delegated investors in a market through a liquidity channel.
Participants in the 2019 Kenan Institute Frontiers of Entrepreneurship conference address the obstacles facing women and underrepresented minority entrepreneurs – from funding to mentorship, resourcing and more – and why overcoming those barriers matters for the broader U.S. and global economies.
Susan St. Ledger, the President of Worldwide Field Operations at Splunk Inc. joined the Kenan Institute for a Dean's Speaker Series talk at the Kenan Center on Tuesday, Jan. 15, 2019. Splunk Inc. is a multinational corporation based in San Francisco, California, that produces software for searching, monitoring and analyzing machine-generated big data, via a web-style interface. Prior to Splunk, Ms. St. Ledger held high-level positions with Salesforce and Sun Microsystems.
On Sept. 9-11, 2019, the Kenan Institute and the University of North Carolina at Chapel Hill’s Institute for African-American Research will co-host the second Black Communities Conference, an international gathering of scholars and community leaders from across the African diaspora. The conference's core mission is to connect academics from a variety of disciplines with black communities, with the goal of enhancing the life of those communities. Hear more from Kenan Institute Managing Director and conference co-chair Mark Little.
North Carolina’s Research Triangle is booming, driving strong job creation and population growth. Yet the region’s affordable housing stock is shrinking. In November 2019, the Leonard W. Wood Center for Real Estate Studies and our Kenan Scholars Program co-hosted "Investing in Affordable Housing Symposium: Private Market Solutions to the Triangle’s Affordable Housing Shortage." The symposium focused on the private market production and preservation of affordable housing to ensure workers can live and invest in the communities where they work. The event brought together the region’s top developers, financiers, government officials and nonprofit professionals to be part of the conversation on developing new and sustainable solutions to address the Triangle’s affordable housing shortage.
Experts from the 2019 Closing the Wealth Gap Conference at UNC Kenan-Flagler Business School discuss the repercussions of the wealth gap not being addressed.
On Sept. 5, our affiliated Center for Sustainable Enterprise awarded Firsthand Foods the 2019 UNC Sustainability Award. Founders Jennifer Curtis and Tina Prevatte Levy met at UNC Kenan-Flagler Business School and developed the concept of connecting farmers to market. The UNC Sustainability Award recognizes leadership and best practice of a North Carolina business demonstrating initiative, innovation and impact in sustainability.
In March 2019, the UNC Energy Center hosted the New Technologies & Economics for Carbon Capture/Sequestration Conference at UNC Kenan-Flagler Business School. The conference brought together a highly select group of energy executives and professionals which focused their discussion on the potential for early commercialization of these technologies.
Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work.