Course content
This is a hands-on, data-oriented course that uses econometric and statistical tools to analyze the determinants and impacts of innovation and new technologies on firms and society. Innovations and new technologies are fundamental to today's fast-changing global economy. As a result, managers and policymakers must decide which technologies to support, how to organize technological discovery, and how to evaluate the impact of new technologies. Over the past decades, large datasets, big data sources, and analytical tools have become available, playing a key role in evaluating the use and impact of new technologies. Modern analysis methods include simple linear regression models for exploring correlations, more sophisticated causal analysis techniques, and advanced methods such as machine learning. This course builds on ideas from economics, finance, and management. We combine theory, learned empirical methods and real-world data using statistical software to analyze the impact of innovation on firms and society.
A central component of the course is the analysis of innovation-related datasets (e.g., firm-level, patent, or labor-market data) using statistical software. The focus is on the causal interpretation of regression results and the selection and assessment of empirical strategies. We will adopt an integrated approach, discussing main topics in innovation through theory, empirical studies, and our own data analysis. Core topics include innovation incentives and competition, innovation policy, intellectual property rights and strategy, and innovation financing and valuation. We will also examine how innovation, automation, and artificial intelligence impact economic growth, financial markets, labor markets, firms, and society. Additionally, this course will teach you how to interpret and communicate results that inform strategic decisions and policy design.
See course description in course catalogue