Conference Agenda

The field of quantitative research has transitioned from classical regression models to research designs emphasizing causal interpretation. The credibility revolution has enhanced reliability in empirical economics by prioritizing research design quality and employing more experimental and quasi-experimental methods. The module introduces a potential outcome model and direct acyclic graphs to analyse the causal effects of policies in natural experiments and randomized control trials. It further discusses in detail recent developments in difference-in-difference and synthetic control methods. Staggered policy implementation and heterogenous impact is discussed. Finally, synthetic difference in difference, combining strengths of both methods is presented. The presentation of these methods is accompanied by practical demonstrations in R. Staggered difference-in-differences is illustrated through examples showcasing the heterogeneous impacts of preferential trade agreements and sanctions on trade flows. Synthetic control is demonstrated by evaluating the impact of Brexit on UK trade with the EU.

Professor Reuer will talk about the state of research and methodological issues in Alliance Governance and Design research. While he discusses the broader literature and recent developments on collaborative strategy, he will identify new theoretical perspectives and topics worthy of future research in International Business.

Endogeneity-related issues in empirical research continue to receive increasing academic attention, serving as pivotal benchmarks for quality in many academic journals. In this workshop, we'll delve into the complexities of endogeneity bias, exploring its sources such as the omission of variables, errors in variables, and simultaneous causality. Led by Dr. Ghasem Zaefarian, Associate Professor of Marketing at Leeds University Business School, this workshop will provide attendees with an overview of endogeneity bias and its potential sources. From outlining the nuances of the issue to discussing techniques like instrumental variables and instrument-free approaches, participants will gain insights into mitigating endogeneity bias in their research. Whether you're designing a new research project or analyzing data to uncover cause-and-effect relationships, this methodological workshop is designed to give you a head start with the tools needed to navigate endogeneity bias effectively.

An ever-increasing share of information is recorded as digital text. Until recently, however, text analysis relied on meticulous human examination, a method inherently limited in scalability when confronted with the vast corpora now at our disposal. The quantity of individual documents within widely-used databases, such as those containing newspaper articles and tweets from Twitter, has surged into the tens of millions or billions more. This burgeoning abundance of large-scale corpora has sparked a heightened enthusiasm for machine learning techniques for text analysis, a trajectory that is poised to persist and intensify with the continual expansion of textual data reservoirs. This workshop will introduce how to perform text analysis using machine learning tools. The main topics covered include: What Is Text Analysis? Text data preprocessing. An overview of text analysis techniques used in economics. Word (text) similarity analysis with an example of measuring aggregate-level Brexit uncertainty. Topic analysis with an example of measuring topic-level Brexit uncertainty. Sentiment analysis with an example of measuring public sentiment towards Brexit using Twitter data.