Applications of random set theory in econometrics

In recent years, the econometrics literature has shown a growing interest in the study of partially identified models, in which the object of economic and statistical interest is a set rather than a point. The characterization of this set and the development of consistent estimators and inference procedures for it with desirable properties are the main goals of partial identification analysis. This review introduces the fundamental tools of the theory of random sets, which brings together elements of topology, convex geometry, and probability theory to develop a coherent mathematical framework to analyze random elements whose realizations are sets. It then elucidates how these tools have been fruitfully applied in econometrics to reach the goals of partial identification analysis.

Non-tariff barriers, integration and the transatlantic economy

We examine the potential impact of TTIP through trade-cost reductions, applying a mix of econometric and computational methods to develop estimates of the benefits (and costs) for the EU, United States, and third countries. Econometric results point to an approximate 80% growth in bilateral trade with an ambitious trade agreement. However, at the same time, computable general equilibrium (CGE) estimates highlight distributional impacts across countries and factors not evident from econometrics alone. Translated through our CGE framework, while bilateral trade increases roughly 80%, there is a fall of about 2.5% in trade with the rest of the world in our central case. The estimated gains in annual consumption range between 1% and 2.25% for the United States and EU, respectively. A purely discriminatory agreement would harm most countries outside the agreement, while the direction of third-country effects hinges critically on whether NTB reductions end up being discriminatory or not. Within the United States and EU, while labour gains across skill categories, the impact on farmers is mixed.