Applied Microeconomics
Applied Microeconomics
The Applied Microeconomics research group unites researchers working on a broad array of topics within such areas as labour economics, economics of education, health economics, family economics, urban economics, environmental economics, and the economics of science and innovation. The group operates in close collaboration with the CAGE Research Centre.
The group participates in the CAGE seminar on Applied Economics, which runs weekly on Tuesdays at 2:15pm. Students and faculty members of the group present their ongoing work in two brown bag seminars, held weekly on Tuesdays and Wednesdays at 1pm. Students, in collaboration with faculty members, also organise a bi-weekly reading group in applied econometrics on Thursdays at 1pm. The group organises numerous events throughout the year, including the Research Away Day and several thematic workshops.
Our activities
Work in Progress seminars
Tuesdays and Wednesdays 1-2pm
Students and faculty members of the group present their work in progress in two brown bag seminars. See below for a detailed scheduled of speakers.
Applied Econometrics reading group
Thursdays (bi-weekly) 1-2pm
Organised by students in collaboration with faculty members. See the Events calendar below for further details
People
Academics
Academics associated with the Applied Microeconomics Group are:
Research Students
Events
Econometrics Seminar - Xiaoxia Shi (Wisconsin)
Title: Testing Inequalities Linear in Nuisance Parameters (with Gregory Cox and Yuya Shimizu) at the econometrics seminar.
Abstract- This paper proposes a new test for inequalities that are linear in possibly partially
identified nuisance parameters, called the generalized conditional chi-squared (GCC)
test. It extends the subvector conditional chi-squared (sCC) test in Cox and Shi (2023,
CS23) to a setting where the nuisance parameter is pre-multiplied by an unknown
and estimable matrix of coefficients. Properly accounting for the estimation noise in
this matrix while maintaining the simplicity of the sCC test is the main innovation
of this paper. [How? New variance formula? Rank condition?] As such, the paper
provides a simple solution to a broad set of problems including subvector inference for
models represented by linear programs, nonparametric instrumental variable models
with discrete regressor and instruments, and linear unconditional moment inequality
models. We also derive a simplified formula for computing the critical value that makes
the computation of the GCC test elementary.