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
Monday, October 27, 2025
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Economic History Seminar - Dora Costa (UCLA)S2.79 |
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Econometrics Seminar - Yonghong An (Texas A&M)S2.79Title: Identifying Strategic Misreporting and Correcting Biased Estimates. The paper is in progress and there is no draft yet, below is the abstract of the paper. This paper investigates the identification and estimation of non-classical measurement error structures in economic variables and develops practical empirical procedures to correct the resulting biases in subsequent estimations. Methodologically, we construct a structural model to rationalize the non-classical error structure and employ a characteristic function–based approach to identify it without requiring secondary measurements. Building on this, we show how to construct a 鈥減seudo measure鈥� that satisfies the classical measurement error assumption, thereby allowing researchers to apply existing methods originally designed for the classical case to eliminate bias. |