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 - Chao Yang (SHUFE))
Title: A Spatial Autoregressive Model for Interval-Valued Data
Authors: Yuying Sun, Chao Yang (presenter) and Junxin Zhao
Abstract: The spatial autoregressive (SAR) model has been widely applied to quantify the spillover effects across agents who are geographically adjacent to each other and/or linked in different types of networks, such as social networks, trade networks, and input-output chains, and so on. Extensions on model framework and estimation methods have been made to analyze different types of data, such as censored variables and discrete choices. However, as far as we know, previous literature focuses on point-valued data and little attention has been paid to interval-valued data, which may contain the overall changes of a unit during a fixed time span such as the number of visitors to a place of interest during a day, the load of customers of a railway line during a week, and the house price of a district during a month. As an interval contains much more information than the mean, range, or variance individually, it would be interesting to model their spatial or network relationships, which may potentially identify the spillover of a variable's whole distribution, providing rich implications about heterogenous spillovers across different quantiles. In this model, we extend the classical SAR framework to analyze interval-valued data, develop a minimal distance GMM estimator, and show its large sample and finite sample performances.