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 - Shin Kanaya (Essex)
Title: Universal inference with time series data (by Shin Kanaya and Yuya Sasaki)
Abstract: In this paper, we develop a likelihood-based inference method for time-series data, building on the universal inference framework of Wasserman, Ramdas, and Balakrishnan (2020, PNAS), whose original work focused primarily on the i.i.d. setting. We consider parametric Markov and hidden Markov models, which encompass a wide range of models used in macroeconomics and finance, including autoregressive, (G)ARCH, and state-space models. Our method retains the original spirit of universal inference: the resulting confidence sets and hypothesis tests are valid without relying on conventional regularity conditions and enjoy finite-sample guarantees. A novel use of the Markov property plays a crucial role in our construction, yielding inference procedures that remain uniformly valid over a class of data-generating processes without imposing either ergodicity or stationarity. We illustrate the proposed method through Monte Carlo simulations and empirical applications.