Lunch seminar

14 January 2019

A next meeting within the Lunch Seminar series will take place on Wednesday, January 23 at 1.15 pm in room A103. This time, dr hab. Katarzyna Kopczewska will present her work “Spatial bootstrapped microeconometrics: Forecasting for out-of-sample geo-locations” (the abstract below). 

The Faculty Lunch Time Seminar series offers a great opportunity for the exchange of knowledge and experience among our Faculty. Faculty members are invited to present their ongoing research and share research ideas to get feedback, inspiration, and maybe to develop new networks for further studies. Everyone is welcome – Faculty members, PhD students, MA and BA students, as well as guests from outside of our Faculty, so please spread the word. 

The full schedule of the seminar series in this term is available here.

If you would like to present in the seminar, please contact Ewa Zawojska via e-mail (Mi$9vumRcF{\ANs8G|f[6`?]#[/db1f`a=E]sK}Wi0PbKQCQ').

Abstract: Spatial econometrics for the big data point geo-locations has a limited possibility of forecasting with a calibrated model for the new out-of-sample geopoints. This is because of spatial weights matrix W defined for in-sample observations only as well as the computational complexity for a huge W. This paper proposes the novel methodology which calibrates both space and model using bootstrap and tessellation. Bootstrapping enables the calibration of the econometric model without the need for estimation on the whole dataset. The best bootstrapped model is selected with Partitioning Around Medoids (PAM) algorithm, which classifies the regression coefficients jointly, in a nonindependent manner. Tessellation for the points used in the selected best model allows for a representative division of space. New out-of-sample points are assigned to tiles and linked to the spatial weights matrix as a replacement for an original point. This efficient procedure supports the big data geo-located point data and makes feasible a usage of calibrated spatial models as a forecasting tool for out-of-sample data.

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