Mini-Course: Introduction to Model Selection, Regularization, and Post-Model Selection Inference
8 September, 2016
Mini-course by Christian Hansen (University of Chicago Booth)
As in many other fields, economists are increasingly making use of high-dimensional models – models with many unknown parameters that need to be inferred from the data. Such models arise naturally in modern data sets that include rich information for each unit of observation (a type of “big data”) and in nonparametric applications where researchers wish to learn, rather than impose, functional forms. High-dimensional models provide a vehicle for modeling and analyzing complex phenomena and for incorporating rich sources of confounding information into economic models.
The goal of this short course is two-fold. First, to provide an overview of and introduction to variable selection methods with an emphasis on penalized estimation methods. Second, to present an introduction to recent proposals that adapt high-dimensional methods to the problem of doing valid inference about model parameters and illustrate applications of these proposals for doing inference about economically interesting parameters.
Dates:
September 8 & 9, 8:30 – 11:45 AM
Location:
Sala de Consejo, Beauchef 851, Floor 4 - Departamento de Ingeniería Industrial, U. de Chile.
Organizers:
Benjamín Villena Roldán
MIPP Chile 2024