cabecera1

folder News - docs

Documents

pdf Call for papers: Workshop in Machine Learning In Labor, Education, And Health Economics, 19-20 November 2020

By 80 downloads

Download (pdf, 274 KB)

200203_CfP_Machine_Learning.pdf

Machine Learning In Labor, Education, And Health Economics - International workshop

19-20 November 2020, IAB - Germany

The Institute of Employment Research (IAB), the Friedrich-Alexander-Universität Erlangen-Nürnberg
(FAU), and the Labour and Socio-Economic Research Center (LASER) are pleased to announce a
workshop on machine learning in economics. Empirical research in economics typically focuses on
the unbiased estimation of causal effects. In contrast, statistics and computer science place more
value on prediction (especially out-of-sample) and data-driven selection of models and variables.
So far, only few studies apply these methods in empirical economic research, but their importance
is growing. This holds in particular with the increasing availability of big data for economic research.
The two-day workshop seeks to bring together researchers who apply machine learning methods in
the following fields: Labor economics, economics of education and health economics.

For more information download the Call in attachment.

Image University of Bologna Summer School on Experimental Auctions: Theory and Applications in Food Marketing and Consumer Preferences Analysis

By 82 downloads

Download (jpg, 52 KB)

SummerSchool_ExperimentalAuctions2020.jpg

University of Bologna Summer School on Experimental Auctions:  Theory and Applications in Food Marketing and Consumer Preferences Analysis
 
University of Bologna Summer School on Experimental Auctions: 
Theory and Applications in Food Marketing and Consumer Preferences Analysis
http://experimentalauctions.jimdo.com/

Verona, Italy, June 29-July 4, 2020

Director: Maurizio Canavari

Co-organisers: Diego Begalli, Claudia Bazzani, Katia Laura Sidali
Department of Business Economics
University of Verona

 
The course is aimed at learning the fundamentals and the recent advances in experimental auctions. It will provide the appropriate techniques to construct an experimental design, with software applications and workshops regarding specific problems in the field of food market valuation. It will also provide the appropriate knowledge of the models used in the experimental auction literature for the data analysis. Results interpretation and policy implications will be also discussed.

Instructors:
Rodolfo M. Nayga, jr. – Professor and Tyson Endowed Chair in Food Policy Economics in the Department of Agricultural Economics and Agribusiness at University of Arkansas, USA

Andreas Drichoutis – Assistant Professor in the Department of Agricultural Economics at Agricultural University of Athens, Greece

Maurizio Canavari – Associate Professor in the Department of Agricultural Sciences at Alma Mater Studiorum-University of Bologna, Italy

Please feel free to spread the information among your professional contacts.

Image NIPE Summer School 2020: The econometrics of big data, with Christian Hansen (UChicago) Popular

By 209 downloads

NIPE Summer School 2020: The econometrics of big data, with Christian Hansen (UChicago)

More info here: https://bit.ly/30eE1Kw

The 17th Edition of the NIPE Summer School in Econometrics will run between June 15 to June 18, 2020. "The Econometrics of Big Data" is the selected topic for this edition, and the course will be taught by Professor Christian B. Hansen, from the University of Chicago - Booth School of Business.

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. Our goal in this course is two-fold. First, we wish to provide an overview and introduction to several modern methods, largely coming from statistics and machine learning, which are useful for exploring high-dimensional data and for building prediction models in high-dimensional settings. Second, we will present 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.

We look forward to welcoming you in Braga!