After a great (and long) week, the SIdE (Italian Econometric Association) Summer School, on Machine Learning Algorithms for Econometricians is now over…
Monthly Archives: July 2019
SIDE Summer School, day 6
Saturday morning, final set of slides for the SIdE (Italian Econometric Association) Summer School, on Machine Learning Algorithms for Econometricians. The first part is now online, on time series
as well as the second part, on causal inference (with just a broad overview), which is also
SIDE Summer School, days 4 and 5
Almost the final set of slides for the SIdE (Italian Econometric Association) Summer School, on Machine Learning Algorithms for Econometricians. The eighth series of slides are online,
as well as the nineth series (also online)
and the twelweth (online)
SIDE Summer School, day 3
Tomorrow morning, it will be the third day of the SIdE (Italian Econometric Association) Summer School, on Machine Learning Algorithms for Econometricians. The fifth series of slides are online.
and so is the sixth series, that are online
and the seventh series of slides, that are online
SIDE Summer School, day 2
Tomorrow morning, it will be the second day of the SIdE (Italian Econometric Association) Summer School, on Machine Learning Algorithms for Econometricians. The third series of slides are online.
and the fourth one are also online.
SIDE Summer School, day 1
This morning, we start the SIdE (Italian Econometric Association) Summer School, on Machine Learning Algorithms for Econometricians. Emmanuel Flachaire will start with a presentation of nonparametric econometric techniques. I will then get back to the geometry of (standard) econometric techniques, to introduce kernels. The first series of slides are online.
I will then spend more time on the (popular) idea of “least squares” and mention other loss functions. Slides are online.
Optimal transport on large networks
With Alfred Galichon and Lucas Vernet, we recently uploaded a paper entitled optimal transport on large networks on arxiv.
This article presents a set of tools for the modeling of a spatial allocation problem in a large geographic market and gives examples of applications. In our settings, the market is described by a network that maps the cost of travel between each pair of adjacent locations. Two types of agents are located at the nodes of this network. The buyers choose the most competitive sellers depending on their prices and the cost to reach them. Their utility is assumed additive in both these quantities. Each seller, taking as given other sellers prices, sets her own price to have a demand equal to the one we observed. We give a linear programming formulation for the equilibrium conditions. After formally introducing our model we apply it on two examples: prices offered by petrol stations and quality of services provided by maternity wards (only the later is described here for privacy issues). These examples illustrate the applicability of our model to aggregate demand, rank prices and estimate cost structure over the network. We insist on the possibility of applications to large scale data sets using modern linear programming solvers such as Gurobi.
Demand for gas in gas stations in Britanny, and demand for maternity in France (with border correction)
In addition to this paper we released a R toolbox to implement our results and an online tutorial, optimalnetwork.github.io.
Pareto Models for Top Incomes
This week, The Society for the Study of Economic Inequality (ECINEQ) organised the Eighth ECINEQ Meeting 2019 in Paris, hosted by the Paris School of Economics and the World Inequality Lab. Emmanuel Flachaire was there to present our joint work on Pareto Models for Top Incomes. Slides are also available
The paper is still available on hal, and the package (TopIncomes) is also available from github,
library(devtools)
install_github("freakonometrics/TopIncomes")
library(TopIncomes)