Next Wednesday, I will be giving a seminar at Università degli studi dell’Insubria on Machine Learning and Econometrics. The presentation is based on Econométrie et Machine Learning (so far only in French) written with Emmanuel Flachaire and Antoine Ly. Slides are now available online…
Tomorrow and Friday, I will give a crash course on advanced techniques in econometrics. Slides are now online (with animations). There will be breaks to discuss, and I will attend the doctoral seminar on Thursday.
Next week, I will be in Varese, Italy, for a series of PhD lectures on advanced econometrics. Slides are now online. I will also give a seminar in two weeks… There should be more on the blog soon…
This Tuesday, I will be giving the second part of the (crash) graduate course on advanced tools for econometrics. It will take place in Rennes, IMAPP room, and I have been told that there will be a visio with Nantes and Angers. Slides for the morning are online, as well as slides for the afternoon.
In the morning, we will talk about variable section and penalization, and in the afternoon, it will be on changing the loss function (quantile regression).
This Monday, I will be giving the first part of the (crash) graduate course on advanced tools for econometrics. It will take place in Rennes, IMAPP room, and I have been told that there will be a visio with Nantes and Angers. Slides for the morning are online, as well as slides for the afternoon.
In the morning, we will talk about smoothing techniques, and in the afternoon, it will be on simulations and bootstrap techniques.
On Thursday, March 23rd, I will give the third lecture of the PhD course on advanced tools for econometrics, on model selection and variable selection, where we will focus on ridge and lasso regressions . Slides are available online.
The first part was on on Nonlinearities in Econometric models, and the second one on Simulations.
On Thursday, March 9nd, I will give the second lecture of the PhD course on advanced tools for econometrics, on simulation techniques (and bootstrap). Slides are available online.
The first part is this Thurdays, on Nonlinearities in Econometric models.
I will give a short graduate course for PhD students, in Rennes, on Thurday mornings, in March (2nd, 9th, 23rd and 30th). The agenda will be
Nonlinear Regression Models and Smoothing Techniques
Bootstrapping and Regression
Penalized Regression Models and LASSO
Quantile Regression and Expectiles
There will be slides available by the end of February.
I will be in London, UK, at the Centre for Central Banking Studies, invited as a keynote speaker for a major conference. For my talk, on Econometric Models and Statistical Learning Techniques, the agenda is the follownig
- introduction on High Dimensional Data and Modeling
- foundations of econometric models, and probabilistic aspects
- machine learning techniques, with a discussion on boosting, cross validation
- classification, from the logistic regression to trees and random forest
- machine learning tools that can be used in econometrics, such as bootstrap, principal component analysis / partial least squares, and instrumental variables and variable selection
Slides are avaible (as usual, the pdf version is more informative than the one on slideshare where animations are missing)
With Mathieu Pigeon, we finalized a short paper on ‘macro’ vs. ‘micro’ methods in claims reserving. The paper is now online on arxiv
Traditionally, actuaries have used run-off triangles to estimate reserve (“macro” models, on agregated data). But it is possible to model payments related to individual claims. If those models provide similar estimations, we investigate uncertainty related to reserves, with “macro” and “micro” models. We study theoretical properties of econometric models (Gaussian, Poisson and quasi-Poisson) on individual data, and clustered data. Finally, application on claims reserving are considered.
There were a lot of posts, recently, related to those topics, starting with Noah Smith ‘s piece entitled “Economics has a Math Problem” and more recently “Econometrics, Math, and Machine Learning…what?” by Matt Bogard. I don’t have (yet) a clear mind on those issues, but there are still a few thoughts that I wanted to share. I did not really want to, but I’ve been asked, on Twitter, and I thought it might be good to write them down, to clarify some ideas I have, but also (probably, hopefully) to get interesting feedbacks.
Continue reading Some thoughts on Economics, Mathematics, Econometrics, Statistics, Machine Learning, etc
I will give a talk on “Modeling Dynamic Incentives: Application to Basketball” at the GERAD (Groupe d’études et de recherche en analyse des décisions) on
June, 10th June, 6th. This is some joint work with Nathalie Colombier and Romuald Elie
An important aspect of the strategy of most organizations is the provision of incentives to the employees to meet the organization’s objectives. Typically this implies tying pay to performance (see Prendergast, 1999). In order to reward employees for their effort, ﬁrms spend considerable resources on performance evaluations. In many cases, evaluation consists of comparing actual performance to a pre-deﬁned individual target. Another frequently used format is relative performance evaluation. Relative performance evaluation may motivate employees to work harder.But it may also be demoralizing and create an excessively competitive workplace, which may hinder overall performance; see Lazear (1989). Determining the overall impact of relative performance evaluation is crucial for companies. Economic research on relative performance evaluation has mainly focused on the comparison of ﬁnal performances between competitors,like in tournament theory, and on quantitative and subjective performance ratings (Lazear and Gibbs, 2009). In contrast, what happens during a competition and the impact of feedback frequency on effort have so far received little attention. Following Berger and Pope (2011), we decided to use a basketball application to get a better understanding of the role of the feedback information. Sports datasets allow to observe score and team behavior continuously (during a game but also during the season) which can be use as a proxy of the effort. Berger an Pope (2010) asked ”can loosing lead to winning ?” looking at the impact of the halftime score difference on winning probability in NCAA (college) and NBA (pro) games. More precisely, they studied whether a team loosing at halftime is more likely to win than expected using a logit model. They ﬁnd that usually the higher the score difference the more likely the are to win. But if the halftime score difference is around 0 they observe a discontinuity: loosing with a small difference (e.g. down by 1 point) can lead to increase the effort and win the game. In this paper we try answer the question ”when loosing lead to winning ?”.