Vous trouverez sur cette page quelques travaux personnels sur des sujets qui m’intéressent, dans le domaine de la science de données et du machine learning appliqué aux politiques publiques.

Contact information

nicolas.saleille@gmail.com
Github page

Essay on statistical models to forecast macroeconomic variables

In this thesis, we explore several short-term forecasting methods to predict the quarterly French GDP growth rate. We consider various forecasting horizons ranging from 1 to 8 month before official figure releases... (click to continue and download pdf)

Efficient statistical methods to compute CVA sensitivities

Credit Valuation Adjustement (CVA) measures the counterparty risk embedded in any financial derivative. The main difficulties linked to CVA are of computational order, since its computation relies on heavy Monte-Carlo...

High-dimensional panel data using the Lasso

Methods to deal with high-dimensional problems are of interest in micro-econometrics mostly as a way to perform model selection, whether it is in a context of a non-parametric model estimated by sieve approximation, selection of control variables or instruments...

EMVS, the EM approach to Bayesian Variable Selection

The Expectation-Maximisation applied to Variable Selection algorithm (EMVS) is a nice Bayesian selection tool. Its main feature is to lower considerably the computationnal power required to estimate posterior distributions in the feature space via MCMC...

Quantile regression for panel data

We review the theoretical foundations and the most important properties of the quantile fixed-effect estimator suggested by Koenker (2004) to handle panel data. This estimator was specifically designed to take into account the panel structure of datasets...