ENSAE ParisTech

About: ENSAE ParisTech is a education organization based out in Palaiseau, France. It is known for research contribution in the topics: Estimator & Minimax. The organization has 406 authors who have published 1104 publications receiving 31916 citations. The organization is also known as: École nationale de la statistique et de l'administration économique & Ecole nationale de la statistique et de l'administration economique.

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

Abstract: Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.

Operations on fuzzy numbers

Abstract: A fuzzy number is a fuzzy subset of the real line whose highest membership values are clustered around a given real number called the mean value ; the membership function is monotonia on both sides of this mean value. In this paper, the usual algebraic operations on real numbers are extended to fuzzy numbers by the use of a fuzzification principle. The practical use of fuzzified operations is shown to be easy, requiring no more computation than when dealing with error intervals in classic tolerance analysis. The field of applications of this approach seems to be large, since it allows many known algorithms to be fitted to fuzzy data.

Innovation and Productivity across Four European Countries

Abstract: NBER WORKING PAPER SERIESINNOVATION AND PRODUCTIVITY ACROSS FOUR EUROPEAN COUNTRIESRachel GriffithElena HuergoJacques MairesseBettina PetersWorking Paper 12722http://www.nber.org/papers/w12722NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts AvenueCambridge, MA 02138December 2006We would like to thank Laura Abramovsky, Manuel Arellano, Rupert Harrison, Jordi Jaumandreu,Elizabeth Kremp, Pierre Mohnen, Helen Simpson. The analysis contained in this paper was fundedby the European Commission, the ESRC and ESF under grant RES-000-23-0901 and the AdvancedInstitute of Management Research (AIM). All errors and omissions remain the responsibility of theauthors. The views expressed herein are those of the author(s) and do not necessarily reflect the viewsof the National Bureau of Economic Research.© 2006 by Rachel Griffith, Elena Huergo, Jacques Mairesse, and Bettina Peters. All rights reserved.Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission providedthat full credit, including © notice, is given to the source.

Two-way Fixed Effects Estimators with Heterogeneous Treatment Effects

Abstract: Linear regressions with period and group fixed effects are widely used to estimate treatment effects. We show that they identify weighted sums of the average treatment effects (ATE) in each group and period, with weights that may be negative. Due to the negative weights, the linear regression estimand may for instance be negative while all the ATEs are positive. In two articles that have used those regressions, half of the weights are negative. We propose another estimator that solves this issue. In one of the articles we revisit, it is of a different sign than the linear regression estimator.

An anatomy of international trade: evidence from french firms

Abstract: We examine the sales of French manufacturing firms in 113 destinations, including France itself. Several regularities stand out: (i) the number of French firms selling to a market, relative to French market share, increases systematically with market size; (ii) sales distributions are similar across markets of very different size and extent of French participation; (iii) average sales in France rise systematically with selling to less popular markets and to more markets. We adopt a model of firm heterogeneity and export participation which we estimate to match moments of the French data using the method of simulated moments. The results imply that over half the variation across firms in market entry can be attributed to a single dimension of underlying firm heterogeneity: efficiency. Conditional on entry, underlying efficiency accounts for much less of the variation in sales in any given market. We use our results to simulate the effects of a 10 percent counterfactual decline in bilateral trade barriers on French firms. While total French sales rise by around $16 billion (U.S.), sales by the top decile of firms rise by nearly $23 billion (U.S.). Every lower decile experiences a drop in sales, due to selling less at home or exiting altogether.