Publications

Résumé The bootstrap is a technique for performing statistical inference. The underlying idea is that most properties of an unknown distribution can be estimated as the same properties of an estimate of that distribution. In most cases, these properties must be estimated by a simulation experiment. The parametric bootstrap can be used when a statistical model is estimated using maximum likelihood since the parameter estimates thus obtained serve to characterise a distribution that can subsequently be used to generate simulated data sets. Simulated test statistics or estimators can then be computed for each of these data sets, and their distribution is an estimate of their distribution under the unknown distribution. The most popular sort of bootstrap is based on resampling the observations of the original data set with replacement in order to constitute simulated data sets, which typically contain some of the original observations more than once, some not at all. A special case of the bootstrap is a Monte Carlo test, whereby the test statistic has the same distribution for all data distributions allowed by the null hypothesis under test. A Monte Carlo test permits exact inference with the probability of Type I error equal to the significance level. More generally, there are two Golden Rules which, when followed, lead to inference that, although not exact, is often a striking improvement on inference based on asymptotic theory. The bootstrap also permits construction of confidence intervals of improved quality. Some techniques are discussed for data that are heteroskedastic, autocorrelated, or clustered.
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Résumé The digital age allows data collection to be done on a large scale and at low cost. This is the case of genealogy trees, which flourish on numerous digital platforms thanks to the collaboration of a mass of individuals wishing to trace their origins and share them with other users. The family trees constituted in this way contain information on the links between individuals and their ancestors, which can be used in historical demography, and more particularly to study migration phenomena. The case of 19th century France is taken as an example, using data from the family trees of 238,009 users of the Geneanet website, or 2.5 million (unique) individuals. Using the geographical coordinates of the birthplaces of 25,485 ancestors born in France between 1800 and 1804 and those of their descendants (24,516 children, 29,715 grandchildren and 62,165 great-grandchildren), we study migration between generations at several geographical scales. We start with a broad scale that of the departments, to reach a much finer one, that of the cities. Our results are consistent with those of the literature traditionally based on the parish or civil status registers. The results show that the use of collaborative genealogy data not only makes it possible to support previous findings of the literature, but also to enrich them.
Mots clés 19th Century, Migration, Collaborative data, Genealogy
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Résumé In this paper, we investigate the effects of trade in foods on obesity in Mexico. To do so, we match data on Mexican food imports from the U.S. with anthropometric and food expenditure data. Our findings suggest that exposure to food imports from the U.S. can explain up to 20% of the rise in obesity prevalence among Mexican women between 1988 and 2012. Pro-obesity effects are driven by areas more exposed to unhealthy food imports. We also find evidence in favour of a price mechanism. By linking trade flows to obesity, the paper sheds light on an important channel through which globalisation may affect health. (C) 2019 Elsevier B.V. All rights reserved.
Mots clés Trade Obesity Nutrition transition Mexico
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