The Role of Economic Space in Decision Making: CommentJournal articleEmmanuel Flachaire, Annals of Economics and Statistics, Issue 77, pp. 21-28, 2005

Over the last few years, Margaret SLADE contributed to some major improvments in the field of industrial economics. The important question of location and spatial interaction in economic decision is one of her central interests. Her paper, prepared for a presentation at the « Conférence de L'ADRES » in Paris, presents the ways and the methods she developed with her coauthors to incorporate the influence of space location in regression model. The new attention to specifying, estimating and testing for the presence of spatial interaction they have taken, concerns the use of semiparametric methods to allow less restrictions on the form of the spatial dependence. The paper is clearly written, without technical developments and the discussion of potential applications is very convincing on the significant role that the location can take in economic devisions.

Propriétés en échantillon fini des tests robustes à l'hétéroscédasticité de forme inconnueJournal articleEmmanuel Flachaire, Annals of Economics and Statistics, Issue 77, pp. 187-199, 2005

In this paper, I investigate the finite sample performance of a test robust to heteroskedasticity of unknown form, based on the consistent covariance matrix estimator proposed in Eicker (1963) and White (1980). The simulation results suggest that, as often used in practice, this test could be unreliable and inefficient, even if the sample size is large. They suggest that reliable and more efficient inference can be obtained if a heteroskedasticity-robust test is computed with the restricted residuals and an appropriate bootstrap method.

Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrapJournal articleEmmanuel Flachaire, Computational Statistics & Data Analysis, Volume 49, Issue 2, pp. 361-376, 2005

In regression models, appropriate bootstrap methods for inference robust to heteroskedasticity of unknown form are the wild bootstrap and the pairs bootstrap. The finite sample performance of a heteroskedastic-robust test is investigated with Monte Carlo experiments. The simulation results suggest that one specific version of the wild bootstrap outperforms the other versions of the wild bootstrap and of the pairs bootstrap. It is the only one for which the bootstrap test gives always better results than the asymptotic test.

Bootstrapping heteroskedasticity consistent covariance matrix estimatorJournal articleEmmanuel Flachaire, Computational Statistics, Volume 17, Issue 4, pp. 501-506, 2002
Les méthodes du bootstrap dans les modèles de régressionJournal articleEmmanuel Flachaire, Économie & Prévision, Volume 142, Issue 1, pp. 183-194, 2000

[fre] Dans la pratique, la plupart des statistiques de test ont une distribution de probabilité de forme inconnue. Généralement, on utilise leur loi asymptotique comme approximation de la vraie loi. Mais, si l'échantillon dont on dispose n'est pas de taille suffisante, cette approximation peut être de mauvaise qualité et les tests basés dessus largement biaises. Les méthodes du bootstrap permettent d'obtenir une approximation de la vraie loi de la statistique, en général plus précise que la loi asymptotique. Elles peuvent également servir à approximer la loi d'une statistique qu'on ne peut pas calculer analytiquement. Dans cet article, nous présentons une méthodologie générale du bootstrap dans le contexte des modèles de régression. [eng] Bootstrap Methods in Regression Models by Emmanuel Flachaire . In practice, we rarely know the true probability distribution of a test statistic and we generally base tests on its asymptotic distribution. If the sample size is not large enough, the asymptotic distribution could be a poor approximation of the true distribution. Consequently, tests based on it could be largely biased. Bootstrap methods yield a more accurate approximation of the distribution of a test statistic than the approximation obtained from the first-order asymptotic theory. Moreover, they provide a way of substituting computation for mathematical analysis when it proves hard to calculate the asymptotic distribution of an estimator or statistic. In this paper, we present a general methodology of the bootstrap in regression models.

A better way to bootstrap pairsJournal articleEmmanuel Flachaire, Economics Letters, Volume 64, Issue 3, pp. 257-262, 1999

In this paper we are interested in heteroskedastic regression models, for which an appropriate bootstrap method is bootstrapping pairs, proposed by Freedman (1981). We propose an ameliorate version of it, with better numerical performance.