Flachaire

Publications

Heterogeneous anchoring in dichotomous choice valuation frameworkJournal articleEmmanuel Flachaire, Guillaume Hollard and Stéphane Luchini, Recherches économiques de Louvain, Volume 73, Issue 4, pp. 369-385, 2007

This article addresses the important issue of anchoring in contingent valuation surveys that use the double-bounded elicitation format. Anchoring occurs when responses to the follow-up dichotomous choice valuation question are influenced by the bid presented in the initial dichotomous choice question. Specifically, we adapt a theory from psychology to characterize respondents as those who are likely to anchor and those who are not. Using a model developed by Herriges and Shogren (1996), our method appears successful in discriminating between those who anchor and those who did not. An important result is that when controlling for anchoring ? and allowing the degree of anchoring to differ between respondent groups ? the efficiency of the double-bounded welfare estimate is greater than for the initial dichotomous choice question. This contrasts with earlier research that finds that the potential efficiency gain from the double-bounded questions is lost when anchoring is controlled for and that we are better off not asking follow-up questions. JEL Classification: Q26, C81, D71.

Model Selection in Iterative Valuation QuestionsJournal articleEmmanuel Flachaire and Guillaume Hollard, Revue d'économie politique, Volume 117, Issue 5, pp. 853-865, 2007

In this article, we propose a unified framework that accomodates many of the existing models for dichotomous choice contingent valuation with follow-up and allows to discriminate between them by simple parametric tests of hypothese. Our empirical results show that the Range model, developped in Flachaire and Hollard [2007], outperforms other standard models and confirms that, when uncertain, respondents tend to accept proposed bids.

Controlling Starting-Point Bias in Double-Bounded Contingent Valuation SurveysJournal articleEmmanuel Flachaire and Guillaume Hollard, Land Economics, Volume 82, Issue 1, pp. 103-111, 2006

In this paper, we study starting-point bias in double-bounded contingent valuation surveys. This phenomenon arises in applications that use multiple valuation questions. Indeed, response to follow-up valuation questions may be influenced by the bid proposed in the initial valuation question. Previous researches have been conducted in order to control for such an effect. However, they find that efficiency gains are lost when we control for undesirable response effects, relative to a single dichotomous choice question. Contrary to these results, we propose a way to control for starting-point bias in double-bounded questions with gains in efficiency.

Une approche comportementale de l'évaluation contingenteJournal articleEmmanuel Flachaire and Guillaume Hollard, Revue Économique, Volume 57, Issue 2, pp. 315-329, 2006

Public economics proposed various models that intend to determine the optimal provision of public goods based on individual preferences. To provide decision makers with empirical recommendations, economists thus need to elicit individual preferences, and more precisely the marginal rate of substitution between private and public goods. Contingent valuation has proved a useful, and successful, tool to gather information on individual preferences. However, contingent valuation has been proved sensitive to various biases. In other words, variables that are not expected to have any influence do so in practice. In this paper, we propose a methodology, based on social psychology, which allows the identification of individuals that are proved immune to biases. This allows designing more powerful, bias free, estimation of individual preferences. Two distinct applications are provided.Classification JEL : C81, C93, Q26

More Efficient Tests Robust to Heteroskedasticity of Unknown FormJournal articleEmmanuel Flachaire, Econometric Reviews, Volume 24, Issue 2, pp. 219-241, 2005

In the presence of heteroskedasticity of unknown form, the Ordinary Least Squares parameter estimator becomes inefficient, and its covariance matrix estimator inconsistent. Eicker (1963) and White (1980) were the first to propose a robust consistent covariance matrix estimator, that permits asymptotically correct inference. This estimator is widely used in practice. Cragg (1983) proposed a more efficient estimator, but concluded that tests basd on it are unreliable. Thus, this last estimator has not been used in practice. This article is concerned with finite sample properties of tests robust to heteroskedasticity of unknown form. Our results suggest that reliable and more efficient tests can be obtained with the Cragg estimators in small samples.

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.