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This book allows those with a basic knowledge of econometrics to learn the main nonparametric and semiparametric techniques used in econometric modelling, and how to apply them correctly. It looks at kernel density estimation, kernel regression, splines, wavelets, and mixture models, and provides useful empirical examples throughout. Using empirical application, several economic topics are addressed, including income distribution, wage equation, economic convergence, the Phillips curve, interest rate dynamics, returns volatility, and housing prices. A helpful appendix also explains how to implement the methods using R. This useful book will appeal to practitioners and researchers who need an accessible introduction to nonparametric and semiparametric econometrics. The practical approach provides an overview of the main techniques without including too much focus on mathematical formulas. It also serves as an accompanying textbook for a basic course, typically at undergraduate or graduate level.
Les méthodes d'estimation non-paramétrique et semi-paramétrique ont suscité beaucoup d'intérêt ces dernières années. En économétrie, elles permettent une plus grande flexibilité dans le choix des modèles de régression. Opposées dans un premier temps à l'économétrie classique, ces techniques se sont finalement avérées lui être fortement complémentaires, pouvant légitimer le choix d'un modèle paramétrique. Cet ouvrage présente une introduction accessible à ces méthodes (noyau, polynômes locaux, splines, ondelettes, modèles de mélanges). Conçu comme un ouvrage pédagogique illustré de plusieurs applications, l'accent est mis sur la compréhension des méthodes, les intuitions sous-jacentes et la mise en œuvre en pratique. Il s'adresse aux étudiants en sciences économiques et en gestion, aux élèves des écoles d'ingénieurs et de commerce, ainsi qu'aux professionnels et praticiens de l'économétrie.
The wild bootstrap is studied in the context of regression models with heteroskedastic disturbances. We show that, in one very specific case, perfect bootstrap inference is possible, and a substantial reduction in the error in the rejection probability of a bootstrap test is available much more generally. However, the version of the wild bootstrap with this desirable property is without the skewness correction afforded by the currently most popular version of the wild bootstrap. Simulation experiments show that this does not prevent the preferred version from having the smallest error in rejection probability in small and medium-sized samples.
Surveys are sometimes viewed with suspicion when used to provide economic values, since they are sensitive to framing effects. However, the extent to which those effects may vary between individuals has received little attention. Are some individuals less sensitive to framing effects than others? We use the theory of social representation to assign to each individual a new variable to serve as a proxy for the individual's sensitivity to framing effects. This allows to gather new and relevant information to limit the impact of framing effects. We examine two framing effects, starting-point bias and willingness-to-pay and willingness-to-accept divergence.
Dans cette étude, nous estimons l’influence de certaines caractéristiques sur les prix des logements avec la méthode des prix hédoniques. Nous utilisons tout d’abord une approche classique, basée sur un modèle de régression paramétrique avec autocorrélation spatiale. Cette approche présente deux inconvénients : la forme fonctionnelle du modèle et la matrice de poids sont fixées a priori. Nous présentons une approche semi-paramétrique qui permet de pallier ces limites.
A random sample drawn from a population would appear to offer an ideal opportunity to use the bootstrap in order to perform accurate inference, since the observations of the sample are IID. In this paper, Monte Carlo results suggest that bootstrapping a commonly used index of inequality leads to inference that is not accurate even in very large samples. Bootstrapping a poverty measure, on the other hand, gives accurate inference in small samples. We investigate the reasons for the poor performance of the bootstrap, and find that the major cause is the extreme sensitivity of many inequality indices to the exact nature of the upper tail of the income distribution. Consequently, a bootstrap sample in which nothing is resampled from the tail can have properties very different from those of the population. This leads us to study two non-standard bootstraps, the m out of n bootstrap, which is valid in some situations where the standard bootstrap fails, and a bootstrap in which the upper tail is modelled parametrically. Monte Carlo results suggest that accurate inference can be achieved with this last method in moderately large samples.
No abstract is available for this item.
We examine the statistical performance of inequality indices in the presence of extreme values in the data and show that these indices are very sensitive to the properties of the income distribution. Estimation and inference can be dramatically affected, especially when the tail of the income distribution is heavy, even when standard bootstrap methods are employed. However, use of appropriate semiparametric methods for modelling the upper tail can greatly improve the performance of even those inequality indices that are normally considered particularly sensitive to extreme values.
In this article, we develop a dichotomous choice model with follow-up questions that describes the willingness to pay being uncertain in an interval. The initial response is subject to starting point bias. Our model provides an alternative interpretation of the starting point bias in the dichotomous choice valuation surveys. Using the Exxon Valdez survey, we show that, when uncertain, individuals tend to answer “yes”.
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.