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An axiomatic approach is used to develop a one-parameter family of measures of divergence between distributions. These measures can be used to perform goodness-of-fit tests with good statistical properties. Asymptotic theory shows that the test statistics have well-defined limiting distributions which are, however, analytically intractable. A parametric bootstrap procedure is proposed for implementation of the tests. The procedure is shown to work very well in a set of simulation experiments, and to compare favorably with other commonly used goodness-of-fit tests. By varying the parameter of the statistic, one can obtain information on how the distribution that generated a sample diverges from the target family of distributions when the true distribution does not belong to that family. An empirical application analyzes a U.K. income dataset.
Standard kernel density estimation methods are very often used in practice to estimate density functions. It works well in numerous cases. However, it is known not to work so well with skewed, multimodal and heavy-tailed distributions. Such features are usual with income distributions, defined over the positive support. In this paper, we show that a preliminary logarithmic transformation of the data, combined with standard kernel density estimation methods, can provide a much better fit of the density estimation.
This Chapter is about the techniques, formal and informal, that are commonly used to give quantitative answers in the field of distributional analysis - covering subjects including inequality, poverty and the modelling of income distributions. It deals with parametric and non-parametric approaches and the way in which imperfections in data may be handled in practice.
After a decade of research on the relationship between institutions and growth, there is no consensus about the exact way in which these two variables interact. In this paper we re-examine the role that institutions play in the growth process using data for developed and developing economies over the period 1975–2005. Our results indicate that the data is best described by an econometric model with two growth regimes. Political institutions are the key determinant of which regime an economy belongs to, while economic institutions have a direct impact on growth rates within each regime. These findings support the hypothesis that political institutions are one of the deep causes of growth, setting the stage in which economic institutions and standard covariates operate.
This paper tests whether individual perceptions of markets as good or bad for a public good is correlated with the propensity to report gaps in willingness to pay and willingness to accept revealed within an incentive compatible mechanism. Identifying people based on a notion of market affinity, we find a substantial part of the gap can be explained by controlling for some variables that were not controlled for before. This result suggests the valuation gap for public goods can be reduced through well-defined variables.
We investigate a general problem of comparing pairs of distributions which includes approaches to inequality measurement, the evaluation of “unfair” income inequality, evaluation of inequality relative to norm incomes, and goodness of fit. We show how to represent the generic problem simply using (1) a class of divergence measures derived from a parsimonious set of axioms and (2) alternative types of “reference distributions.” The problems of appropriate statistical implementation are discussed and empirical illustrations of the technique are provided using a variety of reference distributions.
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.