Multidimensional Poverty Measures from an Information Theory Perspective

This paper proposes to use an information theory approach to the design of multidimensional poverty indices. Traditional monetary approaches to poverty rely on the strong assumption that all relevant attributes of well-being are perfectly substitutable. Based on the idea of the essentiality of some attributes, scholars have recently suggested multidimensional poverty indices where the existence of a trade-off between attributes is relevant only for individuals who are below a poverty threshold in all of them (Bourguignon and Chakravarty 2003, Tsui 2002). The present paper proposes a method which encompasses both approaches and, moreover, it opens the door to an intermediate position which allows, to a certain extent, for substitution of attributes even in the case in which one or more (but not all) dimensions are above the set threshold. An application using individual well-being data from Indonesian households in 2000 is presented in order to compare the results under the different approaches.Multidimensional Poverty, Information Theory

Discounting The Equity Premium Puzzle

This paper applies recent tests of stochastic dominance of several orders proposed by Linton, Maasoumi and Whang (2003) to reexamine the equity premium puzzle. An advantage of this nonparametric framework is that it provides a means to assess whether the existence of a premium is due to an incorrect choice of either the utility function or the underlying returns distribution. The approach is applied to a range of data sets including the S&P500Equity premium puzzle, stochastic dominance, nonparametric, subsampling.

Consistent Testing for Stochastic Dominance: A Subsampling Approach

We propose a procedure for estimating the critical values of the Klecan, McFadden, and McFadden (1990) test for first and second order stochastic dominance in the general k-prospect case. Our method is based on subsampling bootstrap. We show that the resulting test is consistent. We allow for correlation amongst the prospects and for the observations to be autocorrelated over time. Importantly, the prospects may be the residuals from certain conditional models.Bootstrap, Prospect theory, Stochastic dominance

Consistent Testing for Stochastic Dominance: A Subsampling Approach

We study a very general setting, and propose a procedure for estimating the critical values of the extended Kolmogorov-Smirnov tests of First and Second Order Stochastic Dominance due to McFadden (1989) in the general k-prospect case. We allow for the observations to be generally serially dependent and, for the first time, we can accommodate general dependence amongst the prospects which are to be ranked. Also, the prospects may be the residuals from certain conditional models, opening the way for conditional ranking. We also propose a test of Prospect Stochastic Dominance. Our method is based on subsampling and we show that the resulting data tests are consistent.Prospect theory, stochastic dominance, stochastic equicontinuity, subsampling.

Parametric and Nonparametric Tests of Limited Domain and Ordered Hypotheses

Abstract. Technical and conceptual advances in testing multivariate linear and non-linear inequality hypotheses in econometrics are summarized. This is done in the context of substantive empirical settings in economics in which either the null, or the alternative, or both hypotheses define more limited domains than the two-sided alternatives typically tested in the classical testing procedures. The desired goal is increased power which is laudable given the endemic power problems of most of the classical asymptotic tests. The impediments are a lack of familiarity with implementation procedures, and characterization problems of distributions under some composite hypotheses. several empirically important cases are identified in which practical "one-sided" tests can be conducted by either the e 2 ? distribution, or the union intersection mechanisms based on the Gaussian variate, or the increasingly feasible and popular resampling/simulation techniques. Point optimal testing and its derivatives find a natural medium here whenever unique characterization of the null distributions for the "least favorable" cases is not possible. Most of the recent econometric literature in this area is parametric deriving from the multivariate extensions of the classical Gaussian means test with ordered alternatives. Tests for variance components, random coefficients, over dispersion, heteroskedasticity, regime change, ARCH effects, curvature regularity conditions on flexible supply, demand, and other economic functions, are examples But nonparametric tests for ordered relations between distributions, or their quantiles, or curvature regularity conditions on nonparametric economic relations, have witnessed rapid development and applications in economics and finance. We detail tests for Stochastic Dominance which indicate a major departure in the practice of empirical decision making in, so far, the areas of welfare and optimal financial strategy

Consistent testing for stochastic dominance: a subsampling approach

We propose a procedure for estimating the critical values of the extended Kolmogorov- Smirnov tests of First and Second Order Stochastic Dominance in the general K-prospect case. We allow for the observations to be serially dependent and, for the …rst time, we can accommodate general dependence amongst the prospects which are to be ranked. Also, the prospects may be the residuals from certain conditional models, opening the way for conditional ranking. We also propose a test of Prospect Stochastic Dominance. Our method is based on subsampling and we show that the resulting tests are consistent and powerful against some N¡1=2 local alternatives. We also propose some heuristic methods for selecting subsample size and demonstrate in simulations that they perform reasonably.

A retrospective on J.D. Sargan and his contribution to Econometrics

This retrospective provides a biographical history of Denis Sargan's career and reviews his contributions to econometrics, emphasizing the breadth of his work in both theoretical and applied econometrics. We include a complete bibliography for Denis and a list of PhD theses that he supervised - students were a substantive facet of his profesional life. Finally, two of Denis' previously unpublished manuscripts on model building now appear in print. Keywords; dynamic specification, econometrics, error correction model, finite sample distributions, identification, instrumental variables, model building, numerical computation, prices, production function, specification searches, wages

Peter Schmidt: econometrician and consummate professional

Peter Schmidt has been one of its best-known and most respected econometricians in the profession for four decades. He has brought his talents to many scholarly outlets and societies, and has played a foundational and constructive role in the development of the field of econometrics. Peter Schmidt has also served and led the development of Econometric Reviews since its inception in 1982. His judgment has always been fair, informed, clear, decisive, and constructive. Respect for ideas and scholarship of others, young and old, is second nature to him. This is the best of traits, and Peter serves as an uncommon example to us all. The seventeen articles that make up this Econometric Reviews Special Issue in Honor of Peter Schmidt represent the work of fifty of the very best econometricians in our profession. They honor Professor Schmidt’s lifelong accomplishments by providing fundamental research work that reflects many of the broad research themes that have distinguished his long and productive career. These include time series econometrics, panel data econometrics, and stochastic frontier production analysis

Robust Ranking of Journal Quality: An Application to Economics

The paper focuses on the robustness of rankings of academic journal quality and research impact in general, and in Economics, in particular, based on the widely-used Thomson Reuters ISI Web of Science citations database (ISI). The paper analyses 299 leading international journals in Economics using quantifiable Research Assessment Measures (RAMs), and highlights the similarities and differences in various RAMs, which are based on alternative transformations of citations and influence. All existing RAMs to date have been static, so two new dynamic RAMs are developed to capture changes in impact factor over time and escalating journal self citations. Alternative RAMs may be calculated annually or updated daily to determine When, Where and How (frequently) published papers are cited (see Chang et al. (2011a, b, c)). The RAMs are grouped in four distinct classes that include impact factor, mean citations and non-citations, journal policy, number of high quality papers, and journal influence and article influence. These classes include the most widely used RAMs, namely the classic 2-year impact factor including journal self citations (2YIF), 2-year impact factor excluding journal self citations (2YIF*), 5-year impact factor including journal self citations (5YIF), Eigenfactor (or Journal Influence), Article Influence, h-index, and PI-BETA (Papers Ignored - By Even The Authors). As all existing RAMs to date have been static, two new dynamic RAMs are developed to capture changes in impact factor over time (5YD2 = 5YIF/2YIF) and Escalating Self Citations. We highlight robust rankings based on the harmonic mean of the ranks of RAMs across the 4 classes. It is shown that emphasizing the 2-year impact factor of a journal, which partly answers the question as to When published papers are cited, to the exclusion of other informative RAMs, which answer Where and How (frequently) published papers are cited, can lead to a distorted evaluation of journal quality, impact and influence relative to the harmonic mean of the ranks