Local supersymmetry in supergravity

We have studied the local supersymmetry in two D = 4 supergravity models, with N = 1 and N = 2, given the Lagrangians. We have used a simple method based on the differential of the action H, which provides an alternative systematic derivation of the gauge field variations in the first order formalism. This method may be used to find non-relativistic limits of supergravity models.Departamento de Física Teórica, Atómica y ÓpticaMáster en Físic

Motivic Milnor fiber of a quasi-ordinary hypersurface

Let f : (Cd+1, 0) -> (C, 0) be a germ of complex analytic function such that its zero level defines an irreducible germ of quasi-ordinary hypersurface (S, 0) subset of (Cd+1, 0). We describe the motivic Igusa zeta function, the motivic Milnor fibre and the Hodge-Steenbrink spectrum of f at 0 in terms of topological invariants of (S, 0) subset of (Cd+1, 0)

Growth, convergence and public investment : a bayesian model averaging approach [WP]

The aim of this paper is twofold. First, we study the determinants of economic growth among a wide set of potential variables for the Spanish provinces (NUTS3). Among others, we include various types of private, public and human capital in the group of growth factors. Also, we analyse whether Spanish provinces have converged in economic terms in recent decades. The second objective is to obtain cross-section and panel data parameter estimates that are robust to model speci¯cation. For this purpose, we use a Bayesian Model Averaging (BMA) approach. Bayesian methodology constructs parameter estimates as a weighted average of linear regression estimates for every possible combination of included variables. The weight of each regression estimate is given by the posterior probability of each model.L'objectiu d'aquest estudi és doble. Primer, estudiem els factors determinants del creixement econòmic entre un ampli ventall de possibles variables per a les províncies espanyoles. Entre aquests determinant s'inclouen difrents tipus de capital privat, públic i humà. A més, s'analitza si les províncies espanyoles han convergit en termes econòmics. El segon objectiu és l'obtenció d¿estimacions, transversals i amb dades de panell, robustes a l'especificació del model utilitzant una metodologia bayesiana, la qual construeix els paràmetres estimats com una mitja ponderada dels estimadors lineals de totes les possibles combinacions de models donades les variables tingudes en compte. La ponderació de cada paràmetre estimat bé donada per la probabilitat a posteriori de cada model

Estimating complementarity among vertical restraints: Evidence from manufacturing firms

This paper uses a bivariate probit model to analyze firms' decisions to impose regimes of exclusive dealing and/or exclusive territories with their distributors. We employ a panel data set of manufacturing firms (from 1990 to 2005) that contains information about such vertical restraints. Firms report whether they impose vertical restraints on their distributors (retailers or wholesalers) and the type: resale price maintenance, full line forcing, exclusive dealing, and/or exclusive territories. Our results show that the likelihood of imposing exclusive dealing and/or exclusive territories varies widely by industry and that small size greatly reduces the likelihood of imposing exclusive dealing (but not exclusive territories). The results also show the existence of complementarities between exclusive territories and exclusive dealing but not with other types of restraint.vertical restraints, exclusive dealing, exclusive territories, manufacturing industry

A robust partial least squares method with applications

Partial least squares regression (PLS) is a linear regression technique developed to relate many regressors to one or several response variables. Robust methods are introduced to reduce or remove the effect of outlying data points. In this paper we show that if the sample covariance matrix is properly robustified further robustification of the linear regression steps of the PLS algorithm becomes unnecessary. The robust estimate of the covariance matrix is computed by searching for outliers in univariate projections of the data on a combination of random directions (Stahel-Donoho) and specific directions obtained by maximizing and minimizing the kurtosis coefficient of the projected data, as proposed by Peña and Prieto (2006). It is shown that this procedure is fast to apply and provides better results than other procedures proposed in the literature. Its performance is illustrated by Monte Carlo and by an example, where the algorithm is able to show features of the data which were undetected by previous methods