Spatial Weighting Matrix Selection in Spatial Lag Econometric Model

This paper investigates the choice of spatial weighting matrix in a spatial lag model framework. In the empirical literature the choice of spatial weighting matrix has been characterized by a great deal of arbitrariness. The number of possible spatial weighting matrices is large, which until recently was considered to prevent investigation into the appropriateness of the empirical choices. Recently Kostov (2010) proposed a new approach that transforms the problem into an equivalent variable selection problem. This article expands the latter transformation approach into a two-step selection procedure. The proposed approach aims at reducing the arbitrariness in the selection of spatial weighting matrix in spatial econometrics. This allows for a wide range of variable selection methods to be applied to the high dimensional problem of selection of spatial weighting matrix. The suggested approach consists of a screening step that reduces the number of candidate spatial weighting matrices followed by an estimation step selecting the final model. An empirical application of the proposed methodology is presented. In the latter a range of different combinations of screening and estimation methods are employed and found to produce similar results. The proposed methodology is shown to be able to approximate and provide indications to what the ‘true’ spatial weighting matrix could be even when it is not amongst the considered alternatives. The similarity in results obtained using different methods suggests that their relative computational costs could be primary reasons for their choice. Some further extensions and applications are also discussed

Searching for New Leads to Treat Epilepsy: Target-Based Virtual Screening for the Discovery of Anticonvulsant Agents

The purpose of this investigation is to contribute to the development of new anticonvulsant drugs to treat patients with refractory epilepsy. We applied a virtual screening protocol that involved the search into molecular databases of new compounds and known drugs to find small molecules that interact with the open conformation of the Nav1.2 pore. As the 3D structure of human Nav1.2 is not available, we first assembled 3D models of the target, in closed and open conformations. After the virtual screening, the resulting candidates were submitted to a second virtual filter, to find compounds with better chances of being effective for the treatment of P-glycoprotein (P-gp) mediated resistant epilepsy. Again, we built a model of the 3D structure of human P-gp, and we validated the docking methodology selected to propose the best candidates, which were experimentally tested on Nav1.2 channels by patch clamp techniques and in vivo by the maximal electroshock seizure (MES) test. Patch clamp studies allowed us to corroborate that our candidates, drugs used for the treatment of other pathologies like Ciprofloxacin, Losartan, and Valsartan, exhibit inhibitory effects on Nav1.2 channel activity. Additionally, a compound synthesized in our lab, N,N′-diphenethylsulfamide, interacts with the target and also triggers significant Na1.2 channel inhibitory action. Finally, in vivo studies confirmed the anticonvulsant action of Valsartan, Ciprofloxacin, and N,N′-diphenethylsulfamide.Fil: Palestro, Pablo Hernán. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Enrique, Nicolás Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Estudios Inmunológicos y Fisiopatológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Estudios Inmunológicos y Fisiopatológicos; ArgentinaFil: Goicoechea, Sofia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; ArgentinaFil: Villalba, Maria Luisa. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sabatier, Laureano Leonel. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Martín, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Estudios Inmunológicos y Fisiopatológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Estudios Inmunológicos y Fisiopatológicos; ArgentinaFil: Milesi, Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Estudios Inmunológicos y Fisiopatológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Estudios Inmunológicos y Fisiopatológicos; ArgentinaFil: Bruno Blanch, Luis Enrique. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gavernet, Luciana. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Choosing the Right Spatial Weighting Matrix in a Quantile Regression Model

This paper proposes computationally tractable methods for selecting the appropriate spatial weighting matrix in the context of a spatial quantile regression model. This selection is a notoriously difficult problem even in linear spatial models and is even more difficult in a quantile regression setup. The proposal is illustrated by an empirical example and manages to produce tractable models. One important feature of the proposed methodology is that by allowing different degrees and forms of spatial dependence across quantiles it further relaxes the usual quantile restriction attributable to the linear quantile regression. In this way we can obtain a more robust, with regard to potential functional misspecification, model, but nevertheless preserve the parametric rate of convergence and the established inferential apparatus associated with the linear quantile regression approach

Optimal Procurement Contracts with Pre–Project Planning

The paper studies procurement contracts with pre–project investigations in the presence of adverse selection and moral hazard. To model the procurer’s roblem, we extend a standard sequential screening model to endogenous information acquisition with moral hazard. The optimal contract displays systematic distortions in information acquisition. Due to a rent effect, adverse selection induces too much information acquisition to prevent cost overruns and too little information acquisition to prevent false project cancelations. Moral hazard mitigates the distortions related to cost overruns yet exacerbates those related to false negatives. The optimal mechanism is a menu of option contracts that achieves the dual goal of providing incentives for information acquisition and truthful information revelation

High-dimensional variable selection

This paper explores the following question: what kind of statistical guarantees can be given when doing variable selection in high-dimensional models? In particular, we look at the error rates and power of some multi-stage regression methods. In the first stage we fit a set of candidate models. In the second stage we select one model by cross-validation. In the third stage we use hypothesis testing to eliminate some variables. We refer to the first two stages as "screening" and the last stage as "cleaning." We consider three screening methods: the lasso, marginal regression, and forward stepwise regression. Our method gives consistent variable selection under certain conditions.Comment: Published in at http://dx.doi.org/10.1214/08-AOS646 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org