We propose a novel Bayesian model framework for discrete ordinal and count data based on conditional transformations of the responses. The transformation function is estimated from in conjunction with an priori chosen reference distribution. For responses, resulting sense that it fully parametric yet distribution-free approach can additionally account excess zeros additive specifications. categ...

Abstract Conditionally specified models are often used to describe complex multivariate data. Such assume implicit structures on the extremes. So far, no methodology exists for calculating extremal characteristics of conditional since copula and marginals not expressed in closed forms. We consider bivariate that specify distribution X Y . provide tools quantify assumptions extremes this class m...

Shape restrictions have played a central role in economics as both testable implications of theory and sufficient conditions for obtaining informative counterfactual predictions. In this paper, we provide general procedure inference under shape identified partially models defined by conditional moment restrictions. Our test statistics proposed methods are based on the minimum generalized method...

in this paper, in order to optimize the portfolio consisting of selected industrial stocks of petroleum products, automobiles and parts, electrical industry and extraction of minerals from tehran stock exchange member, first, time – varying conditional covariance matrix has been estimated based on the following multivariate garch models: diagonal-vech (1,1), ccc (1,1) and diagonal -bekk (1,1). ...