Seemingly unrelated regression models
نویسندگان
چکیده
منابع مشابه
Bayesian Geoadditive Seemingly Unrelated Regression
Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated, so that separate univariate analysis may result in inefficient estimates of covariate effects. A weakness of parametric models is that they require strong assumptions on the functional form of possibly nonlinear effects of metrical covaria...
متن کاملEfficient Semiparametric Seemingly Unrelated Quantile Regression Estimation
We propose an efficient semiparametric estimator for the coefficients of a multivariate linear regression model — with a conditional quantile restriction for each equation — in which the conditional distributions of errors given regressors are unknown. The procedure can be used to estimate multiple conditional quantiles of the same regression relationship. The proposed estimator is asymptotical...
متن کاملBayesian Geoadditive Seemingly Unrelated Regression 1
Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated, so that separate univariate analysis may result in inefficient estimates of covariate effects. A weakness of parametric models is that they require strong assumptions on the functional form of possibly nonlinear effects of metrical covaria...
متن کاملSparse Seemingly Unrelated Regression Modelling: Applications in Econometrics and Finance
We present a sparse seemingly unrelated regression (SSUR) model to generate substantively relevant structures in the high-dimensional distributions of seemingly unrelated model (SUR) parameters. This SSUR framework includes prior specifications, posterior computations using Markov chain Monte Carlo methods, evaluations of model uncertainty, and model structure searches. Extensions of the SSUR m...
متن کاملHighly accurate likelihood analysis for the seemingly unrelated regression problem
The linear and nonlinear seemingly unrelated regression problem with general error distribution is analyzed using recent likelihood theory that arguably provides the definitive distribution for assessing a scalar parameter; this involves implicit but well defined conditioning and marginalization for determining intrinsic measures of departure. Highly accurate p-values are obtained for the key d...
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ژورنال
عنوان ژورنال: Applications of Mathematics
سال: 2013
ISSN: 0862-7940,1572-9109
DOI: 10.1007/s10492-013-0005-7