Highly accurate likelihood analysis for the seemingly unrelated regression problem

Fraser, D. A. S.; Rekkas, M.

doi:10.1016/j.jeconom.2004.06.001

Cited by 24 publications

(14 citation statements)

References 16 publications

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“…The first analysis of the SUR models appeared in Zellner (1962Zellner ( , 1963 who employed a generalized least squares approach. Later, likelihood and traditional Bayesian approaches have been developed, followed by various other inference approaches; see for e.g., the likelihood distributional approach (Fraser et al (2005)), Bayesian analyses and so on.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Variable Selection for the Seemingly Unrelated Regression Models with a Large Number of Predictors

Ando

2012

JJSS

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Computationally efficient methods for Bayesian analysis of Seemingly Unrelated Regression (SUR) models with a large number of predictors are developed. One of the most crucial problems in Bayesian modeling of SUR models is how to determine the optimal combination of predictors. In this paper, under a Bayesian hierarchical framework where each regression function is represented as a linear combination of a large number of basis functions, the regression coefficients, the variance matrix of the errors, and a set of predictors to be included in the model are estimated simultaneously. Usually the Bayesian model estimation problem is solved using Markov Chain Monte Carlo (MCMC) techniques. Herein we show how a direct Monte Carlo (DMC) technique can be employed to solve the variable selection and model parameter estimation problems more efficiently.

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Section: Introductionmentioning

confidence: 99%

Bayesian Variable Selection for the Seemingly Unrelated Regression Models with a Large Number of Predictors

Ando

2012

JJSS

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“…The accuracy of the different methods is evaluated by computing confidence intervals for in each case and using statistical criteria such as "central coverage, " that is, the proportion of the true parameter value falling within the confidence interval, "upper error, " that is, the proportion of the true parameter value falling above the upper limit of the confidence interval, "lower error" that is, the proportion of the true parameter value falling below the lower limit of the confidence interval, and "average bias, " that is, the average of the absolute difference between the upper and the lower probability errors and their nominal levels. These criteria are standard in the literature and have been considered, for example, by Fraser et al [18] and Chang and Wong [19]. The setup of the Monte Carlo simulation is the logistic growth model that is widely used in many applications (see [1,[20][21][22][23]).…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

Improved Inference for Moving Average Disturbances in Nonlinear Regression Models

Nguimkeu

2014

Journal of Probability and Statistics

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This paper proposes an improved likelihood-based method to test for first-order moving average in the disturbances of nonlinear regression models. The proposed method has a third-order distributional accuracy which makes it particularly attractive for inference in small sample sizes models. Compared to the commonly used first-order methods such as likelihood ratio and Wald tests which rely on large samples and asymptotic properties of the maximum likelihood estimation, the proposed method has remarkable accuracy. Monte Carlo simulations are provided to show how the proposed method outperforms the existing ones. Two empirical examples including a power regression model of aggregate consumption and a Gompertz growth model of mobile cellular usage in the US are presented to illustrate the implementation and usefulness of the proposed method in practice.

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“…See also Appendix A that discusses it further. A similar transformation was considered in Frasera et al (2005), McCulloch et al (2000) and Jacquier et al (2004). Note that the diagonal elements of Ω and Σ are different.…”

Section: Transformed Modelmentioning

confidence: 99%

“…The first analysis of the SUR model appeared in Zellner (1962Zellner ( , 1963 which employed a generalized least squares approach. Later, likelihood and traditional Bayesian approaches were developed followed by various other inference approaches; see, e.g., the likelihood distributional approach (Frasera et al, 2005), Bayesian analyses, the Bayesian method of moments, van der Merwe and Viljoen (1988) and so on.…”

Section: Introductionmentioning

confidence: 99%

A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model

Zellner

Ando

2010

Journal of Econometrics

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Highly accurate likelihood analysis for the seemingly unrelated regression problem

Cited by 24 publications

References 16 publications

Bayesian Variable Selection for the Seemingly Unrelated Regression Models with a Large Number of Predictors

Bayesian Variable Selection for the Seemingly Unrelated Regression Models with a Large Number of Predictors

Improved Inference for Moving Average Disturbances in Nonlinear Regression Models

A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model

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