The Experts below are selected from a list of 36 Experts worldwide ranked by ideXlab platform

Tsunemasa Shiba - One of the best experts on this subject based on the ideXlab platform.

  • Dirichlet Prior for Estimating Unknown Regression Error Heteroskedasticity
    2015
    Co-Authors: Hiroaki Chigira, Tsunemasa Shiba
    Abstract:

    We propose a Bayesian procedure to estimate heteroskedastic variances of the Regression Error Term ƒO, when the form of heteroskedasticity is unknown. The prior information on ƒO is based on a Dirichlet distribution, and in the Markov Chain Monte Carlo sampling, its proposal density parameters' information is elicited from the well-known Eicker-White Heteroskedasticity Consistent Variance-Covariance Matrix Estimator. We present an emprical example to show that our scheme works.

  • Bayesian Estimation of Unknown Regression Error Heteroscedasticity
    2007
    Co-Authors: Hiroaki Chigira, Tsunemasa Shiba
    Abstract:

    We propose a Bayesian procedure to estimate heteroscedastic variances of the Regression Error Term, when the form of heteroscedasticity is unknown. We use prior information that is elicited from the well-known Eicker-White Heteroscedasticity Consistent Variance- CovarianceMatrix Estimator, and then useMarkov ChainMonte Carlo algorithm to simulate posterior pdf's of the unknown heteroscedastic variances. In addition to numerical examples, we present an empirical investigation of the stock prices of Japanese pharmaceutical and biomedical companies.

  • Bayesian Estimation of Unknown Heteroscedastic Variances
    2006
    Co-Authors: Hiroaki Chigira, Tsunemasa Shiba
    Abstract:

    We propose a Bayesian procedure to estimate possibly heteroscedastic variances of the Regression Error Term, without assuming any structure on them. What we propose in this paper, may be construed as a Conditional Bayesian procedure that is conditioned upon the HCCM obtained from the OLS estimation of the original Regression model. After we obtain the Eicker-White HCCM, we set up a Bayesian model and use an MCMC to simulate posterior pdf's of heteroscedastic variances whose structures are unknown. In addition to the numerical examples, we present an empirical investigation on the stock prices of Japanese pharmaceutical and biomedical companies.

Hiroaki Chigira - One of the best experts on this subject based on the ideXlab platform.

  • Dirichlet Prior for Estimating Unknown Regression Error Heteroskedasticity
    2015
    Co-Authors: Hiroaki Chigira, Tsunemasa Shiba
    Abstract:

    We propose a Bayesian procedure to estimate heteroskedastic variances of the Regression Error Term ƒO, when the form of heteroskedasticity is unknown. The prior information on ƒO is based on a Dirichlet distribution, and in the Markov Chain Monte Carlo sampling, its proposal density parameters' information is elicited from the well-known Eicker-White Heteroskedasticity Consistent Variance-Covariance Matrix Estimator. We present an emprical example to show that our scheme works.

  • Bayesian Estimation of Unknown Regression Error Heteroscedasticity
    2007
    Co-Authors: Hiroaki Chigira, Tsunemasa Shiba
    Abstract:

    We propose a Bayesian procedure to estimate heteroscedastic variances of the Regression Error Term, when the form of heteroscedasticity is unknown. We use prior information that is elicited from the well-known Eicker-White Heteroscedasticity Consistent Variance- CovarianceMatrix Estimator, and then useMarkov ChainMonte Carlo algorithm to simulate posterior pdf's of the unknown heteroscedastic variances. In addition to numerical examples, we present an empirical investigation of the stock prices of Japanese pharmaceutical and biomedical companies.

  • Bayesian Estimation of Unknown Heteroscedastic Variances
    2006
    Co-Authors: Hiroaki Chigira, Tsunemasa Shiba
    Abstract:

    We propose a Bayesian procedure to estimate possibly heteroscedastic variances of the Regression Error Term, without assuming any structure on them. What we propose in this paper, may be construed as a Conditional Bayesian procedure that is conditioned upon the HCCM obtained from the OLS estimation of the original Regression model. After we obtain the Eicker-White HCCM, we set up a Bayesian model and use an MCMC to simulate posterior pdf's of heteroscedastic variances whose structures are unknown. In addition to the numerical examples, we present an empirical investigation on the stock prices of Japanese pharmaceutical and biomedical companies.

Rawane Samb - One of the best experts on this subject based on the ideXlab platform.

  • Contribution to the Nonparametric Estimation of the Density of the Regression Errors (Doctoral Thesis)
    arXiv: Statistics Theory, 2010
    Co-Authors: Rawane Samb
    Abstract:

    This thesis deals with the nonparametric estimation of density f of the Regression Error Term E of the model Y=m(X)+E, assuming its independence with the covariate X. The difficulty linked to this study is the fact that the Regression Error E is not observed. In a such setup, it would be unwise, for estimating f, to use a conditional approach based upon the probability distribution function of Y given X. Indeed, this approach is affected by the curse of dimensionality, so that the resulting estimator of the residual Term E would have considerably a slow rate of convergence if the dimension of X is very high. Two approaches are proposed in this thesis to avoid the curse of dimensionality. The first approach uses the estimated residuals, while the second integrates a nonparametric conditional density estimator of Y given X. If proceeding so can circumvent the curse of dimensionality, a challenging issue is to evaluate the impact of the estimated residuals on the final estimator of the density f. We will also attempt to deTermine the pointwise rate of convergence of our proposed estimators. One our main aims is to characterize the optimal choices of the first and second step bandwidths used for estimating m and f respectively.

  • nonparametric kernel estimation of the probability density function of Regression Errors using estimated residuals
    arXiv: Statistics Theory, 2010
    Co-Authors: Rawane Samb
    Abstract:

    This paper deals with the nonparametric density estimation of the Regression Error Term assuming its independence with the covariate. The difference between the feasible estimator which uses the estimated residuals and the unfeasible one using the true residuals is studied. An optimal choice of the bandwidth used to estimate the residuals is given. We also study the asymptotic normality of the feasible kernel estimator and its rate-optimality.

Andriy Norets - One of the best experts on this subject based on the ideXlab platform.

  • Bayesian Regression with nonparametric heteroskedasticity
    Journal of Econometrics, 2015
    Co-Authors: Andriy Norets
    Abstract:

    This paper studies large sample properties of a semiparametric Bayesian approach to inference in a linear Regression model. The approach is to model the distribution of the Regression Error Term by a normal distribution with the variance that is a flexible function of covariates. The main result of the paper is a semiparametric Bernstein–von Mises theorem under misspecification: even when the distribution of the Regression Error Term is not normal, the posterior distribution of the properly recentered and rescaled Regression coefficients converges to a normal distribution with the zero mean and the variance equal to the semiparametric efficiency bound.

Steffen Habermalz - One of the best experts on this subject based on the ideXlab platform.

  • Teaching Causal Inference in Undergraduate Econometrics
    SSRN Electronic Journal, 2010
    Co-Authors: Steffen Habermalz
    Abstract:

    This paper argues that the current way in which the undergraduate introductory econometrics course is taught is neither inline with current empirical practice nor very intuitive. It proposes a shift in focus of the course on causal inference using the Roy-Rubin Causal Model (RRCM). A second theme of the paper is the suggestion to use random regressors from the start to improve the ability of students to intuitively relate to the Regression model and to enable the teacher to present many Regression pitfalls under the umbrella of the non-zero covariance between regressors and the Regression Error Term. Finally, the paper discusses how to make room for the new material and suggests ways of dealing with the added complexity of the approach.