We propose a Bayesian procedure to estimate heteroscedastic variances of the regression error term ω, when the form of heteroscedasticity is unknown. The prior information on ω is based on a Dirichlet distribution, and in the Markov Chain Monte Carlo sampling, its proposal density...

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...

It is widely recognized that taking cointegration relationships into consideration is useful in forecasting cointegrated processes. However, there are a few practical problems when forecasting large cointegrated processes using the well-known vector error correction model. First, it is hard to...

This paper is concerned with the estimation of the autoregressive parameter of dynamic panel data models. We propose a bias-corrected GMM estimator whose bias is smaller than that of many existing GMM estimators. And we propose a small sample corrected estimator of the variance in order to...

We propose a Bayesian procedure to estimate heteroscedastic variances of the regression error term ƒÖ, when the form of heteroscedasticity is unknown. The prior information on ƒÖ is elicited from the wellknown Eicker-White Heteroscedasticity Consistent Variance-Covariance Matrix Estimator....

In general, Wald tests for the Granger non-causality in vector autoregressive (VAR) process are known to have non-standard asymptotic properties for cointegrated systems. However, that may have standard asymptotic properties depending on the rank of the submatrix of cointegration. In this paper,...

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...

This paper considers a test of the rank of cointegration. The test is based on the fact that in an m-variate system the m-r th principal component is I (1) under the null of r cointegration rank but I (0) under the alternative of r+1 cointegration rank. Exploiting this fact, we construct a...

It is widely believed that taking cointegration and integration into consideration is useful in constructing long-term forecasts for cointegrated processes. This paper shows that imposing neither cointegration nor integration leads to superior long-term forecasts.