The Asymptotic Covariance Matrix of the Least Squares Estimator in the Stochastic Linear Regression Model: The Case of Elliptically Symmetric Distribution

This article considers the unconditional asymptotic covariance matrix of the least squares estimator in the linear regression model with stochastic explanatory variables. The asymptotic covariance matrix of the least squares estimator of regression parameters is evaluated relative to the standard asymptotic covariance matrix when the joint distribution of the dependent and explanatory variables is in the class of elliptically symmetric distributions. An empirical example using financial data is presented. Numerical examples and simulation experiments are given to illustrate the difference of the two asymptotic covariance matrices.     

On the Properties of the Likelihood Function of Spanos' Conditional t Heteroskedastic Model

This article investigates the properties of the likelihood function of Spanos’ conditional t heteroskedastic model (Spanos, 1994 Spanos , A. ( 1994 ). On modeling heteroskedasticity: The student's t and elliptical linear regression models . Econometric Theor. 10 : 286 – 315 .[Crossref], [Web of Science ®] , [Google Scholar]) On modeling heteroskedasticity: the student's t and elliptical linear regression models. It is shown that estimability of the degrees of freedom of t distribution and the block-diagonality of the information matrix of the joint likelihood function with respect to conditional mean parameters and remaining parameters hold for the model. The joint maximum likelihood estimator and its inference based on the t-statistic and χ²-statistic are examined in finite samples by simulation when the degrees of freedom is known and unknown.     

The Effect of Estimation of Unknown Degrees of Freedom on Estimation of Remaining Parameters in Spanos’ Conditional t Heteroskedastic Model

This article investigates the effect of estimation of unknown degrees of freedom on efficient estimation of remaining parameters in Spanos’ conditional t heteroskedastic model. We compare by simulation three maximum likelihood estimators (MLEs) of the remaining parameters in the model: the MLE of the remaining parameters when all the parameters are estimated by the MLE, when the degrees of freedom is estimated by a method of moments estimator, and when the degrees of freedom is erroneously specified. The latter two methods are found to perform poorly compared to the former method for the inference of variance parameters in the model. Thus, efficient estimation of degrees of freedom by the MLE is important to estimate efficiently the remaining variance parameters.     

Trust and reciprocity among international groups: Experimental evidence from Austria and Japan

This paper explores national identity in trust and reciprocity at the intra- and international levels by adopting a modified trust game played among groups from Austria and Japan, wherein subjects play the roles of trustor and trustee consecutively without any information feedback. Although the intranational trust levels in both countries are identical, the international trust for Japanese groups is less than that of Austrian groups. On the other hand, the international reciprocity for Japanese groups is greater than that of Austrian groups. Additionally, the Japanese reciprocation level toward Austrians is higher than that toward Japanese.     

Gains by the common structural variance

Consider a linear simultaneous equations model with a structural change. We present gains by the common structural variance in identification and asymptotic efficiency of the limited information maximum likelihood (LIML) estimator of structural coefficients in two regimes. The gains are obtained by comparing two LIML estimators derived with and without the common structural variance restriction.     

The Finite-Sample Performance of White's Test for Heteroskedasticity Under Stochastic Regressors

We study the finite-sample properties of White's test for heteroskedasticity in stochastic regression models where explanatory variables are random and not given. We investigate by simulation the effect of non independence of explanatory variables and error term and heteroskedasticity on White's test. A standard bootstrap method in the computationally convenient form is found to work well with respect to the size and power.     

Clinical trial of treatment with saikokaryukotsuboreito for eugonadal patients with late-inset hypogonadism-related symptoms

The purpose of this study was to evaluate the efficacy and safety of saikokaryukotsuboreito (SKRBT), which is widely used for a variety of clinical conditions, neuropsychiatric disorders, for patients with LOH-related symptoms. Twenty-two eugonadal patients over 40 years of age with LOH-related symptoms were included in this study. SKRBT was given orally to these patients three times daily to a total of 7.5 g/day for more than two months. Laboratory and endocrinological profiles were reviewed, and LOH symptoms were judged by means of several health assessment instruments such as the Aging Males' Symptoms (AMS) scale, Self-rated Depression Scale (SDS), International Prostate Symptom Score (IPSS), and King's Health Questionnaire (KHQ). Total AMS scores and AMS subscores were significantly decreased after the treatment. The KHQ, general health perception and impact on life scores were also significantly decreased, although no significant improvement was observed in other KHQ factors or the SDS score or IPSS. The serum concentrations of testosterone fractions did not change with treatment. Laboratory values did not change, and no adverse effects were identified after treatment. We conclude that SKRBT may be considered for treatment of patients with LOH-related symptoms for eugonadal patients.     

A Simulation Study of White's Test for Heteroskedasticity in Fixed and Stochastic Regression Models

We study the finite-sample properties of White's test for heteroskedasticity in fixed and stochastic regression models. We compare by simulation White and bootstrap methods when the underlying distribution is symmetric as well as asymmetric. The superior performance of the bootstrap method in small samples does not hold when the underlying distribution is asymmetric.