Stock Market and Aggregate Investment Behavior in Malaysia: An Empirical Analysis

The present paper analyzes the role of stock market, more specifically real stock prices and stock market uncertainty/volatility, on aggregate investment behavior for an emerging market, Malaysia. Employing the autoregressive distributed lags approach to cointegration test, the paper establishes a long run equilibrium that ties the aggregate investment to its determinants—real income, real stock prices, real lending rate and stock market volatility. In the long run, we document a positive relation between aggregate investment and real stock prices and a negative relation between aggregate investment and stock market volatility. These results are further supported by our analyses of their dynamic interactions based on Granger causality and impulse-response functions. Based on the results, the real stock market prices, which has yet reached the level recorded prior to the crisis, may have explained the low investment in Malaysia after the Asian crisis. Moreover, the stock market volatility can also post a threat to the investment performance.     

Estimation from Burr type XII distribution using progressive first-failure censored data

In this paper, a new life test plan called a progressively first-failure-censoring scheme introduced by Wu and Ku? [On estimation based on progressive first-failure-censored sampling, Comput. Statist. Data Anal. 53(10) (2009), pp. 3659–3670] is considered. Based on this type of censoring, the maximum likelihood (ML) and Bayes estimates for some survival time parameters namely reliability and hazard functions, as well as the parameters of the Burr-XII distribution are obtained. The Bayes estimators relative to both the symmetric and asymmetric loss functions are discussed. We use the conjugate prior for the one-shape parameter and discrete prior for the other parameter. Exact and approximate confidence intervals with the exact confidence region for the two-shape parameters are derived. A numerical example using the real data set is provided to illustrate the proposed estimation methods developed here. The ML and the different Bayes estimates are compared via a Monte Carlo simulation study.     

On the estimation of reliabilty function in a Weibull lifetime distribution

The estimation of the reliability function of the Weibull lifetime model is considered in the presence of uncertain prior information (not in the form of prior distribution) on the parameter of interest. This information is assumed to be available in some sort of a realistic conjecture. In this article, we focus on how to combine sample and non-sample information together in order to achieve improved estimation performance. Three classes of point estimatiors, namely, the unrestricted estimator, the shrinkage estimator and shrinkage preliminary test estimator (SPTE) are proposed. Their asymptotic biases and mean-squared errors are derived and compared. The relative dominance picture of the estimators is presented. Interestingly, the proposed SPTE dominates the unrestricted estimator in a range that is wider than that of the usual preliminary test estimator. A small-scale simulation experiment is used to examine the small sample properties of the proposed estimators. Our simulation investigations have provided strong evidence that corroborates with asymptotic theory. The suggested estimation methods are applied to a published data set to illustrate the performance of the estimators in a real-life situation.     

Exploring the effects of workplace support on work-life experience: a study of Malaysia

To date most studies of workplace support and work-life experience have been of contexts where government policies and legislation to protect employee work-life balance interests are well established, such as US, UK, and other European countries. Little scrutiny has been given to these issues in less developed economies, where support and protection in terms of work-life policies and legislation at the national level is rather limited. Malaysia, the setting of this study, provides such a context. Two types of organizational support, work flexibility and superior support, are studied for their impact on work-life experience of Malaysians. The findings of this study are based on a national survey of working adults in Malaysia. Work-life experience is conceptualized to capture conflict and enrichment aspects, as well as bidirectional effects between work and nonwork. Results show that work flexibility and superior support lower work-life conflict marginally but facilitate greater work-life enrichment among Malaysians. The paper also draws out implications of these findings for human resource development professionals operating in workplace settings within national contexts with limited mandatory work-life provisions.     

Penalized and Shrinkage Estimation in the Cox Proportional Hazards Model

This article considers the shrinkage estimation procedure in the Cox's proportional hazards regression model when it is suspected that some of the parameters may be restricted to a subspace. We have developed the statistical properties of the shrinkage estimators including asymptotic distributional biases and risks. The shrinkage estimators have much higher relative efficiency than the classical estimator, furthermore, we consider two penalty estimators—the LASSO and adaptive LASSO—and compare their relative performance with that of the shrinkage estimators numerically. A Monte Carlo simulation experiment is conducted for different combinations of irrelevant predictors and the performance of each estimator is evaluated in terms of simulated mean squared error. Simulation study shows that the shrinkage estimators are comparable to the penalty estimators when the number of irrelevant predictors in the model is relatively large. The shrinkage and penalty methods are applied to two real data sets to illustrate the usefulness of the procedures in practice.     

Inference under Heteroscedasticity of Unknown Form Using an Adaptive Estimator

The heteroscedasticity consistent covariance matrix estimators are commonly used for the testing of regression coefficients when error terms of regression model are heteroscedastic. These estimators are based on the residuals obtained from the method of ordinary least squares and this method yields inefficient estimators in the presence of heteroscedasticity. It is usual practice to use estimated weighted least squares method or some adaptive methods to find efficient estimates of the regression parameters when the form of heteroscedasticity is unknown. But HCCM estimators are seldom derived from such efficient estimators for testing purposes in the available literature. The current article addresses the same concern and presents the weighted versions of HCCM estimators. Our numerical work uncovers the performance of these estimators and their finite sample properties in terms of interval estimation and null rejection rate.     

Probabilistic Properties of Parametric Dual and Inverse Time Series Models Generated by ARMA Models

For the class of autoregressive-moving average (ARMA) processes, we examine the relationship between the dual and the inverse processes. It is demonstrated that the inverse process generated by a causal and invertible ARMA (p, q) process is a causal and invertible ARMA (q, p) model. Moreover, it is established that this representation is strong if and only if the generating process is Gaussian. More precisely, it is derived that the linear innovation process of the inverse process is an all-pass model. Some examples and applications to time reversibility are given to illustrate the obtained results.     

Stabilizing the Performance of Kurtosis Estimator of Multivariate Data

The estimation of the kurtosis parameter of the underlying distribution plays a central role in many statistical applications. The central theme of the article is to improve the estimation of the kurtosis parameter using a priori information. More specifically, we consider the problem of estimating kurtosis parameter of a multivariate population when some prior information regarding the the parameter is available. The rationale is that the sample estimator of the kurtosis parameter has a large estimation error. In this situation we consider shrinkage and pretest estimation methodologies and reappraise their statistical properties. The estimation based on these strategies yield relatively smaller estimation error in comparison with the sample estimator in the candidate subspace. A large sample theory of the suggested estimators are developed and compared. The results demonstrate that suggested estimators outperform the estimator based on the sample data only in the candidate subspace. In an effort to appreciate the relative behavior of the estimators in a finite sample scenario, a Monte-carlo simulation study is planned and performed. The result of simulation study strongly corroborates the asymptotic result. To illustrate the application of the estimators, some example are showcased based on recently published data.     

纪念苏丹商人穆罕默德·哈吉在华首设商务处

中国与阿拉伯非洲的历史交往源远流长,特别是与苏丹的联系,可以追溯到西汉时期,而且从那时起双方之间的往来一直延续不断.本文在历史回顾的基础上,通过实地调查和口传资料,着重考察了19世纪末苏丹商人穆罕默德·哈吉在广州首设商务办事处并开展苏(丹)中(国)贸易、加强双方社会联系的历史活动,揭示了苏中悠久关系史上鲜为人知的一页.     

An Insurance Model for Risk Management of Process Facilities

Most existing risk management models for process industries do not consider the effect of insurance coverage, which results in an overestimation of overall risk. A model is presented in this article to study the effect of insurance coverage of health, safety, environmental, and business risks. The effect of insurance recovery is modeled through the application of adjustment factors by considering the stochastic factors affecting insurance recovery. The insurance contract's conditions, deductibles, and policy limits are considered in developing the insurance recovery adjustment factors. Copula functions and Monte Carlo simulations are used to develop the distribution of the aggregate loss by considering the dependence among loss classes. A case study is used to demonstrate both the practical application of the proposed insurance model to improve management decisions, and the mitigating effect of insurance to minimize the residual risk.