董事会规模、独立性与会计信息透明度——来自中国资本市场的经验证据

本文利用581家中国上市公司数据,实证检验了会计信息透明度对董事会规模和独立性的影响。研究结果表明,会计信息透明度是公司的一个独特特征,董事会规模与会计信息透明度显著正相关,外部董事比例与会计信息透明度没有显著相关性。而在非国有控股公司中,会计信息透明度对董事会规模没有显著影响。这些证据表明,只有在国有控股公司中,会计信息透明度才能具有治理作用,影响董事会规模。     

由赴港生子到生育公地悲剧的认识

赴港生子是近年来中国大陆出现的新的人口现象,且多是能领社会风习之先的富人所为,其中必具诸多预示性内涵。文章通过现象解析、模型推演,证明在存在性别偏好的条件下,孩子的数量质量替代率下降;放宽约束条件后,富人更具多生的潜能;进而对理性家庭最优生育选择必将导致的生育公地悲剧展开理论探讨;并佐以浙江省近年来人口发展资料,揭示出在目前我国人口发展新情境中,部分富人,尤其是民营企业主的生育意愿有被激活且渐行释放的迹象,经济发达地区的生育率似有持续走高之势,生育率反弹的势能似在不断增强。藉此,应该继续维持现有政策执行力,增强新型生育文化的引致力。     

我国家庭动态变化对二氧化碳排放的影响分析

人口数量及其增长率常常被认为是二氧化碳排放增长的重要推动力之一,而家庭变动对二氧化碳排放的影响往往被忽视。基于此,采用VAR模型,通过脉冲响应函数来考察人口和家庭变动对二氧化碳排放的动态影响,并用方差分解法揭示其相互影响程度及差异。结果表明,家庭层面变量对二氧化碳排放的影响远大于人口总量对二氧化碳排放的影响。考察人口总量、平均家庭规模以及家庭户变动三者对二氧化碳排放影响的贡献差异发现,平均家庭规模对二氧化碳排放的影响大于家庭户变动对二氧化碳排放的影响,且大于人口总量对二氧化碳排放的影响。相对于人口总量,以家庭户为视角来研究人口因素对二氧化碳的影响更为重要。因此,在节能减排的政策建议中,应当更多倡导推广有利于可持续发展的家庭户模式,家庭减排对于减排目标的实现将具有更大的潜力。     

Asymmetric Multivariate Stochastic Volatility

This paper proposes and analyses two types of asymmetric multivariate stochastic volatility (SV) models, namely, (i) the SV with leverage (SV-L) model, which is based on the negative correlation between the innovations in the returns and volatility, and (ii) the SV with leverage and size effect (SV-LSE) model, which is based on the signs and magnitude of the returns. The paper derives the state space form for the logarithm of the squared returns, which follow the multivariate SV-L model, and develops estimation methods for the multivariate SV-L and SV-LSE models based on the Monte Carlo likelihood (MCL) approach. The empirical results show that the multivariate SV-LSE model fits the bivariate and trivariate returns of the S&P 500, the Nikkei 225, and the Hang Seng indexes with respect to AIC and BIC more accurately than does the multivariate SV-L model. Moreover, the empirical results suggest that the univariate models should be rejected in favor of their bivariate and trivariate counterparts.     

Tests for Serial Dependence and Other Specification Analysis in Models of Markets in Disequilibrium

The assumption of serial independence of disturbances is the starting point of most of the work done on analyzing market disequilibrium models. We derive tests for serial dependence given normality and homoscedasticity using the Lagrange multiplier (LM) test principle. Although the likelihood function under serial dependence is very complicated and involves multiple integrals of dimensions equal to the sample size, the test statistic we obtain through the LM principle is very simple. We apply the test to the housing-start data of Fair and Jaffee (1972) and study its finite sample properties through simulation. The test seems to perform quite well in finite samples in terms of size and power. We present an analysis of disequilibrium models that assumes that the disturbances are logistic rather than normal. The relative performances of these distributions are investigated by simulation.     

The Disappointing Properties of GLS-Based Unit Root Tests in the Presence of Structural Breaks

Abstract

It is well known that prior application of GLS detrending, as advocated by Elliot et al. [Elliot, G., Rothenberg, T., Stock, J. (1996). Efficient tests for an autoregressive unit root. Econometrica 64:813–836], can produce a significant increase in power to reject the unit root null over that obtained from a conventional OLS-based Dickey and Fuller [Dickey, D., Fuller, W. (1979). Distribution of the estimators for autoregressive time series with a unit root. J. Am. Statist. Assoc. 74:427–431] testing equation. However, this paper employs Monte Carlo simulation to demonstrate that this increase in power is not necessarily obtained when breaks occur in either level or trend. It is found that neither OLS nor GLS-based tests are robust to level or trend breaks, their size and power properties both deteriorating as the break size increases.     

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.     

Granger Causality Test in the Presence of Spillover Effects

In this article, we investigate the effect of spillover (i.e., causality in variance) on the reliability of Granger causality test based on ordinary least square estimates. We studied eight different versions of the test both, with and without Whites heteroskedasticity consistent covariance matrix (HCCME). The properties of the tests are investigated by means of a Monte Carlo experiment where 21 different data generating processes (DGP) are used and a number of factors that might affect the test are varied. The result shows that the best choice to test for Granger causality under the presence of spillover is the Lagrange Multiplier test with HCCME.     

Improved estimator of population total in PPS sampling

In this paper, we have considered an estimation of the population total Y of the study variable y, making use of information on an auxiliary variable x. A class of estimators for the population total Y using transformation on both the variables study as well as auxiliary has been suggested based on the probability proportional to size with replacement (PPSWR). In addition to many the usual PPS estimator, Reddy and Rao's (1977) estimator and Srivenkataramana and Tracy's (1979, 1984, 1986) estimators are shown to be members of the proposed class of estimators. The variance of the proposed class of estimators has been obtained. In particular, the properties of 75 estimators based on different known population parameters of the study as well as auxiliary variables have been derived from the proposed class of estimators. In support of the present study, numerical illustrations are given.     

Do synergies exist in related acquisitions? A meta-analysis of acquisition studies

Mergers and acquisitions (M&A) aim to increase the wealth of shareholders of the acquiring company, in particular by creating synergies. It is often assumed that relatedness is a source of synergies. Our study distinguishes between business, cultural, technological, and size relatedness. It discusses the reasons why these different forms of relatedness can lead to an acquisition success and we conduct a meta-analysis of 67 prior M&A studies. Results indicate that positive effects can be expected under specific conditions only and have a limited overall impact on acquisition success. A moderator analysis finds that synergies stemming from relatedness depend on industry-, country-, and investor-characteristics.