论企业价值最大化目标下的收益分配

企业财务管理的目标在于实现企业价值最大化,实现这一目标在于科学、合理的分配收益:兼顾消费与 积累的关系、确保息税按期足额支付、前置人力资本的收益分配、优化现金股利和股票股利的结构。     

Instrumental variable estimation based on grouped data

The paper considers the estimation of the coefficients of a single equation in the presence of dummy intruments. We derive pseudo ML and GMM estimators based on moment restrictions induced either by the structural form or by the reduced form of the model. The performance of the estimators is evaluated for the non-Gaussian case. We allow for heteroscedasticity. The asymptotic distributions are based on parameter sequences where the number of instruments increases at the same rate as the sample size. Relaxing the usual Gaussian assumption is shown to affect the normal asymptotic distributions. As a result also recently suggested new specification tests for the validity of instruments depend on Gaussianity. Monte Carlo simulations confirm the accuracy of the asymptotic approach.     

最大持续产量原理在可持续发展实践中的应用

文章指出,实施可持续发展要以生态学原理为指导,其中较重要的就是最大持续产量原理。最大持续产量原理在农业、林业、渔业、有害生物防治、生物资源保护等方面有着广泛的应用。     

Relative performance of the grid, nearest neighbor, and OLS estimators

This article provides various paradigms for the grid estimator, the most useful being a representation of the grid estimator as a combination of the nonparametric nearest neighbor estimator and a parametric estimator. Hence, the grid estimator falls into the class of semiparametric estimators. The article used this representation to derive the relative efficiency of the nearest neighbor, grid, and OLS estimators. Under statistically perfect conditions, the OLS estimator dominated the grid estimator, which in turn dominated the nearest neighbor estimator. A Monte Carlo experiment verified the theoretical results. A second Monte Carlo experiment showed the fragility of the OLS superiority to misspecification. The results cast light upon appraisal practice.     

Exploring the Accuracy of Joint-Distribution Approximations Given Partial Information

Abstract

We test the accuracy of various methods for approximating underspecified joint probability distributions. In particular, we examine the maximum entropy and the analytic center approximations, and we introduce three methods for approximating a discrete joint probability distribution given partial probabilistic information. Our results suggest that recently proposed approximations and our new approximations more accurately represent the possible uncertainty models than do previous models such as maximum entropy.     

Testing conditional factor models

Using nonparametric techniques, we develop a methodology for estimating and testing conditional alphas and betas and long-run alphas and betas, which are the averages of conditional alphas and betas, respectively, across time. The estimators and tests can be implemented for a single asset or jointly across portfolios. The traditional Gibbons, Ross, and Shanken (1989) test arises as a special case of no time variation in the alphas and factor loadings and homoskedasticity. As applications of the methodology, we estimate conditional CAPM and multifactor models on book-to-market and momentum decile portfolios. We reject the null that long-run alphas are equal to zero even though there is substantial variation in the conditional factor loadings of these portfolios.     

Fear of Floating and Pegging: A Simultaneous Choice Model of De Jure and De Facto Exchange Rate Regimes in Developing Countries

We present an analysis of the determinants of de jure and de facto exchange rate regimes based on a panel probit model with simultaneous equations. The model is estimated using simulation-based maximum likelihood methods. The empirical results suggest a triangular structure of the model such that the choice of de facto regimes depends on the choice of de jure regimes but not vice versa. This gives rise to a novel interpretation of regime discrepancies.

Conditional Effects of Touch on Purchase Behavior

Interpersonal touch has been shown to affect consumer behaviors such as compliance with a request, impulse buying, and tipping behavior. In this study, we examine if the impact of touch on purchase behavior is gender specific, and if it depends on how much the individual likes the product. Findings indicate male consumers are more likely to purchase a product at low to moderate levels of perceived tastiness when they are touched by a female server, whereas females are less likely to purchase. However, the touch encounter doesn’t matter for either gender when a consumer really likes the product.     

Does china income FSDs follow Benford? A comparison between Chinese income first significant digit distribution with Benford distribution

Since Benford’s law is an empirical phenomenon that occurs in a range of data sets, this raises the question as to whether or not the same thing might be true in terms of the Chinese income distribution data. We focus on the first significant digit (FSD) distribution of Chinese micro income data from the 2005 Inter-Census sample, which corresponds to 1% of Chinese population and other micro income data from the China family panel studies (CFPS) and Chinese General Social Survey (CGSS). We use information theoretic-entropy based methods to investigate the degree to which Benford’s FSD law is consistent with the FSD of Chinese income data and our findings suggest consistency between the Chinese FSD income distribution and Benford’s distribution. The close connection between the two distributions has implications for the quality of the sample of Chinese micro data.     

Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.