Dividend Policy: An Empirical Analysis
股利政策的实证分析AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:摘要研究企业股利政策与股利预测,未直接研究资产定价或收益预测,也未明确建立面板模型。
反馈分类问题股利政策的实证分析AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
Starting with the “partial adjustment model” suggested by Lintner [10, 11], this paper examines the dividend policies of individual firms. The Lintner model, in which the change in dividends from year t-1 to year t is regressed on a constant, the level of dividends for t-1, and the level of profits for t, explains dividend changes for individual firms fairly well relative to other models tested. But a model in which the constant term is suppressed and the level of earnings for t-1 is added, provides the best predictions of dividends on a year of data not used in fitting the regressions. Though the dividend policy of individual firms is certainly a subject of economic interest, perhaps much of the novelty of the paper is methodological: specifically, the way in which a validation sample, simulations, and prediction tests are used to investigate results obtained from a pilot sample. To avoid spurious results that could follow from the extensive data-dredging involved in finding “good-fitting” dividend models, only half of the available firms are used in the original search, the remaining firms serving as a check on the findings. In addition, since the models tested are autoregressive, their statistical properties cannot always be evaluated analytically. This problem is surmounted to some extent by using simulations to study the results and conclusions obtained from the data for individual firms. The novelty in this use of simulations is that they are directed towards checking specific empirical results rather than establishing the properties of some general model. Finally, the conclusions drawn from the regression analysis and from the simulations are again checked by using the various models to predict dividend changes for a new year of data. The coherence in the results obtained with these various tests justifies strong conclusions with respect to the “best” dividend models and their properties.
In this paper, we develop bootstrap methods for factor-augmented regressions with potentially weak common components, allowing signal eigenvalues to diverge at heterogeneous rates $N^{α_k}$, where $0<α_k\leq1$, $k=1,\ldots,r$. The regression estimator targets a rotation of the latent parameter vector that may be data dependent, while factor estimation can induce asymptotic bias. The proposed procedure directly constructs the rotation-specific target in the bootstrap world,…
Normality pre-tests on reduced-form innovations cannot detect whether two or more underlying structural shocks are Gaussian, and hence cannot detect a failure of identification in non-Gaussian SVARs. We propose a bootstrap-based approach that reveals such a failure. Under the null of valid identification and certain regularity conditions, the conditional bootstrap distribution of the maximum likelihood estimator of the impact matrix is asymptotically normal, so the…
Governments and development partners often seek Sustainable Development Goal (SDG) "accelerators" whose progress can propagate gains across human well-being, institutions, resources, and environmental priorities. Such claims require evidence on direction, timing, robustness, and trade-offs, not only network connectivity. We map directed SDG interdependence using a balanced panel of 114 countries observed annually from 2000 to 2024. Because the goal series are persistent,…
基于广义逆距离权重的GNAR模型改进多元时间序列预测
Many multivariate time series arise on networks where relationships between variables depend on physical or more general distances. Standard vector autoregressive models struggle in such settings due to their quadratic parameter growth, while generalised network autoregressive (GNAR) models offer a parsimonious alternative by explicitly incorporating network structures. Existing GNAR formulations assume fixed weights, typically uniform or reciprocal distances, which may not…
多元Lévy自适应B样条回归
We propose Multivariate Lévy Adaptive B-Spline Regression (MLABS), a Bayesian nonparametric regression model designed to capture nonlinear effects, interactions, and locally varying features of multivariate functions. MLABS represents the regression function as a random sum of low-order tensor-product B-spline basis functions under a finite Lévy random measure prior. The resulting representation adapts to the complexity and local structure of the regression function while…
Joint-event forecasts combine an estimated dependence model with current marginal forecasts. Retaining historical transforms at the marginal fits available when each outcome arrived creates overlapping estimation errors. We derive their joint influence with the terminal estimates in a stable Vine Copula VAR with normal innovation margins and a fixed, correctly specified Gaussian or positive Clayton vine. An intercept identity and the stable VAR filter reduce the historical…