A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations

We introduce a new multivariate GARCH model with multivariate thresholds in conditional correlations and develop a two-step estimation procedure that is feasible in large dimensional applications. Optimal threshold functions are estimated endogenously from the data and the model conditional covariance matrix is ensured to be positive definite. We study the empirical performance of our model in two applications using U.S. stock and bond market data. In both applications our model has, in terms of statistical and economic significance, higher forecasting power than several other multivariate GARCH models for conditional correlations.     

Partitioning with Respect to a Control: Unequal Sample Sizes Case

This article describes a method for partitioning with respect to a control for the situation in which the treatment sample sizes are unequal and also for the situation where the treatment sample sizes are equal except for a few missing values. Calculation of the critical values required for finding confidence limits is discussed and tables are presented for the “almost equal” sample size case. An application of this method to length of stay data for congestive heart failure patients is also provided.     

Nonshared Environment Research

SUMMARY

This review focuses on conceptualizations of nonshared environment and on four areas of research that should be targeted for future growth. It is argued that there are at least two different approaches to the study of nonshared environment. “Experience-oriented” researchers center on sibling differential experiences in the family and their role in children's development. “Outcome-oriented” investigators focus on the search for environmental origins of individual differences in outcomes. Turkheimer and Waldron's (2000) concept of objective versus effective nonshared environment and Reiss and colleagues' (2000) notion of single-system versus multi-system nonshared environment processes are also discussed. Four topics for future research are outlined: (1) age-related changes and development; (2) the role of the self; (3) the role of context; and (4) the importance of extrafamilial experiences. More work in these areas will lead to useful theories of how nonshared environment processes are linked to sibling and individual differences in behavioral development and adjustment.     

When Does Predation Dominate Collusion?

I study repeated competition among oligopolists. The only novelty is that firms may go bankrupt and permanently exit: the probability that a firm survives a price war depends on its financial strength, which varies stochastically over time. Under some conditions including no entry, an anti‐folk theorem holds: when firms are patient, so that strength levels change relatively quickly, every Nash equilibrium involves an immediate price war that lasts until at most one firm remains. Surprisingly, the possibility of entry may facilitate collusion, as may impatience. The model can explain some observed patterns of collusion and predation.     

自我表露及其作用

有关自我表露的研究是心理学关于自我研究的一个重要领域,心理学家发现,展示个人信息的行为有重要的心理学意义.自我表露是个人成长和自我实现的必要环节,是个体印象管理和应对压力的有效方式,也是心理治疗的重要途径.     

跨国公司对华投资国民待遇问题研究

跨国投资企业对东道国发挥的经济效应 ,与东道国对跨国投资企业给予的待遇密切相关。介绍外国对其外资企业所采取的待遇标准 ,论述我国对来华投资企业的待遇安排及其产生的效果 ,认为我国当前对来华投资企业实行国民待遇不切实际 ,只能逐步向有限制的国民待遇方向发展。     

Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.     

Model-based measurement of latent risk in time series with applications

Summary.  Risk is at the centre of many policy decisions in companies, governments and other institutions. The risk of road fatalities concerns local governments in planning countermeasures, the risk and severity of counterparty default concerns bank risk managers daily and the risk of infection has actuarial and epidemiological consequences. However, risk cannot be observed directly and it usually varies over time. We introduce a general multivariate time series model for the analysis of risk based on latent processes for the exposure to an event, the risk of that event occurring and the severity of the event. Linear state space methods can be used for the statistical treatment of the model. The new framework is illustrated for time series of insurance claims, credit card purchases and road safety. It is shown that the general methodology can be effectively used in the assessment of risk.     

多媒体技术在连串反应动力学教学中的应用

利用Matlab语言中Simulink工具 ,开发了连串反应动力学过程动态模拟多媒体功能模块 ,并将其应用于教学中 ,效果良好。