Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.     

Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.     

Inference for large financial systems

We treat the parameter estimation problem for mean‐field models of large interacting financial systems such as the banking system and a pool of assets held by an institution or backing a security. We develop an asymptotic inference approach that addresses the scale and complexity of such systems. Harnessing the weak convergence results developed for mean‐field financial systems in the literature, we construct an approximate likelihood for large systems. The approximate likelihood has a conditionally Gaussian structure, enabling us to design an efficient numerical method for its evaluation. We provide a representation of the corresponding approximate estimator in terms of a weighted least‐squares estimator, and use it to analyze the large‐system and large‐sample behavior of the estimator. Numerical results for a mean‐field model of systemic financial risk highlight the efficiency and accuracy of our estimator.     

Takeover vulnerability and the credibility of signaling: The case of open-market share repurchases

There is debate in the literature focuses on whether open market repurchases can be taken as a signal of stock undervaluation. This research argues that takeover pressures before a repurchase announcement can be a credible signal of undervaluation. The empirical results indicate that repurchasing firms with a higher probability of takeover experience greater announcement effects, improvements in operating performance and long-run abnormal return, positive forecast revisions by financial analysts, and enhanced agreement between management and shareholders. These findings suggest that takeover probability and open-market share repurchases appear to constitute a double-signal for conveying stock undervaluation to the market.     

DSGE pileups

The sampling distribution of estimators for DSGE structural parameters tends to be non-normal and/or pile up on the boundary of the theoretically admissible parameter space. This calls into question both the reliability of asymptotic approximations and the presumption of correct specification. This paper seeks to develop a conceptual framework for understanding how these phenomena arise, and to provide pragmatic methods for dealing with them in practice. The results are presented in three examples and a medium scale DSGE model.     

A true simulation study of three estimators at equal protection of respondents in randomized response sampling

In this study, three different estimators for estimating the proportion of a sensitive attribute in survey sampling are compared at equal protection of the respondents. The three estimators considered are due to Odumade and Singh (2009, Comm. Statist. Theory Methods) , Singh and Sedory (2011, Sociological Methods and Research) and a new estimator obtained by minimizing a chi‐squared distance. A SAS Macro is developed to compare these three estimators using a simulation study at equal protection of the respondents. A set of data from a real face‐to‐face interview was collected using two decks of cards and has been analyzed. The results are discussed.     

基于最大似然估计的分集信号中频合成方法

为了降低分集信号合成时输入信号的信噪比要求,提出了一种分集信号的中频合成方法 。该方法利用最大似然估计和迭代算法,通过计算输出合成信号和输入分集信号的相关值获 得合成权值,在中频实现信号合成。仿真和实验结果表明,该方法放宽了输入信号信噪比要 高于解调捕获门限这一要求,能自动实现最大合成增益。     

普通似然及经验似然情况下的结点问题

本文在普通似然及经验似然情况下,分别对数据无结点和有结点的情况做了计算,得出在普通似然及经验似然问题中,有结点与无结点情况完全相同的结论。     

跳频信号的归一化多普勒频差最大似然估计

针对双站定位涉及的跳频信号多普勒频差估计问题,提出了归一化的频差最大似然估计算法,利用两个定位站接收到的跳频脉冲串信号,构建一个关于基准跳频频率多普勒频差的似然函数,通过网格搜索得到使似然函数最大的多普勒频差估计,既解决了跳频信号在不同跳频频率上多普勒频差不一致的问题,又充分利用在不同频率的脉冲串信号提高了多普勒频差估计精度。通过仿真对算法的性能进行了评估,结果表明,与基于子空间的算法比较,在脉冲数达到240个时,所提算法执行效率提升30%以上。     

Dynamic causality between the U.S. stock market,the Chinese stock market and the global gold market: implications for individual investors’ diversification strategies

This paper proposes a generalization of the prior VAR and EGARCH model to explore the linkage between returns and volatility transmissions in the U.S. stock market, the Chinese stock market, and the global gold market from 10 July 1996 to 20 July 2018. We found that past returns of the U.S. stock market can predict the current returns of the other two markets, and that significant reciprocal volatility transmission existed within and across all three markets. We further implemented average out-of-sample (OOS) forecasting to show that a risk-adjusted portfolio, such as mean-variance with sample estimator, does not outperform an equal-weighted portfolio. This provides insights for individual investors and helps to explain the ongoing disagreement in the portfolio literature concerning the effectiveness of risk-adjusted portfolios and equal-weighted portfolios when the number of assets is small.