Identification-robust moment-based tests for Markov switching in autoregressive models

ABSTRACT

This paper develops tests of the null hypothesis of linearity in the context of autoregressive models with Markov-switching means and variances. These tests are robust to the identification failures that plague conventional likelihood-based inference methods. The approach exploits the moments of normal mixtures implied by the regime-switching process and uses Monte Carlo test techniques to deal with the presence of an autoregressive component in the model specification. The proposed tests have very respectable power in comparison with the optimal tests for Markov-switching parameters of Carrasco et al. (2014 Carrasco, M., Hu, L., Ploberger, W. (2014). Optimal test for Markov switching parameters. Econometrica 82(2):765–784.[Crossref], [Web of Science ®] , [Google Scholar]), and they are also quite attractive owing to their computational simplicity. The new tests are illustrated with an empirical application to an autoregressive model of USA output growth.     

Markov regime switching in mean and in fractional integration parameter

We propose a specific general Markov-regime switching estimation both in the long memory parameter d and the mean of a time series. We employ Viterbi algorithm that combines the Viterbi procedures in two state Markov-switching parameter estimation. It is well-known that existence of mean break and long memory in time series can be easily confused with each other in most cases. Thus, we aim at observing the deviation and interaction of mean and d estimates for different cases. A Monte Carlo experiment reveals that the finite sample performance of the proposed algorithm for a simple mixture model of Markov-switching mean and d changes with respect to the fractional integrating parameters and the mean values for the two regimes.     

Testing Business Cycle Asymmetries Based on Autoregressions With a Markov-Switching Intercept

Implications of two concepts of asymmetry—deepness and steepness—are investigated for autoregressive processes with a Markov-switching intercept. The formulas for the skewness of these processes and the skewness of the first differences of these processes are derived. The parameter restrictions leading to nondeepness and nonsteepness are presented for the special case of a first-order autoregression and two states. It is shown that these restrictions imply that previous tests for asymmetries in autoregressive processes with a Markov-switching intercept can lead to wrong conclusions. In an empirical application of the developed tests, the U.S. unemployment rate is found to be steep.     

BOOST－PFC电路反馈环节的优化设计

介绍了平均电流控制ＢＯＯＳＴ─ＰＦＣ电路的原理，建立了电流环和电压环的小信号简化模型，讨论了双环反馈环节的设计原则和方法，提出最优设计的思想和数学模型，并以一种ＢＯＯＳＴ─ＰＦＣ电路为例进行优化设计，计算模拟结果显示优化后有关指标好于优化前。     

制造柔性的期权特征及其经济价值度量方法

本文分析了制造柔性的内涵,对其从生产状态变化的目标和产生来源进行了分类,并对制造柔性从期权的视角重新加以了表述.然后提出当存在市场需求、产品价格和可变成本三种不确定源的情形下制造柔性的多阶段期权定价模型,最后结合蒙特卡洛法实现了对项目投资决策阶段柔性经济价值的度量.     

叶嘉莹词学理论新框架初探

西方文学理论的特点在于研究的系统性与解剖的深入性，而中国古代文学理论的特点则在于对整体生命的把握。二者各有其优点和不足。叶嘉莹教授把这二者完美地结合起来，在词学研究中开辟出一条新的道路，使许多有争论的问题迎刃而解。文章对叶嘉莹的词学理论新框架进行了初步的探索，指出其最大特点是把古代与当代沟通起来，使古典作品在现代人心灵中得到生命的延续。这正是叶嘉莹词学理论体系的意义与价值之所在。     

Inference on Filtered and Smoothed Probabilities in Markov-Switching Autoregressive Models

ABSTRACT

We derive a statistical theory that provides useful asymptotic approximations to the distributions of the single inferences of filtered and smoothed probabilities, derived from time series characterized by Markov-switching dynamics. We show that the uncertainty in these probabilities diminishes when the states are separated, the variance of the shocks is low, and the time series or the regimes are persistent. As empirical illustrations of our approach, we analyze the U.S. GDP growth rates and the U.S. real interest rates. For both models, we illustrate the usefulness of the confidence intervals when identifying the business cycle phases and the interest rate regimes.     

OBS系统仿真平台的构建及分析

分析了光突发交换网络核心交换节点的基本交换结构、交换控制模块的主要功能以及核心交换模块的构成;对光突发交换系统进行了仿真平台的构建,讨论了系统仿真中所使用的主要模块及其功能、所涉及的关键技术,给出了所构建的仿真平台和可以设置的参数;在多种交换方式下,分别建立了核心交换节点仿真模型,并进行了仿真;讨论了采用不同交换结构对光突发丢失率性能的影响。     

任务转换范式下情绪注意偏向

相对于其他信息来说,情绪信息往往会引起情绪障碍患者更多的注意并具有一种认知加工上的优先权。在注意实验中,与中性刺激相比,具有情绪意义的刺激更能吸引注意或占用注意资源从而引起注意偏向。通过转换代价和任务规则一致性效应两个指标阐述了任务转换范式下情绪障碍患者的情绪信息注意偏向加工机制,并指出这两种指标分别代表了个体在注意偏向中的两个不同的认知控制加工,为情绪障碍患者的情绪调节提供必要的依据。文中最后还指出了ERPs、f MRI新技术是未来任务转换范式下研究情绪与注意关系的新方向。     

流动人口的自营性收入不平等与工资性收入不平等

利用2005年全国1％人口抽样调查的子样本数据以及2012年流动人口动态监测调查数据,研究了各可观测因素对自营者和受雇者收入分布的解释能力存在何种差异,及其在2005-2012年间所发生的变化.考虑到就业方式选择的内生性问题,文章首先采用转换回归模型对自营者和受雇者的收入方程进行了估计.在此基础上,进一步根据基于回归分析的分解分析方法,利用Shapley分解过程对自营者收入的Gini系数和受雇者收入的Gini系数分别进行了分解分析.结果显示,对于流动人口而言,收入分配过程中的制度性障碍正在减少.