Modeling and forecasting realized covariance matrices with accounting for leverage

The existing dynamic models for realized covariance matrices do not account for an asymmetry with respect to price directions. We modify the recently proposed conditional autoregressive Wishart (CAW) model to allow for the leverage effect. In the conditional threshold autoregressive Wishart (CTAW) model and its variations the parameters governing each asset's volatility and covolatility dynamics are subject to switches that depend on signs of previous asset returns or previous market returns. We evaluate the predictive ability of the CTAW model and its restricted and extended specifications from both statistical and economic points of view. We find strong evidence that many CTAW specifications have a better in-sample fit and tend to have a better out-of-sample predictive ability than the original CAW model and its modifications.     

Robust multi-sensor scheduling for multi-site surveillance

This paper presents mathematical programming techniques for solving a class of multi-sensor scheduling problems. Robust optimization problems are formulated for both deterministic and stochastic cases using linear 0–1 programming techniques. Equivalent formulations are developed in terms of cardinality constraints. We conducted numerical case studies and analyzed the performance of optimization solvers on the considered problem instances.     

Multi‐sited ethnography: Opportunities for the study of race

Multi‐sited ethnography (MSE) is an underutilized research method that presents particular challenges and unique possibilities for generative research that can build upon existing theories of race and ethnicity. This review discusses the theoretical and methodological components of MSE and provides examples of both transnational and translocal ethnographic studies. These examples illustrate how MSE as a method can enrich analyses of race and ethnicity by enabling ethnographers to identify site‐specific phenomena as well as social processes that transcend a single locale. MSE, when intentionally paired with methodological framings in line with research priorities, can enable scholars to produce rigorous sociological analysis and theory building. Multi‐sited ethnographies of race can provide a set of analyses that complement a range of other sociological research tools, including single‐sited ethnography and quantitative studies, to deepen our understandings about the production and maintenance of racial and ethnic categories in the United States and transnationally.     

通过透析作为社会管理方式的民主制揭示社会心理学现象

旨在对那些研究社会政治进程的社会心理进行分析。民主被视为社会治理的手段之一,同时,民主作为社会政治体系,以社会心理现象为表象,通过对个人与社会的行为动机加以治理的方式呈现出来。民主具有程序性,或者说,民主是一个过程;民主属性发展程度的特点在于公民参与制定社会决策的程度;没有既民主又不民主的国家,只是各国民主发展程度不同。     

Forecasting time series of economic processes by model averaging across data frames of various lengths

This paper presents an extension of mean-squared forecast error (MSFE) model averaging for integrating linear regression models computed on data frames of various lengths. Proposed method is considered to be a preferable alternative to best model selection by various efficiency criteria such as Bayesian information criterion (BIC), Akaike information criterion (AIC), F-statistics and mean-squared error (MSE) as well as to Bayesian model averaging (BMA) and naïve simple forecast average. The method is developed to deal with possibly non-nested models having different number of observations and selects forecast weights by minimizing the unbiased estimator of MSFE. Proposed method also yields forecast confidence intervals with a given significance level what is not possible when applying other model averaging methods. In addition, out-of-sample simulation and empirical testing proves efficiency of such kind of averaging when forecasting economic processes.     

Modeling veterans’ health benefit grants using the expectation maximization algorithm

A novel application of the expectation maximization (EM) algorithm is proposed for modeling right-censored multiple regression. Parameter estimates, variability assessment, and model selection are summarized in a multiple regression settings assuming a normal model. The performance of this method is assessed through a simulation study. New formulas for measuring model utility and diagnostics are derived based on the EM algorithm. They include reconstructed coefficient of determination and influence diagnostics based on a one-step deletion method. A real data set, provided by North Dakota Department of Veterans Affairs, is modeled using the proposed methodology. Empirical findings should be of benefit to government policy-makers.     

“Better red than dead”: Socialism in British Public Schools, 1900–1918

This paper examines socialism in Edwardian public schools. It concentrates on understanding their students’ responses to, and conceptions of, socialism’s core tenets. For, existing scholarship has depicted these Edwardian institutions as almost uniformly conservative and proficient in inculcating students with pro-social hierarchy, anti-social innovation views. Any ideological tensions, then, stemmed from the interaction of popular, conservative-compliant, dogmas within school contexts: social Darwinism, imperialism, athleticism, muscular Christianity and so on. This paper, however, draws on new Eton College archive sources to enhance and complicate this view. It argues socialism captivated the students’ attention and garnered a strong supportive minority that, at times, advocated complete collective ownership. Moreover, the school administration showed tolerance to even the most outspoken socialists, and its non-hegemonic, semi-decentralized nature gave rise to local, independent sites of support. Accordingly, this research is important to understanding Edwardian public school ideologies and socialization processes. In wider terms, too, it casts insight on Edwardian socialism’s penetration into secondary education and inter-class acceptance.     

p-Value adjustment to control type I errors in linear regression models

The paper is devoted to a new randomization method that yields unbiased adjustments of p-values for linear regression model predictors by incorporating the number of potential explanatory variables, their variance–covariance matrix and its uncertainty, based on the number of observations. This adjustment helps control type I errors in scientific studies, significantly decreasing the number of publications that report false relations to be authentic ones. Comparative analysis with such existing methods as Bonferroni correction and Shehata and White adjustments explicitly shows their imperfections, especially in case when the number of observations and the number of potential explanatory variables are approximately equal. Proposed method is easy to program and can be integrated into any statistical software package.