LOG-PERIODOGRAM ESTIMATION OF LONG MEMORY VOLATILITY DEPENDENCIES WITH CONDITIONALLY HEAVY TAILED RETURNS

Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared, and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that if the data is conditionally leptokurtic, the log-periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In United States stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.     

Sample sizes for estimation in clinical research

A sample size justification should be given for all clinical investigations. However, sometimes the objective of a trial is to estimate an effect with a view to planning a later definitive study. This paper describes the calculations for designing studies where one wishes to adopt an estimation approach through using confidence intervals around the overall response. Calculations are given for data anticipated to take a Normal form. Copyright © 2004 John Wiley & Sons, Ltd.     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data

Estimated associations between an outcome variable and misclassified covariates tend to be biased when the methods of estimation that ignore the classification error are applied. Available methods to account for misclassification often require the use of a validation sample (i.e. a gold standard). In practice, however, such a gold standard may be unavailable or impractical. We propose a Bayesian approach to adjust for misclassification in a binary covariate in the random effect logistic model when a gold standard is not available. This Markov Chain Monte Carlo (MCMC) approach uses two imperfect measures of a dichotomous exposure under the assumptions of conditional independence and non-differential misclassification. A simulated numerical example and a real clinical example are given to illustrate the proposed approach. Our results suggest that the estimated log odds of inpatient care and the corresponding standard deviation are much larger in our proposed method compared with the models ignoring misclassification. Ignoring misclassification produces downwardly biased estimates and underestimate uncertainty.     

Implied Preference for Seismic Design Level and Earthquake Insurance

Seismic risk can be reduced by implementing newly developed seismic provisions in design codes. Furthermore, financial protection or enhanced utility and happiness for stakeholders could be gained through the purchase of earthquake insurance. If this is not so, there would be no market for such insurance. However, perceived benefit associated with insurance is not universally shared by stakeholders partly due to their diverse risk attitudes. This study investigates the implied seismic design preference with insurance options for decisionmakers of bounded rationality whose preferences could be adequately represented by the cumulative prospect theory (CPT). The investigation is focused on assessing the sensitivity of the implied seismic design preference with insurance options to model parameters of the CPT and to fair and unfair insurance arrangements. Numerical results suggest that human cognitive limitation and risk perception can affect the implied seismic design preference by the CPT significantly. The mandatory purchase of fair insurance will lead the implied seismic design preference to the optimum design level that is dictated by the minimum expected lifecycle cost rule. Unfair insurance decreases the expected gain as well as its associated variability, which is preferred by risk-averse decisionmakers. The obtained results of the implied preference for the combination of the seismic design level and insurance option suggest that property owners, financial institutions, and municipalities can take advantage of affordable insurance to establish successful seismic risk management strategies.     

Generations,Identities, and the Collective Memory of Che Guevara1

Has the image of Che Guevara lost its power to evoke radical politics in the face of pervasive commodification? The commercialization of this 1960s political icon has called into question the power of the market to shape collective memories. Meanwhile, antisystemic movements of the left continue to erect his image at protest events. In light of this contest over how Che Guevara is remembered, we investigate, using data from a survey of Spanish citizens, who is most likely to recall him. We find qualified support for the theory of generational imprinting—Che is more often recalled by those generations who saw him rise to prominence during their formative years, although prominent as a collective symbol rather than as a living person. Our results also corroborate the claim that historical figures or events are more salient for, and therefore more likely to be remembered by, some subgenerational units than others. Thus, although the younger generations are in general more likely than their elders to recall Che, he is most frequently remembered by the highly educated leftists who espouse postmaterialist and posttraditionalist values and identify more with their local regions than with the nation of Spain. These patterns suggest that, in contrast to the dire predictions of mass culture theorists, the memory of Che Guevara has become increasingly tied to markers of social, ethnic‐regional, and political identity.     

Non-ignorable missing covariate data in survival analysis: a case-study of an International Breast Cancer Study Group trial

Summary.  Non-ignorable missing data, a serious problem in both clinical trials and observational studies, can lead to biased inferences. Quality-of-life measures have become increasingly popular in clinical trials. However, these measures are often incompletely observed, and investigators may suspect that missing quality-of-life data are likely to be non-ignorable. Although several recent references have addressed missing covariates in survival analysis, they all required the assumption that missingness is at random or that all covariates are discrete. We present a method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non-ignorable and continuous or discrete. Our method is useful in reducing the bias and improving efficiency in the presence of missing data. The methodology clearly specifies assumptions about the missing data mechanism and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results.     

Time, Self, and the Curiously Abstract Concept of Agency*

The term "agency" is quite slippery and is used differently depending on the epistemological roots and goals of scholars who employ it. Distressingly, the sociological literature on the concept rarely addresses relevant social psychological research. We take a social behaviorist approach to agency by suggesting that individual temporal orientations are underutilized in conceptualizing this core sociological concept. Different temporal foci—the actor's engaged response to situational circumstances—implicate different forms of agency. This article offers a theoretical model involving four analytical types of agency ("existential,""identity,""pragmatic," and "life course") that are often conflated across treatments of the topic. Each mode of agency overlaps with established social psychological literatures, most notably about the self, enabling scholars to anchor overly abstract treatments of agency within established research literatures.