First‐order bias correction for fractionally integrated time series

Most of the long memory estimators for stationary fractionally integrated time series models are known to experience non‐negligible bias in small and finite samples. Simple moment estimators are also vulnerable to such bias, but can easily be corrected. In this article, the authors propose bias reduction methods for a lag‐one sample autocorrelation‐based moment estimator. In order to reduce the bias of the moment estimator, the authors explicitly obtain the exact bias of lag‐one sample autocorrelation up to the order n⁻¹. An example where the exact first‐order bias can be noticeably more accurate than its asymptotic counterpart, even for large samples, is presented. The authors show via a simulation study that the proposed methods are promising and effective in reducing the bias of the moment estimator with minimal variance inflation. The proposed methods are applied to the northern hemisphere data. The Canadian Journal of Statistics 37: 476–493; 2009 © 2009 Statistical Society of Canada     

‘We’re Civil Servants’: The Status of Trauma-Informed Care in the Community

ABSTRACT

Trauma-informed care (TIC) in social service organizations means that the organizations operate with the understanding that everyone involved has possibly experienced trauma in their lifetime. This qualitative study examined local service organizations’ usage of the 5 main principles of TIC: safety, trustworthiness, collaboration, empowerment, and choice (as developed by Fallot & Harris, 2006). Ten focus groups (n = 69) and 6 individual interviews (n = 6) with employees from administration through management were interviewed such that almost all facets of each agency were represented. The participants were asked about their agencies’ policies and practices for utilizing the 5 principles of TIC. The results suggest that the vast majority of organizations in this study implemented many of the principles of TIC with clients, though they had not labeled their practices as “trauma-informed.” However, although clients were receiving TIC, some of the principles were neglected as they pertain to staff, such as choice and empowerment. The findings of this study suggest that agencies are unaware of the relevance of TIC as it relates to staff. It is recommended that future research examine whether the use of TIC in agencies prevents “burnout,” high turnover rates, and vicarious traumatization of staff.     

The debt trap: A two-compartment train wreck… and how to avoid it

We explore sustainable paths out of a debt trap with a highly stylized two-sector differential equations model for the stocks of money in Government and Society. The model fits the data for the U.S. between 1981 and 2012 with a coefficient of correlation of 0.996. The solutions provide detailed “escape conditions” from the debt trap. A primary surplus is required. Then a government can escape its debt trap either through sustained annual monetary outflows from society to the government (taxation) but with a low initial growth rate, or through annual monetary inflows into both sectors (stimulus) with higher initial growth rate. We illustrate the use of our model with simulations which show how five indebted countries can escape their debt trap in 30 (or 70) years.     

A Comparison of Survival Function Estimators in the Koziol–Green Model

In this paper, three competing survival function estimators are compared under the assumptions of the so-called Koziol– Green model, which is a simple model of informative random censoring. It is shown that the model specific estimators of Ebrahimi and Abdushukurov, Cheng, and Lin are asymptotically equivalent. Further, exact expressions for the (noncentral) moments of these estimators are given, and their biases are analytically compared with the bias of the familiar Kaplan–Meier estimator. Finally, MSE comparisons of the three estimators are given for some selected rates of censoring.     

Modified minimax estimation of regression coefficients

Working with the linear regression model (1.1) and having the extraneous information (1.2) about regression coefficients the problem exists how to build estimators (1.3) with the risk (1.4) which enable to utilize the known information in order to reduce their risk as compared with the risk (1.6) of the LSE (1.5). Solution of this problem is known for the positive definite matrix T, namely in form for estimators (1.8) and (1.10).First, it is shown that the proposed estimators (2.6),(2.9) and (2.16) based on psedoinversions of the matrix L represent the solution of the problem of the positive semidefinite matrix T=L'L.Further, the problem of interpretability of estimators in the sense of the inequality (3.1) exists; it is shown that all mentioned estimators are at least partially interpretable in the sense of requirements (3.2) or (3.10).     

Estimation with right‐censored observations under a semi‐Markov model

The semi‐Markov process often provides a better framework than the classical Markov process for the analysis of events with multiple states. The purpose of this paper is twofold. First, we show that in the presence of right censoring, when the right end‐point of the support of the censoring time is strictly less than the right end‐point of the support of the semi‐Markov kernel, the transition probability of the semi‐Markov process is nonidentifiable, and the estimators proposed in the literature are inconsistent in general. We derive the set of all attainable values for the transition probability based on the censored data, and we propose a nonparametric inference procedure for the transition probability using this set. Second, the conventional approach to constructing confidence bands is not applicable for the semi‐Markov kernel and the sojourn time distribution. We propose new perturbation resampling methods to construct these confidence bands. Different weights and transformations are explored in the construction. We use simulation to examine our proposals and illustrate them with hospitalization data from a recent cancer survivor study. The Canadian Journal of Statistics 41: 237–256; 2013 © 2013 Statistical Society of Canada     

Consumer bankruptcy and default: The role of individual social capital

In this paper, we empirically assess the role of individual social capital on personal bankruptcy and default outcomes in the consumer credit market. After controlling for a borrower’s risk score, debt, income, wealth, and legal and economic environments, we find that default/bankruptcy risk rises and then falls over the lifecycle, while a borrower who owns a home or is married has a lower risk of default/bankruptcy. Moreover, a borrower who migrates 190 miles from his “state of birth” is 17% more likely to default and 15% more likely to file for bankruptcy, while a borrower who continues to live in his state of birth is 14% and 10% less likely to default and file for bankruptcy, respectively. A borrower who moves to a rural area is 9% and 7% less likely to default and declare bankruptcy, respectively. We also find that measures of social networks, norms, and cooperation and trust (i.e., aggregate social capital) are inversely related to consumer bankruptcy.