Shared Parenting After Parental Separation: The Views of 12 Experts

ABSTRACT

This article summarizes panel discussions that took place at an international conference on shared parenting (SP) held in May 2017. The panelists were internationally recognized experts on the legal and psychological implications of custody arrangements and parenting plans. Seven broad themes dominated the discussions: whether or not there was persuasive evidence that SP provides real benefits to children whose parents separate; what specific factors make SP beneficial; what symbolic value SP might have; whether there should be a legal presumption in favor of SP, and if so, what factors should make for exceptions; whether high parental conflict, parents’ failure to agree on the parenting plan, or dynamics of parental alienation should preclude SP; and what should happen when a parent wants to relocate away from the other parent.     

Does Joint Physical Custody “Cause” Children’s Better Outcomes?

ABSTRACT

Policymakers and researchers are concerned with whether joint physical custody (JPC) produces better outcomes for children than sole custody. Although several review articles summarizing up to 61 empirical articles demonstrate very positive answers, many of the research designs used compromise the ability to claim that it is JPC per se—and not selection effects—that causes the effect. We discuss several research design issues, such as propensity score analysis, that can more powerfully probe the question of causality. Some studies have already been conducted employing these strategies and more are recommended and likely to soon be forthcoming. On the basis of this comprehensive review we conclude that JPC probably does cause benefits to children on average, and that social scientists can now provisionally recommend rebuttably presumptive JPC to policymakers.     

Signal information of bird song changes in human-dominated landscapes

Urban Ecosystems - Communication systems rely upon specific social contexts and environments that permit effective transmission, and thus, are particularly vulnerable to disruption by anthropogenic...     

Factors related to attitudes toward diversity in Australia,Malaysia, and Puerto Rico

In this study, we tested social identity complexity theory (Roccas & Brewer, 2002) in relation to attitudes toward diversity and the associated variables of patriotism, nationalism, religiosity, aggression, and sense of self (well-being) in a cross-cultural study of 398 Malaysian, 239 Australian, and 201 Puerto Rican students. Puerto Ricans reported the most positive attitudes toward diversity, followed by Australians, and finally Malaysians. For Puerto Ricans, pro-diversity attitudes were predicted by a positive correlation with constructive patriotism and a negative correlation with blind patriotism. For Australians, pro-diversity attitudes were predicted by a positive association with constructive patriotism, a negative association with traditional nationalism, and a negative association with physical aggression. For Malaysians, pro-diversity attitudes were predicted by positive relationships with all of the following variables: constructive patriotism, traditional nationalism, verbal aggression, hostility, and the need to bolster self through idealizing others. These results support social identity complexity theory and partially support associated predictions for Australians, Puerto Ricans, and some of the predictions for Malaysians. The results are explained in terms of local cultural contexts that may not easily be understood by a simple understanding of the variables that were measured.     

When children’s testimonies are used as evidence: how children’s accounts may impact child custodial decisions

Abstract

In child custody cases, children oftentimes provide allegations of experienced trauma against one of their parents. Such allegations can happen before any investigative interviews (e.g., by the police or child protective services) have taken place. A central theme here concerns how to appraise such allegations and make certain that children’s accounts are taken seriously. In the current special issue, the focus is on new work on the functioning of children’s memory and its relation to trauma or work on children’s suggestibility and memory when they are traumatized. Specifically, key experts in the field of children’s memory provided contributions on: (1) the impact of interviewer support and rapport building on children’s testimonies, (2) the role of parental alienation in children’s testimonial accuracy, and (3) different types of false memories in children’s memory reports.     

Homemaking,Temporality and Later Life

Abstract

While much literature has focused on the meaning of home, relatively little has been conducted on homemaking in later life. This paper demonstrates the importance of time in conceptualizations of home. Using an extensive case study from a multi-interview study with eight older people in England, it is shown that gardening can form the basis of a temporal framework which structures a life. Importantly, gardening can seem essential in homemaking and a reason not to move to residential living. This paper builds on literature that suggests home is a process and that people’s conceptualization and experience of home develop throughout their lives. As ageing and dying have become long and complicated processes, it is argued older people may find this challenging as this makes it difficult to prepare for the end of life.     

Role of stylized features in constructing better estimators

This article discusses the role played by stylized features of financial time series in constructing better estimators for the model parameters. We study in detail one such estimator for the transition probabilities of a simple regime switching model. The estimator is based on the squared autocovariances of the time series, which has been discussed in several empirical studies of economic and financial time series. The effectiveness of this estimator in improving the estimation accuracy is investigated, using both finite sample and asymptotic computations. We also report simulation results to confirm our findings and to extend our conclusions over a bigger region of the parameter space.     

Trend estimation of multivariate time series with controlled smoothness

This paper extends the univariate time series smoothing approach provided by penalized least squares to a multivariate setting, thus allowing for joint estimation of several time series trends. The theoretical results are valid for the general multivariate case, but particular emphasis is placed on the bivariate situation from an applied point of view. The proposal is based on a vector signal-plus-noise representation of the observed data that requires the first two sample moments and specifying only one smoothing constant. A measure of the amount of smoothness of an estimated trend is introduced so that an analyst can set in advance a desired percentage of smoothness to be achieved by the trend estimate. The required smoothing constant is determined by the chosen percentage of smoothness. Closed form expressions for the smoothed estimated vector and its variance-covariance matrix are derived from a straightforward application of generalized least squares, thus providing best linear unbiased estimates for the trends. A detailed algorithm applicable for estimating bivariate time series trends is also presented and justified. The theoretical results are supported by a simulation study and two real applications. One corresponds to Mexican and US macroeconomic data within the context of business cycle analysis, and the other one to environmental data pertaining to a monitored site in Scotland.     

The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting.     

Layered adaptive importance sampling

Monte Carlo methods represent the de facto standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples. The performance of any such method is strictly related to the specification of the proposal distribution, such that unfortunate choices easily wreak havoc on the resulting estimators. In this work, we introduce a layered (i.e., hierarchical) procedure to generate samples employed within a Monte Carlo scheme. This approach ensures that an appropriate equivalent proposal density is always obtained automatically (thus eliminating the risk of a catastrophic performance), although at the expense of a moderate increase in the complexity. Furthermore, we provide a general unified importance sampling (IS) framework, where multiple proposal densities are employed and several IS schemes are introduced by applying the so-called deterministic mixture approach. Finally, given these schemes, we also propose a novel class of adaptive importance samplers using a population of proposals, where the adaptation is driven by independent parallel or interacting Markov chain Monte Carlo (MCMC) chains. The resulting algorithms efficiently combine the benefits of both IS and MCMC methods.