Simultaneous inference of parental admixture proportions and admixture times from unphased local ancestry calls

Population genetic analyses of local ancestry tracts routinely assume that the ancestral admixture process is identical for both parents of an individual, an assumption that may be invalid when considering recent admixture. Here we present Parental Admixture Proportion Inference (PAPI), a Bayesian tool for inferring the admixture proportions and admixture times for each parent of a single admixed individual. PAPI analyzes unphased local ancestry tracts and has two components models: a binomial model that exploits the informativeness of homozygous ancestry regions to infer parental admixture proportions, and a hidden Markov model (HMM) that infers admixture times from tract lengths. Crucially, the HMM employs an approximation to the pedigree crossover dynamics that accounts for unobserved within-ancestry recombination, enabling inference of parental admixture times. We compared the accuracy of PAPI's admixture proportion estimates with those of ANCESTOR in simulated admixed individuals and found that PAPI outperforms ANCESTOR by an average of 46% in a representative set of simulation scenarios, with PAPI's estimates deviating from the ground truth by 0.047 on average. Moreover, PAPI's admixture time estimates were strongly correlated with the ground truth in these simulations (R = 0.76), but have an average downward bias of 1.01 generations that is partly attributable to inaccuracies in local ancestry inference. As an illustration of its utility, we ran PAPI on real African Americans from the PAGE study (N = 5,786) and found strong evidence of assortative mating by ancestry proportion: couples' ancestry proportions are closer to each other than expected by chance (P<10-6), and are highly correlated (R = 0.87). We anticipate that PAPI will be useful in studying the population dynamics of admixture and will also be of interest to individuals seeking to learn about their personal genealogies.

Economic Approach to Intergenerational Mobility

This chapter provides an analytical survey and synthesis of economic literature on intergenerational mobility in developing countries, with a focus on data and methodological challenges. Sample truncation from co-residency and measurement error cause substantial downward bias in intergenerational regression coefficient, whereas intergenerational correlation and intergenerational rank correlation are more robust to such data limitations. To understand heterogeneity, reliable estimates of both the intercept and the slope are necessary. The OLS estimate of the intercept is biased upward, but less so in the rank–rank regression. Sibling correlation is a broader measure of mobility, especially convenient with limited data. Estimating intergenerational causal effects is challenging as it requires long panel data. A promising alternative is to focus on the causal effects of policies on measurement of relative and absolute mobility, without disentangling the role of genetic inheritance.

Correcting the bias of the Root Mean Squared Error of Approximation under missing data

Missing data are ubiquitous in psychological research. They may come about as an unwanted result of coding or computer error, participants' non-response or absence, or missing values may be intentional, as in planned missing designs. We discuss the effects of missing data on χ²-based goodness-of-fit indices in Structural Equation Modeling (SEM), specifically on the Root Mean Squared Error of Approximation (RMSEA). We use simulations to show that naive implementations of the RMSEA have a downward bias in the presence of missing data and, thus, overestimate model goodness-of-fit. Unfortunately, many state-of-the-art software packages report the biased form of RMSEA. As a consequence, the scientific community may have been accepting a much larger fraction of models with non-acceptable model fit. We propose a bias-correction for the RMSEA based on information-theoretic considerations that take into account the expected misfit of a person with fully observed data. The corrected RMSEA is asymptotically independent of the proportion of missing data for misspecified models. Importantly, results of the corrected RMSEA computation are identical to naive RMSEA if there are no missing data.

Pervasive Downward Bias in Estimates of Liability Scale Heritability in GWAS Meta-Analysis: A Simple Solution

SNP heritability is a fundamental quantity in the genetic analysis of complex traits. For binary phenotypes, in which the continuous distribution of risk in the population is unobserved, observed-scale heritabilities must be transformed to the more interpretable liability-scale. We demonstrate here that the field standard approach for performing the liability conversion can downwardly bias estimates by as much as ~20% in simulation and ~30% in real data. These attenuated estimates stem from the standard approach failing to appropriately account for varying levels of ascertainment across the cohorts comprising the meta-analysis. We formally derive a simple procedure for incorporating cohort-specific ascertainment based on the summation of effective sample sizes across the contributing cohorts, and confirm via simulation that it produces unbiased estimates of liability-scale heritability.

Lower and upper bound estimates of inequality of opportunity for emerging economies

Equality of opportunity is an important normative ideal of distributive justice. In spite of its wide acceptance and economic relevance, standard estimation approaches suffer from data limitations that can lead to both downward and upward biased estimates of inequality of opportunity. These shortcomings may be particularly pronounced for emerging economies in which comprehensive household survey data of sufficient sample size is often unavailable. In this paper, we assess the extent of upward and downward bias in inequality of opportunity estimates for a set of twelve emerging economies. Our findings suggest strongly downward biased estimates of inequality of opportunity in these countries. To the contrary, there is little scope for upward bias. By bounding inequality of opportunity from above, we address recent critiques that worry about the prevalence of downward biased estimates and the ensuing possibility to downplay the normative significance of inequality.

Modelling and forecasting stock volatility and return: a new approach based on quantile Rogers–Satchell volatility measure with asymmetric bilinear CARR model

This paper proposes quantile Rogers–Satchell (QRS) measure to ensure robustness to intraday extreme prices. We add an efficient term to correct the downward bias of Rogers–Satchell (RS) measure and provide scaling factors for different interquantile range levels to ensure unbiasedness of QRS. Simulation studies confirm the efficiency of QRS measure relative to the intraday squared returns and RS measures in the presence of extreme prices. To smooth out noises, QRS measures are fitted to the CARR model with different asymmetric mean functions and error distributions. By comparing to two realised volatility measures as proxies for the unobserved true volatility, results from Standard and Poor 500 and Dow Jones Industrial Average indices show that QRS estimates using asymmetric bilinear mean function provide the best in-sample model fit based on two robust loss functions with heavier penalty for under-prediction. These fitted volatilities are then incorporated into return models to capture the heteroskedasticity of returns. Model with a constant mean, Student-t errors and QRS estimates gives the best in-sample fit. Different value-at-risk (VaR) and conditional VaR forecasts are provided based on this best return model. Performance measures including Kupiec test for VaRs are evaluated to confirm the accuracy of the VaR forecasts.

Strategic Welfare Policies with Migration: A Theoretical Model and Empirical Evidence

We test the welfare magnet hypothesis for Europe. We modify the existing theoretical frameworks assuming that: (a) welfare services, intended as the output of welfare expenditure, not the poor’s income or social expenditure, enter the median voter’s utility function; (b) preferences depend on the position of the median voter in the income distribution; and (c) the total amount of welfare services provided may differ from the amount needed to finance them, because of inefficiencies in the transfer process. We then test the welfare magnet hypothesis for 22 European countries by estimating a reaction function corresponding to the generic form adopted by the literature, but using the variables inspired by the model. We find evidence of a positive strategic interaction among countries, which suggests a downward bias in the choice of the protection level because of welfare competition. The level of social protection also positively depends on GDP, the redistributive attitudes of residents and their weight in the population, vis-à-vis the migrants’ share, and the efficiency of social expenditure.

Estimation of the case fatality rate based on stratification for the COVID-19 outbreak

This work is motivated by the recent worldwide pandemic of the novel coronavirus disease (COVID-19). When an epidemiological disease is prevalent, estimating the case fatality rate, the proportion of deaths out of the total cases, accurately and quickly is important as the case fatality rate is one of the crucial indicators of the risk of a disease. In this work, we propose an alternative estimator of the case fatality rate that provides more accurate estimate during an outbreak by reducing the downward bias (underestimation) of the naive CFR, the proportion of deaths out of confirmed cases at each time point, which is the most commonly used estimator due to the simplicity. The proposed estimator is designed to achieve the availability of real-time update by using the commonly reported quantities, the numbers of confirmed, cured, deceased cases, in the computation. To enhance the accuracy, the proposed estimator adapts a stratification, which allows the estimator to use information from heterogeneous strata separately. By the COVID-19 cases of China, South Korea and the United States, we numerically show the proposed stratification-based estimator plays a role of providing an early warning about the severity of a epidemiological disease that estimates the final case fatality rate accurately and shows faster convergence to the final case fatality rate.

The Role of Migration Costs in Residential Sorting

Economistsgenerallyemploytwo ‘revealed preference’ approaches to measure households’ preferences for non-market amenities—the hedonic and equilibrium sorting models. The conventional hedonic model assumes free mobility across space. Violation of this assumption can bias the estimates of household willingness to pay for local amenities. Mobility constraints are more easily handled by the sorting framework. In this chapter, we examine the role of migration costs in household residential sorting and apply these two models to estimate the willingness to pay for clean air in the USAand China. Our results demonstrate that ignoring mobility costs in spatial sorting will underestimate the implicit value of non-market amenities in both countries. Such a downward bias is larger in developing countries, such as China, where migration costs are higher.

Dynamics of Russians’ Health Status in 2003–2018: The Relationship Between Objective and Subjective Characteristics

The article uses data from the Russia Longitudinal Monitoring Survey – Higher School of Economics (RLMS-HSE) from 2003–2018 to analyze the dynamics of the health status of Russians, determined through subjective and objective characteristics. It is shown that, regardless of the chosen indicator, relatively few Russians currently have a good health, and the proportion of people without diagnosed diseases and those who assess their health as good does not differ much. Analysis of the dynamics of various health characteristics has shown that against the background of a decrease in the share of Russians without diagnosed health problems, the number of people with positive self-assessments of their health has increased. However, this growth appeared in the period of 2003–2013, and then stopped. Results of the multinomial regression model demonstrate that having diagnosed diseases leads to a downward bias in self-assessments of individual’s health, the degree of which depends on the nature of the disease. However, the complete absence of such diseases does not mean that an individual evaluates his health as good, since several socioeconomic and demographic factors, including psycho-emotional state, also have a significant impact on this self-assessment of health. The article also shows that objective and subjective health characteristics are associated with the self-care practices, which, although they have become somewhat more popular in the period of 2003–2018, still are used only by a minority of Russians, even among young people.