Hierarchical Time-Varying Estimation of Asset Pricing Models

This paper presents a new hierarchical methodology for estimating multi factor dynamic asset pricing models. The approach is loosely based on the sequential Fama–MacBeth approach and developed in a kernel regression framework. However, the methodology uses a very flexible bandwidth selection method which is able to emphasize recent data and information to derive the most appropriate estimates of risk premia and factor loadings at each point in time. The choice of bandwidths and weighting schemes are achieved by a cross-validation procedure; this leads to consistent estimators of the risk premia and factor loadings. Additionally, an out-of-sample forecasting exercise indicates that the hierarchical method leads to a statistically significant improvement in forecast loss function measures, independently of the type of factor considered.

An Introduction to Factored Regression Models with Blimp

In this paper, we provide an introduction to the factored regression framework. This modeling framework applies the rules of probability to break up or “factor” a complex joint distribution into a product of conditional regression models. Using this framework, we can easily specify the complex multivariate models that missing data modeling requires. The article provides a brief conceptual overview of factored regression and describes the functional notation used to conceptualize the models. Furthermore, we present a conceptual overview of how the models are estimated and imputations are obtained. Finally, we discuss how users can use the free software package, Blimp, to estimate the models in the context of a mediation example.

Introducing LASSO-type penalisation to generalised joint regression modelling for count data

In this work, we propose an extension of the versatile joint regression framework for bivariate count responses of the package by Marra and Radice (R package version 0.2-3, 2020) by incorporating an (adaptive) LASSO-type penalty. The underlying estimation algorithm is based on a quadratic approximation of the penalty. The method enables variable selection and the corresponding estimates guarantee shrinkage and sparsity. Hence, this approach is particularly useful in high-dimensional count response settings. The proposal’s empirical performance is investigated in a simulation study and an application on FIFA World Cup football data.

A regression framework for brain network distance metrics

Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances (or similarities) between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances (or similarities) between connection matrices, and adapt several standard methods for estimation and inference within our framework: Standard F-test, F-test with individual level effects (ILE), Feasible Generalized Least Squares (FGLS), and Permutation. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing Multivariate Distance Matrix Regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.

Close Social Networks Among Older Adults: The Online and Offline Perspectives

Qualitative studies have found that the use of Information and Communication Technologies is related to an enhanced quality of life for older adults, as these technologies might act as a medium to access social capital regardless of geographical distance. In order to quantitatively study the association between older people’s characteristics and the likelihood of having a network of close friends offline and online, we use data from the Survey of Health, Ageing and Retirement in Europe and data from Facebook. Using a novel approach to analyze aggregated and anonymous Facebook data within a regression framework, we show that the associations between having close friends and age, sex, and being a parent are the same offline and online. Migrants who use internet are less likely to have close friends offline, but migrants who are Facebook users are more likely to have close friends online, suggesting that digital relationships may compensate for the potential lack of offline close friendships among older migrants.

Environmental cycles and individual variation in the vertical movements of a benthic elasmobranch

Trends in depth and vertical activity reflect the behaviour, habitat use and habitat preferences of marine organisms. However, among elasmobranchs, research has focused heavily on pelagic sharks, while the vertical movements of benthic elasmobranchs, such as skate (Rajidae), remain understudied. In this study, the vertical movements of the Critically Endangered flapper skate (Dipturus intermedius) were investigated using archival depth data collected at 2 min intervals from 21 individuals off the west coast of Scotland (56.5°N, −5.5°W) in 2016–17. Depth records comprised nearly four million observations and included eight time series longer than 1 year, forming one of the most comprehensive datasets collected on the movement of any skate to date. Additive modelling and functional data analysis were used to investigate vertical movements in relation to environmental cycles and individual characteristics. Vertical movements were dominated by individual variation but included prolonged periods of limited activity and more extensive movements that were associated with tidal, diel, lunar and seasonal cycles. Diel patterns were strongest, with irregular but frequent movements into shallower water at night, especially in autumn and winter. This research strengthens the evidence for vertical movements in relation to environmental cycles in benthic species and demonstrates a widely applicable flexible regression framework for movement research that recognises the importance of both individual-specific and group-level variation.

A more accurate method for colocalisation analysis allowing for multiple causal variants

In genome-wide association studies (GWAS) it is now common to search for, and find, multiple causal variants located in close proximity. It has also become standard to ask whether different traits share the same causal variants, but one of the popular methods to answer this question, coloc, makes the simplifying assumption that only a single causal variant exists for any given trait in any genomic region. Here, we examine the potential of the recently proposed Sum of Single Effects (SuSiE) regression framework, which can be used for fine-mapping genetic signals, for use with coloc. SuSiE is a novel approach that allows evidence for association at multiple causal variants to be evaluated simultaneously, whilst separating the statistical support for each variant conditional on the causal signal being considered. We show this results in more accurate coloc inference than other proposals to adapt coloc for multiple causal variants based on conditioning. We therefore recommend that coloc be used in combination with SuSiE to optimise accuracy of colocalisation analyses when multiple causal variants exist.

Analysis of Health Care Billing via Quantile Variable Selection Models

Fraudulent billing of health care insurance programs such as Medicare is in the billions of dollars. The extent of such overpayments remains an issue despite the emerging use of analytical methods for fraud detection. This motivates policy makers to also be interested in the provider billing characteristics and understand the common factors that drive conservative and/or aggressive behavior. Statistical approaches to tackling this problem are confronted by the asymmetric and/or leptokurtic distributions of billing data. This paper is a first attempt at using a quantile regression framework and a variable selection approach for medical billing analysis. The proposed method addresses the varying impacts of (potentially different) variables at the different quantiles of the billing aggressiveness distribution. We use the mammography procedure to showcase our analysis and offer recommendations on fraud detection.