Inference With Cross-Lagged Effects - Problems in Time and New Interpretations

The interpretation of cross-effects from vector autoregressive models to infer structure and causality amongst constructs is widespread and sometimes problematic. I first explain how hypothesis testing and regularization are invalidated when processes that are thought to fluctuate continuously in time are, as is typically done, modeled as changing only in discrete steps. I then describe an alternative interpretation of cross-effect parameters that incorporates correlated random changes for a potentially more realistic view of how process are temporally coupled. Using an example based on wellbeing data, I demonstrate how some classical concerns such as sign flipping and counter intuitive effect directions can disappear when using this combined deterministic / stochastic interpretation. Models that treat processes as continuously interacting offer both a resolution to the hypothesis testing problem, and the possibility of the combined stochastic / deterministic interpretation.

Temporal relationships between device-derived sedentary behavior, physical activity, and sleep in early childhood

Study Objectives Understanding the ideal composition of a child’s day requires a better understanding of the relations between wake behaviors (sedentary behavior [SB], physical activity [PA]) and sleep. Here we examine between- and within-person temporal associations between daytime wake behaviors and overnight sleep in early childhood, an important age when healthy behaviors are initiated and 24-hour behaviors are largely determined by caregivers. Methods Daily, repeated measures of wake behavior and overnight sleep were assessed via wrist-worn actigraphy (mean=9 days/nights) in 240 children (50.8±9.8 months). Multilevel models with lagged effects were used to examine the temporal associations between wake and overnight sleep measures and adjusted for daily nap duration, age, sex, and socioeconomic status. Results Between-person associations for sleep outcomes were negative between MVPA and total activity for sleep efficiency (SE). Between-person associations for wake outcomes were positive between sleep duration and light PA, and negative between SE and both MVPA and total PA. When children obtained higher SE relative to their individual average, they were more likely to engage in less SB and greater MVPA and total PA the next day. Conclusions Generally, days with greater activity or sleep were not associated with greater subsequent sleep or PA. Most subsequent behaviors were not influenced by children achieving higher activity or sleep relative to their individual average levels, although higher sleep efficiency was beneficially associated with next day wake behaviors. Future analyses with young children should consider within-person associations and could investigate lagged effects beyond one day.

Hurricanes Substantially Reduce the Nutrients in Tropical Forested Watersheds in Puerto Rico

Because nutrients including nitrogen and phosphorus are generally limited in tropical forest ecosystems in Puerto Rico, a quantitative understanding of the nutrient budget at a watershed scale is required to assess vegetation growth and predict forest carbon dynamics. Hurricanes are the most frequent disturbance in Puerto Rico and play an important role in regulating lateral nitrogen and phosphorus exports from the forested watershed. In this study, we selected seven watersheds in Puerto Rico to examine the immediate and lagged effects of hurricanes on nitrogen and phosphorous exports. Our results suggest that immediate surges of heavy precipitation associated with hurricanes accelerate nitrogen and phosphorus exports as much as 297 ± 113 and 306 ± 70 times than the long-term average, respectively. In addition, we estimated that it requires approximately one year for post-hurricane riverine nitrogen and phosphorus concentrations to recover to pre-hurricane levels. During the recovery period, the riverine nitrogen and phosphorus concentrations are 30 ± 6% and 28 ± 5% higher than the pre-hurricane concentrations on average.

The emerging role of drought as a regulator of dissolved organic carbon in the boreal landscapes

One likely consequence of global climate change is an increased frequency and intensity of droughts at high latitudes. We use a 17-year record from 13 nested boreal streams to examine the direct and lagged effects of summer drought on the quantity and quality of dissolved organic carbon (DOC) inputs from catchment soils. Protracted periods of drought reduced DOC concentrations in all catchments but also led to large pulses of DOC inputs upon rewetting in autumn. Concurrent changes in DOC optical properties and chemical character suggest that seasonal drying and rewetting triggers soil processes that alter the forms of carbon supplied to streams. Contrary to common belief, the clearest drought effects were observed in larger watersheds, whereas responses were most muted in smaller catchments. Collectively, our results reveal an emerging shift in the seasonal distribution of DOC concentrations and character, with potentially far-reaching consequences for northern aquatic ecosystems.

A Methodological Note: An Introduction to Autoregressive Models

The autoregressive model is a useful tool to analyze longitudinal data. It is particularly suitable for gerontological research as autoregressive models can be used to establish the causal relationship within a single variable over time as well as the causal ordering between two or more variables (e.g., physical health and psychological well-being) over time through bivariate autoregressive cross-lagged or contemporaneous models. Specifically, bivariate autoregressive models can explore the cross-lagged effects between two variables over time to determine the proper causal ordering between these variables. The advantage of analyzing cross-lagged effects is to test for the strength of prediction between two variables controlling for each variable's previous time score as well as the autoregressive component of the model. Bivariate autoregressive contemporaneous models can also be used to determine causal ordering within the same time point when compared to cross-lagged effects. Since the technique uses structural equation modeling, models are also adjusted for measurement error. This paper will present an introduction to setting up models and a step-by-step approach to analyzing univariate simplex autoregressive models, bivariate autoregressive cross-lagged models, and bivariate autoregressive contemporaneous models.

Equity Risk and Return across Hidden Market Regimes

The key to understanding the dynamics of stock markets, particularly the mechanisms of their changes, is in the concept of the market regime. It is regarded as a regular transition from one state to another. Although the market agenda is never the same, its functioning regime allows us to reveal the logic of its development. The article employs the concept of financial turbulence to identify hidden market regimes. These are revealed through the ratio of the components, which describe single changes of correlated risks and volatility. The combinations of typical and atypical variates of correlational and magnitude components of financial turbulence allowed four hidden regimes to be revealed. These were arranged by the degree of financial turbulence, conceptually analyzed and assessed from the perspective of their duration. The empirical data demonstrated ETF day trading profits for S&P 500 sectors, covering the period of January 1998–August 2020, as well as day trade profits of the Russian blue chips within the period of October 2006–February 2021. The results show a significant difference in regard to the market performance and volatility, which depend on hidden regimes. Both sample data groups demonstrated similar contemporaneous and lagged effects, which allows the prediction of volatility jumps in the periods following atypical correlations.

Longitudinal associations between interpersonal relationship functioning and posttraumatic stress disorder (PTSD) in recently traumatized individuals: differential findings by assessment method

Background The role of interpersonal relationship functioning in trauma recovery is well-established. However, much of this research has been done with cross-sectional samples, often years after trauma exposure, using self-report methodology only, and is focused on intimate relationship adjustment. Methods The current study investigated the longitudinal associations between interpersonal (intimate and non-intimate) relationship functioning and clinician- and self-reported posttraumatic stress disorder (PTSD) symptoms in 151 recently (within the past 6 months) traumatized individuals. Participants were assessed at four time points over 1 year. Results Approximately 53% of the sample was diagnosed with PTSD at initial assessment, with declining rates of diagnostic status over time to 16%. Latent difference score (LDS) modeling revealed nonlinear declines in both clinician-assessed and self-reported PTSD symptom severity, with faster declines in earlier periods. Likewise, LDS models revealed nonlinear declines in negative (conflict) aspects of interpersonal relationship functioning, but linear declines in positive (support, depth) aspects. The relationship between PTSD and relationship functioning differed for clinician- and self-reported PTSD. Bivariate LDS modeling revealed significant cross-lagged effects from relationship conflict to clinician-assessed PTSD, and significant cross-lagged effects from self-reported PTSD to relationship conflict over time. Conclusions These results highlight that the variability in prior results may be related to the method of assessing PTSD symptomatology and different relational constructs. Implications for theory and early intervention are discussed.