Time-varying Relationship Research Articles

Towards a sustainable development: econometric analysis of energy use, economic factors, and CO2 emission in Pakistan during 1975-2018.

Many studies have computed the carbon dioxide emissions (CO2Es) associated with energy consumption, overall population, imports, manufacturing industries, and financial development in various countries. However, past studies have ignored the impact of CO2Es on fossil fuel energy, domestic economy, rural-urban unemployment, rural-urban population, services value-added, and fiscal deficit, especially in the context of Pakistan. Thus, to avoid the problems of mis-specification, sustainable growth, and carbon reduction simultaneously, it is necessary to study how to accomplish the time-varying relationship between factors. The present study applied autoregressive distributed lag (ARDL) model for cointegration between CO2Es and its determinants to test long-run and short-run effects from 1975 to 2018. The findings are as follows: first, in the short run, CO2Es, fossil fuel, and services value added show the unidirectional causality, while CO2Es, economic growth, rural-urban population, rural-urban unemployment, and fiscal deficit have bidirectional causality among them. Second, in the long run, we found bidirectional causality between CO2Es and its determinants. Finally, the diagnostic estimations, cumulative sum, and cumulative sum of squares check the long-run association between the selected variables and present the constancy of coefficients. The empirical outcomes give new insights for policymakers to regulate renewable technology investment in the energy sector for the improvement of environmental excellence. Related to the key results, the focused policies are presented below.

Characteristic Analysis of Water Lubricated Plain Journal Bearing under Transient Load

Transient load has a huge impact on the life and stability of water-lubricated bearings. In this paper work, CFD software is used to analyse the dynamic characteristic of water lubricated bearings under different transient loads of 500N, 1000N, 2000N and 3000N. The water film pressure contour distribution at different transient time was given. The time-varying relationships between the different transient loads with bearing forces, the journal displacement, the maximum value of water film pressure as well as the minimum value of water film thickness are obtained. The results show that with the increase of transient load, the effective bearing area of dynamic water pressure film decreases, and the maximum pressure increases. The bearing forces, the journal displacement, the maximum value of water film pressure as well as the minimum value of water film thickness will increase fast.

The Impact of Self- and Relation-Inferred Efficacy on Physical Activity in Older Adult Couples

Previous research has indicated that physical activity (PA) is a health-promoting behavior that is closely linked in couples. However, few studies have examined how PA is intertwined among couples in their everyday lives. For example, relation-inferred efficacy (RIE) is an individual perception that captures whether a close other believes in one’s own abilities to perform specific behaviours; it originates from the sports literature on coach-athlete dyads and has been shown to shape athlete performance. Applying a repeated daily life assessment design, the current study targets self-efficacy (SE) and relation-inferred efficacy (RIE) as predictors of PA in older adult couples, as well as potential moderators when obstacles occur. We hypothesized that: (a.) There is a main effect of SE and RIE on PA. (b.) PA is lower on days when people anticipate barriers (c.) SE and RIE moderate the time-varying relationship between PA and barriers. Heterosexual couples (N=108 couples, Mage=70.5 years, SD=6.70) rated their SE and REI, completed daily electronic questionnaires asking about barriers and wore an accelerometer to capture indices of PA across seven days. In line with past work, SE (r(2438)=.13, p=<.001) and RIE (r(2438)=.14, p=<.001) were significantly related to total moderate-to-vigorous PA (MVPA) and step counts. A series of multilevel models were fit to examine the hypotheses. Preliminary analyses indicated that RIE (estimate=3.93, SE=1.49, p=.009) is a stronger predictor of MVPA than SE (estimate=2.83, SE=2.02, p=.16). Further analysis will be conducted to unpack daily life circumstances that create barriers to PA, including daily pain, anxiety, and tiredness.

The Impact of Self- and Relation-Inferred Efficacy on Physical Activity in Older Adult Couples

With increasing life expectancy, late life has become a longer, crucial part of the individual and dyadic life course. New opportunities, tasks and decisions emerged. Successful aging norms emphasize agency and autonomy. This can be activating, but also alienating. With increasing constraints, agency is limited and ideals of autonomy become dysfunctional. This challenges also relationships. Aging, functional losses and approaching death threaten dyadic satisfaction and functionality. Potentially, successful aging norms could erode dyadic solidarity when needed the most: in late life. This mixed-methods longitudinal study combines interviews and questionnaires at three observations across five years. Its focus lies on change over time and findings at observation three. The sample consists of eight German couples (78-86 years old, 50-65 years married, high relationship satisfaction, white, urban). What does aging mean for individualized actors? How do aging couples negotiate, decide and act on aging, autonomy and death? How do successful aging norms modulate dyadic aging? Overall, actors have internalized successful aging and benefit by influencing their health positively. However, this has become ambivalent. Actors increasingly perceive their future as limited and beyond individual control. Acceptance of losses that challenge the self is difficult, autonomy ideals burdensome and death salient. As individual agency is constrained, the dyad is still a functional stronghold against aging. Yet, it has to adapt as well to – potentially differential - individual aging. Losses can and do threaten couples’ functional and emotional unity. Four patterns of self-dyad dynamics emerged and exemplify tensions between individualized and dyadic successful aging.

Time-varying impact of economic policy uncertainty and geopolitical risk on tourist arrivals: Evidence from a developing country

Although the relationship between uncertainties and tourism has been widely discussed, the time-varying impacts of uncertainties on tourism have been ignored. This paper investigates the time-varying relationship between two global and domestic uncertain shocks (namely, economic policy uncertainty (EPU), geopolitical risks (GPR)) with Chinese inbound tourism using a time-varying parameter vector autoregression (TVP-VAR) model. The empirical results show that uncertain shocks on tourist arrivals have significantly time-varying features, and the direction of the effect changes. The impact gradually weakens with the increase in the lag period. However, the impacts are heterogeneous depending on the uncertain shocks. Additionally, the effects of major event shocks on tourism differ. The uncertainty of the 9/11 terrorist attack had the shortest impact on tourism, while the impact on tourism was greater for geopolitical risks than for economic policy uncertainty during the global financial crisis. These findings provide new insights for policy holders and relevant investors into the mentioned relationships.

Drawbacks in the 3-Factor Approach of Fama and French (2018)

This paper features a statistical analysis of the monthly three factor Fama/French return series. Rolling OLS regressions explore the relationship between the 3 factors, using data from July 1926 to June 2018, available on French’s website. The results suggest there are significant and time-varying relationships between the factors. A sub-sample from July 1990 to July 2018 is used to analyze the three series using two-stage least squares and the Hausman test to check for issues related to endogeneity. The empirical results suggest that the factors, when combined in OLS regression analysis, as suggested by Fama and French (2018), are likely to suffer from endogeneity. Ramsey’s RESET tests suggest a nonlinear relationship exists between the three series. We use two instruments to estimate the market betas, and compare them to betas estimated not using instruments. Non-parametric tests of the two sets of betas suggest significant differences. The results suggest that using these factors in linear regression analysis, as recommended by Fama and French [(2018). Choosing factors. Journal of Financial Economics, 128(2), 234–252] is problematic in that the estimated coefficients are highly sensitive to the correct model specification.

Policy uncertainty and renewable energy: Exploring the implications for global energy transitions, energy security, and environmental risk management

Investment in renewable energy is paramount, especially in the context of climate change. However, while many studies investigate the drivers of renewable energy, little is known about how risk or uncertainty affects the distribution of renewables. Using parametric panel data methods, we find a negative long-run relationship between economic policy uncertainty and renewable energy consumption, suggesting that higher levels of country risk may dampen the uptake of renewables. To test the sensitivity of this result, we conducted a non-parametric analysis to explore the time-varying relationship between economic policy uncertainty and renewable energy consumption and the negative relationship holds for most of the sample period. Our findings assist policymakers geared towards energy transition, energy security, and environmental objectives (reducing greenhouse gas emissions) and should consider the economic risk profile of countries when attempting to encourage investment in renewable energy.

Classification of Drilled Lithology in Real-Time Using Deep Learning with Online Calibration

Summary Decision making to optimize the drilling operation is based on a variety of factors, among them real-time interpretation of drilled lithology. Because logging while drilling (LWD) tools are placed some meters above the bit, mechanical drilling parameters are the earliest indicators, although they are difficult to interpret accurately. This paper presents a novel deep learning methodology using mechanical drilling parameters for lithology classification. A cascade of deep neural networks (DNNs) are trained on historical data from wells on a field operated by Equinor. Rather than an end-to-end approach, the drilling parameters are used to estimate LWD sensor readings in an intermediate step using the first DNNs. This allows continuous updates of the models during operation using delayed LWD data. The second DNN takes the virtual LWD estimates as input to predict currently drilled lithology, similar to manual expert interpretation of logs. This configuration takes into account case-dependent [mud, bottomhole assembly (BHA), wellbore geometry] and time-varying (bit wear, wellbore friction) relationships between drilling parameters and LWD readings while assuming a constant rule when using LWD data to classify lithology. Upon completion of training and validation, the system is tested on a separate, unseen wellbore, for which results are presented. Visualizations for true lithology alongside the estimates are given, along with confusion matrices and model accuracy. The system achieves high accuracy on the test set and presents low confusion between classes, meaning that it distinguishes well between the lithologies present in the wellbore. It can be seen that the borders between successive layers of lithology are detected rapidly, which is crucial seen from an optimization standpoint, so the driller may immediately adjust accordingly. It shows promise as an advisory system, capable of accurately classifying currently drilled lithology by continuously adapting to changing downhole conditions. Although we cannot expect perfect estimates of lithology purely based on drilling parameters, we can obtain a preliminary map of the subsurface this way. This novel configuration gives a real-time interpretation of the currently drilled lithology. Thus, the drilling operation can be improved through early information and prompt drilling parameter adjustments to accommodate the current drilling environment.

Bayesian Shrinkage for Functional Network Models, With Applications to Longitudinal Item Response Data

ABSTRACT Longitudinal item response data are common in social science, educational science, and psychology, among other disciplines. Studying the time-varying relationships between items is crucial for educational assessment or designing marketing strategies from survey questions. Although dynamic network models have been widely developed, we cannot apply them directly to item response data because there are multiple systems of nodes with various types of local interactions among items, resulting in multiplex network structures. We propose a new model to study these temporal interactions among items by embedding the functional parameters within the exponential random graph model framework. Inference on such models is difficult because the likelihood functions contain intractable normalizing constants. Furthermore, the number of functional parameters grows exponentially as the number of items increases. Variable selection for such models is not trivial because standard shrinkage approaches do not consider temporal trends in functional parameters. To overcome these challenges, we develop a novel Bayes approach by combining an auxiliary variable MCMC algorithm and a recently developed functional shrinkage method. We apply our algorithm to survey and review datasets, illustrating that the proposed approach can avoid the evaluation of intractable normalizing constants as well as the detection of significant temporal interactions among items. Through a simulation study under different scenarios, we examine the performance of our algorithm. Our method is, to our knowledge, the first attempt to select functional variables for models with intractable normalizing constants. Supplementary materials for this article are available online.

It\u2019s complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US

Restricting in-person interactions is an important technique for limiting the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Although early research found strong associations between cell phone mobility and infection spread during the initial outbreaks in the United States, it is unclear whether this relationship persists across locations and time. We propose an interpretable statistical model to identify spatiotemporal variation in the association between mobility and infection rates. Using 1 year of US county-level data, we found that sharp drops in mobility often coincided with declining infection rates in the most populous counties in spring 2020. However, the association varied considerably in other locations and across time. Our findings are sensitive to model flexibility, as more restrictive models average over local effects and mask much of the spatiotemporal variation. We conclude that mobility does not appear to be a reliable leading indicator of infection rates, which may have important policy implications.

Energy and Bank Equity Interactions

Oil price shocks harm real output and bank and industrial profit in most oil-importing countries, which has motivated us to investigate the impact of these shocks on the equity performance of banking industries. To fulfill the research objectives, we involve a sample of developed and emerging economies for comparison purposes. The objective of employing the Toda and Yamamoto (Journal of econometrics, 1995, 66 (1), 225–250) causality test is to explore the time-variant relationship between oil prices and banking indices to investigate how oil price shocks affect the performance of country-specific banking industries. In addition, an impulse response function and variance decomposition analysis are utilized to, respectively, examine the time-variant relationship between oil price shocks and macroeconomic factors and the performance of the banking sector. Results vary across different economies in our sample, but the magnitude of oil price impact is relatively significant in the US, the UK, Canada, Japan, Mexico, and Brazil. The findings indicate that oil price rises adversely affect equity bank indices in developed and emerging economics except for Mexico. Notably, our findings show that oil prices and interest rates jointly have significant power in explaining the banking equity variation and suggest that international bank portfolio investors should consider hedging oil price risk.

Temperature and precipitation effects on the isotopic composition of global precipitation reveal long-term climate dynamics

Earth’s climate history is traced through the long-term covariance between the isotopic (δ18O) composition of archived meteoric waters (groundwater, ice cores) with air temperature (T) and amount of precipitation (P). To assess recent multi-decadal climatic changes, we analysed δ18O, T and P, and the relationships between these parameters at 20 stations having 60 years of continuous monthly isotopic records. Using nonparametric regressions and time series modelling we found significant linear and non-linear relationships for δ18O with T and P and showed that the δ18O dependency on these two parameters varied over decadal scales, thereby revealing complex relationships related to recycled moisture, large-scale convective processes and atmospheric-oceanic oscillations. Due to multiple factors controlling the δ18O composition of precipitation including P and T effects, we found that time-varying relationships between δ18O in precipitation P and T were better explained using the non-linear regressions. Our results affirmed that δ18O distributions in global precipitation are integrative indicators of climate dynamics whose patterns can be applied to better understand region-specific climatic changes in the present, past, and future.

Time-varying associations between everyday affect and cortisol in older couples.

Positive as well as negative emotional experiences fluctuate and they may be shared between romantic partners. Everyday emotion dynamics in couples may relate to physiological stress responses, with accompanying cortisol secretion being one pathway through which emotional dynamics can "get under the skin." We examined time-varying relationships of one's own as well as one's partner's sadness and contentment with salivary cortisol. We also examined the degree to which perceptions that the respective partner was sharing their feelings moderates these associations. Both partners of 84 community-dwelling older adult couples (Mage = 71.1 years) provided simultaneous daily life assessments of momentary cortisol, sadness and contentment, and perceptions of shared spousal feelings four times per day over seven consecutive days (up to 28 assessments). Momentary cortisol was higher in moments when individuals reported higher sadness, lower contentment, and perceptions that their partner was sharing their feelings less than usual. Cortisol was also higher when the partner reported higher sadness and lower contentment but only when one felt that the partner was sharing their feelings more than usual at that moment in time. This study illuminates a potential pathway (i.e., perceptions of shared spousal feelings) by which not only one's own emotional experiences, but also those of the spouse, are associated with the activation of physiological stress systems as indicated by elevated cortisol in a sample of older couples. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The conditional volatility premium on currency portfolios

Our paper examines conditional risk-return relations in a number of currency investment strategies, while modeling economic states using a large number of underlying risk factors. We identify a time-varying relationship between currency returns and volatility risk for most currency portfolios. In particular, value and momentum portfolios present risk-return relationships which switch sign, depending upon economic states. The positive relationship for the value portfolio is associated with “flight to quality” periods and the mean reversion for nominal exchange rates during financial crises. The positive relationship for the momentum portfolio is linked to the US and global business cycles and investors require positive compensation for risk in recessions.

Systemic Risk of China’s Financial Industry during the Spread of the COVID-19 Epidemic and the Breakdown of Crude Oil Negotiation

ABSTRACT This research first adopts three indicators to measure the systemic risk of different financial industries in China. Second, we employ the Time Varying Parameter-Stochastic Volatility-Vector Auto Regression (TVP-SV-VAR) model to investigate the time-varying relationship among COVID-19 epidemic, crude oil price, and financial systemic risk. The results herein not only help us grasp the current level of systematic risk in China, but also can assist at improving the early warning risk indicators and enhance the risk management system. Lastly, this research can also help investors to make reasonable asset planning.

A Time-Varying Coupling Analysis of Expressway Traffic Volume and Manufacturing PMI

This study investigates the time-varying coupling relationship between expressway traffic volume and manufacturing purchasing manager index (PMI). First, for the traffic volume and manufacturing PMI time-series data, unit root stability test and Johansen cointegration test are applied to determine the stability of single sequence and the long-term stable correlation between variables, respectively. Then, a time-varying vector autoregressive model (TVP-VAR) is developed to quantify the time-varying correlation between variables. The time-varying parameters of TVP-VAR are estimated using the Markov chain Monte Carlo (MCMC) theory. Finally, the model is validated using examples from China. In the numeric example, three variables, i.e., expressway car traffic volume, expressway truck traffic volume, and manufacturing PMI, are selected for analysis. Results show that there is a positive interaction between expressway traffic volume (both car and truck) and manufacturing PMI. Express traffic volume slowly promotes the development of manufacturing industry. However, with the reform policy of road freight structure in China, the promotion effect of truck traffic on manufacturing PMI in the past two years has decreased significantly. Moreover, as affected by the China demand-led economic development model in recent years, the stimulus effect of manufacturing PMI on expressway passenger traffic volume has increased year by year. And, while the expressway freight structure remains stable, truck traffic volume is hardly affected by fluctuations in manufacturing PMI. These research results are helpful for policy makers to understand the time-varying coupling relationship between expressway traffic volume and manufacturing development and finally to improve the expressway management level.

Transport infrastructure and house prices in the long run

We examine the effects of transport infrastructure on house prices in the OECD countries over the period 1870 to 2016. We employ both parametric and non-parametric panel data techniques that account for the non-linear and time-varying relationship between transport infrastructure and house prices. Our parametric results, which are based on newly developed panel data models that incorporate interactive fixed effects, suggest that transport infrastructure is positively associated with house prices. The non-parametric estimates suggest a time-varying relationship and that the average positive effect of transport infrastructure has been the case for most of the period we study. We also examine three channels through which transport infrastructure could influence house prices – economic growth, employment, and crime – and find that economic growth is a mechanism through which transport infrastructure transmits to house prices.

Time-Varying Impact of Economic Growth on Carbon Emission in BRICS Countries: New Evidence From Wavelet Analysis

Carbon emission leads to environmental and social consequences, which could be severe in the emerging economies. Owing to the dilemma of emission and economic expansion, it is necessary to achieve a more comprehensive understanding of the dynamic relationship between economic growth and carbon emission. Multivariate Wavelet analysis is introduced in addition to the decoupling analysis for BRICS countries. The decoupling analysis detects an obvious trend of economic growth decoupling from carbon emission in China, and generates mixed results for the other countries. Estimates of wavelet coherency suggest that BRICS countries have experienced different kinds of structural changes in growth–emission nexus. Results of partial phase-difference and wavelet gain imply that different resource endowments and growth paths lead to varied impact of economic growth on carbon emission and time-varying characteristics of the causality relationship over different frequencies. Energy structure and trade openness matter for anatomizing this time-varying relationship. To succeed in the fight against climate change, the policy makers need to pay serious attention to the dynamic impact of economic growth, energy structure, and trade openness on carbon emission.

A Routing Query Algorithm Based on Time-Varying Relationship Group in Opportunistic Social Networks

With the fast development of IoT and 5G technologies, opportunity social networks composed of portable mobile devices have become a hot research topic in recent years. However, arbitrary node movement in opportunity networks and the absence of end-to-end pathways make node communication unstable. At the same time, the problem of ignoring human social preferences and relying on wrong message relay nodes lead to a low data transmission rate and high network overhead. Based on the above issues, we propose a time-varying relationship groups-based routing query algorithm for mobile opportunity networks (Time-varying Relationship Groups, TVRGs). Firstly, we construct the relationship groups based on the time-varying characteristics according to the intimacy between users. Secondly, we calculate the importance of nodes by their connectivity time and communication frequency. Finally, we find the suitable message relay nodes according to the similarity of node weights and their action trajectories and design the routing query algorithm accordingly. The simulation results show that the algorithm can vastly improve the message query success rate, effectively improve the data transmission efficiency, and reduce the average delay and system overhead compared with the existing routing algorithms.

Transport infrastructure and CO2 emissions in the OECD over the long run

• We examine the impact of transport infrastructure on CO2 emissions across. • Our time period spans almost 150 years for OECD nations. • We utilise a newly developed nonparametric estimator. • A time-varying link is found between transport infrastructure and CO2 emissions. • The effect is most distinct between 1940 and 1960. A gap in the transportation-environment literature is the absence of studies analysing the effect of transport infrastructure on carbon dioxide (CO 2 ) emissions, controlling for other factors correlated with CO 2 emissions. We address this gap by providing parametric and non-parametric estimates of the effect of transport infrastructure on CO 2 emissions for a panel of OECD countries over a period of almost 150 years. We also examine economic growth and population as channels through which transport infrastructure influences CO 2 emissions. Our point estimates suggest that a 1% increase in transport infrastructure is associated with an increase in CO 2 emissions of about 0.4%, although dependent on the long-run estimator used. Our non-parametric estimates suggest a time-varying relationship between transport infrastructure and CO 2 emissions, which was positive during the first wave of globalisation, World War II and for most of the period since 1950. We find that economic growth and population mediate the transport infrastructure CO 2 emissions relationship.