scholarly journals Networks of stress, affect and eating behaviour: anticipated stress coping predicts goal-congruent eating in young adults

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Björn Pannicke ◽  
Tim Kaiser ◽  
Julia Reichenberger ◽  
Jens Blechert
Keyword(s):  
Time Series Data ◽  
Self Regulation ◽  
Eating Behaviour ◽  
Network Models ◽  
Series Data ◽  
Stress Coping ◽  
Longitudinal Assessment ◽  
Eating Behaviours ◽  
Vector Autoregressive ◽  
Age Range

Abstract Background Many people aim to eat healthily. Yet, affluent food environments encourage consumption of energy dense and nutrient-poor foods, making it difficult to accomplish individual goals such as maintaining a healthy diet and weight. Moreover, goal-congruent eating might be influenced by affects, stress and intense food cravings and might also impinge on these in turn. Directionality and interrelations of these variables are currently unclear, which impedes targeted intervention. Psychological network models offer an exploratory approach that might be helpful to identify unique associations between numerous variables as well as their directionality when based on longitudinal time-series data. Methods Across 14 days, 84 diet-interested participants (age range: 18–38 years, 85.7% female, mostly recruited via universities) reported their momentary states as well as retrospective eating episodes four times a day. We used multilevel vector autoregressive network models based on ecological momentary assessment data of momentary affects, perceived stress and stress coping, hunger, food craving as well as goal-congruent eating behaviour. Results Neither of the momentary measures of stress (experience of stress or stress coping), momentary affects or craving uniquely predicted goal-congruent eating. Yet, temporal effects indicated that higher anticipated stress coping predicted subsequent goal-congruent eating. Thus, the more confident participants were in their coping with upcoming challenges, the more they ate in line with their goals. Conclusion Most eating behaviour interventions focus on hunger and craving alongside negative and positive affect, thereby overlooking additional important variables like stress coping. Furthermore, self-regulation of eating behaviours seems to be represented by how much someone perceives a particular eating episode as matching their individual eating goal. To conclude, stress coping might be a potential novel intervention target for eating related Just-In-Time Adaptive Interventions in the context of intensive longitudinal assessment.

scholarly journals Networks of stress, affect and eating behaviour: anticipated stress coping predicts goal-congruent eating in young adults

2020 ◽  
Author(s):  
Björn Pannicke ◽  
Tim Kaiser ◽  
Julia Reichenberger ◽  
Jens Blechert
Keyword(s):  
Time Series Data ◽  
Self Regulation ◽  
Eating Behaviour ◽  
Network Models ◽  
Series Data ◽  
Stress Coping ◽  
Longitudinal Assessment ◽  
Eating Behaviours ◽  
Vector Autoregressive ◽  
Age Range

Background: Many people aim to eat healthily. Yet, affluent food environments encourage consumption of energy dense and nutrient-poor foods, making it difficult to accomplish individual goals such as maintaining a healthy diet and weight. Moreover, goal-congruent eating might be influenced by affects, stress and intense food cravings and might also impinge on these in turn. Directionality and interrelations of these variables are currently unclear, which impedes targeted intervention. Psychological network models offer an exploratory approach that might be helpful to identify unique associations between numerous variables as well as their directionality when based on longitudinal time-series data. Methods: Across 14 days, 84 diet-interested participants (age range: 18 – 38 years, 85.7% female, mostly recruited via universities) reported their momentary states as well as retrospective eating episodes four times a day. We used multi-level vector autoregressive network models based on ecological momentary assessment data of momentary affects, perceived stress and stress coping, hunger, food craving as well as goal-congruent eating behaviour. Results: Neither of the momentary measures of stress (experience of stress or stress coping), momentary affects or cravings uniquely predicted goal-congruent eating. Yet, temporal effects indicated that higher anticipated stress coping predicted subsequent goal-congruent eating. Thus, the more confident participants were in their coping with upcoming challenges, the more they ate in line with their goals. Conclusion: Most eating behaviour interventions focus on hunger and craving alongside negative and positive affect, thereby overlooking additional important variables like stress coping. Furthermore, self-regulation of eating behaviours seems to be represented by how much someone perceives a particular eating episode as matching their individual eating goal. To conclude, stress coping might be a potential novel intervention target for eating related Just-In-Time Adaptive Interventions in the context of intensive longitudinal assessment.

scholarly journals Hypothesizing directional causality between the governance indicators and economic growth: the case of Afghanistan

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Mohammad Naim Azimi ◽  
Mohammad Musa Shafiq
Keyword(s):  
Economic Growth ◽  
Rule Of Law ◽  
Time Series Data ◽  
Political Stability ◽  
Wald Test ◽  
Series Data ◽  
Vector Autoregressive ◽  
Government Effectiveness ◽  
Governance Indicators ◽  
The Government

AbstractThis paper examines the causal relationship between governance indicators and economic growth in Afghanistan. We use a set of quarterly time series data from 2003Q1 to 2018Q4 to test our hypothesis. Following Toda and Yamamoto’s (J Econom 66(1–2):225–250, 1995. 10.1016/0304-4076(94)01616-8) vector autoregressive model and the modified Wald test, our empirical results show a unidirectional causality between the government effectiveness, rule of law, and the economic growth. Our findings exhibit significant causal relationships running from economic growth to the eradication of corruption, the establishment of the rule of law, quality of regulatory measures, government effectiveness, and political stability. More interestingly, we support the significant multidimensional causality hypothesis among the governance indicators. Overall, our findings not only reveal causality between economic growth and governance indicators, but they also show interdependencies among the governance indicators.

scholarly journals A Study on Estimation and Prediction of Vector Time Series Model Using Financial Big Data (Interest Rates)

2021 ◽  
Vol 12 (5) ◽  
pp. 309-316
Author(s):  
Jae-Hyun Kim, Chang-Ho An
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Prediction Model ◽  
Interest Rates ◽  
Time Series Data ◽  
Information Criteria ◽  
Series Data ◽  
Call Rate ◽  
Causality Test ◽  
Vector Autoregressive

Due to the global economic downturn, the Korean economy continues to slump. Hereupon the Bank of Korea implemented a monetary policy of cutting the base rate to actively respond to the economic slowdown and low prices. Economists have been trying to predict and analyze interest rate hikes and cuts. Therefore, in this study, a prediction model was estimated and evaluated using vector autoregressive model with time series data of long- and short-term interest rates. The data used for this purpose were call rate (1 day), loan interest rate, and Treasury rate (3 years) between January 2002 and December 2019, which were extracted monthly from the Bank of Korea database and used as variables, and a vector autoregressive (VAR) model was used as a research model. The stationarity test of variables was confirmed by the ADF-unit root test. Bidirectional linear dependency relationship between variables was confirmed by the Granger causality test. For the model identification, AICC, SBC, and HQC statistics, which were the minimum information criteria, were used. The significance of the parameters was confirmed through t-tests, and the fitness of the estimated prediction model was confirmed by the significance test of the cross-correlation matrix and the multivariate Portmanteau test. As a result of predicting call rate, loan interest rate, and Treasury rate using the prediction model presented in this study, it is predicted that interest rates will continue to drop.

Inferring Gene Regulatory Network Models from Time-Series Data Using Metaheuristics

2020 ◽  
Author(s):  
Jose Eduardo H. da Silva ◽  
Heder S. Betnardino ◽  
Helio J.C. Barbosa ◽  
Alex B. Vieira ◽  
Luciana C.D. Campos ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Network Models ◽  
Series Data ◽  
Gene Regulatory

Application of Deep Learning for Characterization of Drivers’ Engagement in Secondary Tasks in In-Vehicle Systems

2020 ◽  
Vol 2674 (8) ◽  
pp. 429-440
Author(s):  
Osama A. Osman ◽  
Hesham Rakha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Network Models ◽  
Sensor Technology ◽  
Series Data ◽  
Neural Network Models ◽  
Secondary Tasks ◽  
Driving Assistance

Distracted driving (i.e., engaging in secondary tasks) is an epidemic that threatens the lives of thousands every year. Data collected from vehicular sensor technologies and through connectivity provide comprehensive information that, if used to detect driver engagement in secondary tasks, could save thousands of lives and millions of dollars. This study investigates the possibility of achieving this goal using promising deep learning tools. Specifically, two deep neural network models (a multilayer perceptron neural network model and a long short-term memory networks [LSTMN] model) were developed to identify three secondary tasks: cellphone calling, cellphone texting, and conversation with adjacent passengers. The Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) time series data, collected using vehicle sensor technology, were used to train and test the model. The results show excellent performance for the developed models, with a slight improvement for the LSTMN model, with overall classification accuracies ranging between 95 and 96%. Specifically, the models are able to identify the different types of secondary tasks with high accuracies of 100% for calling, 96%–97% for texting, 90%–91% for conversation, and 95%–96% for the normal driving. Based on this performance, the developed models improve on the results of a previous model developed by the author to classify the same three secondary tasks, which had an accuracy of 82%. The model is promising for use in in-vehicle driving assistance technology to report engagement in unlawful tasks or alert drivers to take over control in level 1 and 2 automated vehicles.

The Dynamics of Exports and Economic Growth: Assessing the Evidence from Nigeria

2018 ◽  
Vol 4 (1) ◽  
pp. 15-22
Author(s):  
Clement A.U. Ighodaro ◽  
Ovenseri-Ogbomo F. O.
Keyword(s):  
Economic Growth ◽  
Social Services ◽  
Time Series Data ◽  
Positive Impact ◽  
Capital Expenditure ◽  
Impulse Response Function ◽  
Series Data ◽  
Vector Autoregressive ◽  
Long Run ◽  
Short Run

The paper empirically examines the dynamics of exports and economic growth in Nigeria using time series data for 1970 to 2017. The Vector autoregressive model (VAR) was used to investigate the long run and short run relationship between exports and economic growth as well as some selected variables. The result shows that there exists a stable long run relationship among economic growth, exports, capital expenditure on education and social services. Also, the Granger causality results reveal that export Granger causes economic growth and not the other way round. This means that an increase in economic growth may result from increase in export, but increase in economic growth does not necessarily lead to increase in exports. The Impulse Response Function (IRF) shows that a one standard innovation in exports will lead to permanent positive impact on economic growth in Nigeria. This therefore supports the exports led growth hypothesis for Nigeria.

scholarly journals Dynamics of Push and Pull Factors of Migrant Workers in Developing Countries: The Case of Indonesian Workers in Malaysia

2012 ◽  
Vol 4 (12) ◽  
pp. 703-711 ◽  
Author(s):  
Fariastuti Djafar
Keyword(s):  
Low Income ◽  
Migrant Workers ◽  
Time Series Data ◽  
Series Data ◽  
Pull Factors ◽  
Vector Autoregressive ◽  
Push And Pull Factors ◽  
Objective Time ◽  
Long Run ◽  
Push And Pull

Low income and high unemployment in labour sending countries and high income and low unemployment in labour receiving countries are frequently justified as push and pull factors of migrant workers, respectively. Indonesia is the main labour-exporting country to Malaysia but the studies on the push factors in Indonesia and the pull factors in Malaysia are very limited. This paper has three objectives. The first objective is to examine the long-run relationship among income and unemployment in Indonesia and Malaysia and the Indonesian migrant workers in Malaysia. This is followed by examining the causality between the variables in the second objective, and the extent to which income and unemployment in Indonesia and Malaysia determine the Indonesian migrant workers in Malaysia in the third objective. Time series data were employed and analysed by utilizing the Vector Autoregressive (VAR) framework. The findings show a long-run relationship among income and unemployment in Indonesia and Malaysia and the Indonesian migrant workers in Malaysia. Only unidirectional causality is found in the long-run, which is from income and unemployment in Indonesia and Malaysia to Indonesian migrant workers in Malaysia. The findings also show that the Indonesian migrant workers in Malaysia are significantly determined by income and unemployment, positively in the case of Indonesia, and negatively, in Malaysia.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shuo Sun ◽  
Mingchen Gu ◽  
Yingping Wang ◽  
Rongjie Lin ◽  
Lifeng Xing ◽  
...  
Keyword(s):  
Manufacturing Industry ◽  
Time Series Data ◽  
Traffic Volume ◽  
Series Data ◽  
Time Varying ◽  
Truck Traffic ◽  
Positive Interaction ◽  
Vector Autoregressive ◽  
Coupling Relationship ◽  
Johansen Cointegration Test

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.

scholarly journals Vector Autoregressive X (VARX) Modeling for Indonesian Macroeconomic Indicators and Handling Different Time Variations with Cubic Spline Interpolation

2019 ◽  
pp. 175-180
Author(s):  
Ayu Septiani ◽  
I Made Sumertajaya ◽  
Muhammad Nur Aidi
Keyword(s):  
Economic Growth ◽  
Interest Rates ◽  
Time Series Data ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Cubic Spline ◽  
Series Data ◽  
Cubic Spline Interpolation ◽  
Vector Autoregressive ◽  
Time Variations

This study discusses data handling that has different time variations (for example, data available in quarterly form but the desired data is monthly) in this case the GDP variable in the quarter series, while the other five variables use monthly series, whereas in multivariate analysis the data condition must be the same, then an approach is taken to reduce monthly data from quarterly data using the interpolation method. Therefore, before conducting the VARX analysis the author interpolated GDP data from the quarter to monthly by interpolation. After the data is ready, VARX modeling of the exchange rate, economic growth (GDP), interest rates on Bank Indonesia Certificates (SBI), and inflation as endogenous variables and US interest rates (FFR) and US inflation as exogenous variables. The purpose of this study is to implement and evaluate the performance of Cubic Spline interpolation methods for time series data that have different time variations. Build VARX models and predict exchange rates, economic growth (GDP), SBI interest rates, and inflation based on US interest rates (FFR) and US inflation with the best models. Meanwhile, the interpolation method used by researchers to estimate the monthly value of the GDP variable based cubic spline interpolation. Based on the AIC value of the smallest VARX model obtained at 240.6668 so the best model obtained is the VARX (4.0) model.

scholarly journals A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data

2013 ◽  
Vol 5 (8) ◽  
pp. 379-384
Author(s):  
Seuk Wai ◽  
Mohd Tahir Ismail . ◽  
Siok Kun Sek .
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Markov Switching ◽  
Stock Index ◽  
Smooth Transition ◽  
Series Data ◽  
Var Model ◽  
Economic Time Series ◽  
Vector Autoregressive ◽  
Economic Time

Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange.

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