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 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.

Visual Classification of Formula One Races by Events, Drivers, and Time Periods

2020 ◽  
pp. 101-114
Author(s):  
Tobias Lampprecht ◽  
David Salb ◽  
Marek Mauser ◽  
Huub van de Wetering ◽  
Michael Burch ◽  
...  
Keyword(s):  
Time Series Data ◽  
Series Data ◽  
Time Varying ◽  
Visual Metaphor ◽  
Web Based ◽  
Visual Classification ◽  
Formula One ◽  
Data Worth ◽  
Time Periods

Formula One races provide a wealth of data worth investigating. Although the time-varying data has a clear structure, it is pretty challenging to analyze it for further properties. Here the focus is on a visual classification for events, drivers, as well as time periods. As a first step, the Formula One data is visually encoded based on a line plot visual metaphor reflecting the dynamic lap times, and finally, a classification of the races based on the visual outcomes gained from these line plots is presented. The visualization tool is web-based and provides several interactively linked views on the data; however, it starts with a calendar-based overview representation. To illustrate the usefulness of the approach, the provided Formula One data from several years is visually explored while the races took place in different locations. The chapter discusses algorithmic, visual, and perceptual limitations that might occur during the visual classification of time-series data such as Formula One races.

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 The identification of temporal communities through trajectory clustering correlates with single-trial behavioural fluctuations in neuroimaging data

2019 ◽  
Author(s):  
William Hedley Thompson ◽  
Jessey Wright ◽  
James M. Shine ◽  
Russell A. Poldrack
Keyword(s):  
Time Series Data ◽  
Dynamic Network ◽  
Fmri Data ◽  
Series Data ◽  
Time Varying ◽  
Trajectory Clustering ◽  
Single Trial ◽  
Neuroimaging Data ◽  
New Perspective ◽  
Static Connectivity

AbstractInteracting sets of nodes and fluctuations in their interaction are important properties of a dynamic network system. In some cases the edges reflecting these interactions are directly quantifiable from the data collected. However, in many cases (such as functional magnetic resonance imaging (fMRI) data), the edges must be inferred from statistical relations between the nodes. Here we present a new method, Temporal Communities through Trajectory Clustering (TCTC), that derives time-varying communities directly from time-series data collected from the nodes in a network. First, we verify TCTC on resting and task fMRI data by showing that time-averaged results correspond with expected static connectivity results. We then show that the time-varying communities correlate and predict single-trial behaviour. This new perspective on temporal community detection of node-collected data identifies robust communities revealing ongoing spatiotemporal community configurations during task performance.

scholarly journals Environmental Pollution Effects of Regional Industrial Transfer Illustrated with Jiangsu, China

2021 ◽  
Vol 13 (21) ◽  
pp. 12128
Author(s):  
Guangxiong Mao ◽  
Wei Jin ◽  
Ying Zhu ◽  
Yanjun Mao ◽  
Wei-Ling Hsu ◽  
...  
Keyword(s):  
Environmental Pollution ◽  
Manufacturing Industry ◽  
Time Series Data ◽  
Estimation Method ◽  
Raw Material ◽  
Series Data ◽  
Pollution Load ◽  
Pollution Effects ◽  
Industrial Transfer ◽  
Southern Jiangsu

Industrial transfer is reshaping the geographic layout of industries and facilitating the transfer and spread of environmental pollution. This study employs the pollution transfer estimation method to discuss the environmental effect of industrial transfer. By compiling statistics on industries of a certain scale according to time-series data, the researchers compute the pollution load generated by industrial transfer and the difference in pollution emissions for each region and industry. Through the constructed evaluation model, the empirical scope is Jiangsu, which is the most developed industry in China. The results reveal that there is an apparent spatial hierarchy among the transferred industries in Jiangsu. Most industries transfer from the southern Jiangsu region toward the central Jiangsu and northern Jiangsu regions. Environmental pollution is redistributed among prefecture-level cities because of intercity industrial transfer; the spatial characteristics of pollution exhibit a notable hierarchical pattern. Furthermore, the transferred pollution load differs considerably between industries. The textile industry and chemical raw material and chemical product industry are mainly transferred toward the Central Jiangsu and Northern Jiangsu regions, whereas the papermaking and paper product manufacturing industry is primarily redistributed to the Southern Jiangsu region. The empirical results can serve as a reference for analyzing the environmental pollution effects of regional industrial transfer.

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 Is Fiscal Policy Effective in Generating Higher Real Output? A Case of Pakistan

2013 ◽  
Vol 1 (2) ◽  
pp. 47-58
Author(s):  
Hafiz Saqib Mehmood Najmi ◽  
Farrukh Bashir ◽  
Saman Maqsood
Keyword(s):  
Fiscal Policy ◽  
Time Series Data ◽  
Government Expenditure ◽  
Cointegration Test ◽  
Series Data ◽  
Government Revenue ◽  
Real Gdp ◽  
Long Run ◽  
Johansen Cointegration Test ◽  
Motivating Factor

Keeping in view the objective that is to observe the usefulness of fiscal policy on real GDP of Pakistan, the study collects time series data from 1976 to 2012 through reliable sources of statistical bureaus of Pakistan. Using Johansen Cointegration test, the long run results demonstrate investment and government expenditure as raising factor for real GDP of Pakistan while GDP Deflator and government revenue as de-motivating factor for real GDP of Pakistan in the long run.

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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