A wide range of real-world multivariate time series encountered in practice exhibit two simultaneous and interacting seasonal cycles, for example hourly electricity demand, intraday financial prices, and sub-daily traffic volumes. Existing Bayesian f...
In this paper the theory on the estimation of vector autoregressive (VAR) models for I(2) processes is extended to the case of long VAR approximation of more general processes. Hereby the order of the autoregression is allowed to tend to infinity at...
Network vector AutoRegressive models play a vital role in multivariate time series analysis. However, previous research in the classic Network vector AutoRegressive (NAR) model is limited to strict assumptions of linearity and time-invariance of node...
The existing spatial panel structural vector auto-regressive model can effectively capture the time and spatial dynamic dependence of endogenous variables. However, the hypothesis that the common factors have the same effect for all spatial units is...
This paper proposes a score-driven Bayesian method to detect online mean change-points in a vector autoregressive model. The within-state process follows a multivariate autoregressive structure with time-varying autoregressive coefficient matrices an...
A state space model with an unobserved multivariate random walk and a linear observation equation is studied. The purpose is to find out when the extracted trend cointegrates with its estimator, in the sense that a linear combination is asymptoticall...
Coal consumption and carbon dioxide emissions from coal combustion in China are attracting increasing attention worldwide. Between 1990 and 2013, the coal consumption in Shandong Province increased by approximately 5.29 times. Meanwhile, the proporti...
Climate change refers to a statistically significant change in the average state of the climate or a climate alteration that lasts for a long period of time. Runoff (R) is as a measure of the interaction between climate change and human activities an...
In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is firs...
Earth’s gravitational field provides invaluable insights into the changing nature of our planet. It reflects mass change caused by geophysical processes like continental hydrology, changes in the cryosphere or mass flux in the ocean. Satellite...
Energy system models for the analysis of future scenarios are mainly driven by the set of energy service demands that define the broad outlines of socio-economic development throughout the model time horizon. Here, the long-term effects of the COVID-...
Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive int...
Based on the theory of new structural economics, this research aims to explore the dynamic correlation among high-quality development, environmental regulation structures, and property rights structures in China’s construction industry. A panel...
Under the influence of global climate change and urbanization processes, the number of available water resources (AWRs) in basins has become significantly more uncertain, which has restricted the sustainable development of basins. Therefore, it is im...
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Haze, due to biomass burning, is a recurring problem in Southeast Asia (SEA). Exposure to atmospheric particulate matter (PM) remains an important public health concern. In this paper, we examined the long-term seasonality of PM2.5 and PM10 in Singap...
The efficient development and widespread utilization of clean energy hold global significance, particularly for developing countries like China, which has committed to carbon peak and neutrality targets. In this context, the financial sector plays a...
To predict the variability of dam water levels, parametric Multivariate Linear Regression (MLR), stochastic Vector AutoRegressive (VAR), Random Forest Regression (RFR) and Multilayer Perceptron (MLP) Artificial Neural Network (ANN) models were compar...
This study focused on the dynamic relationships among industrial development, energy consumption, economic growth, and carbon emissions in China, with the goal of achieving long-term ecological sustainability. Using the Panel Vector Autoregressive (P...
The main aim of this paper is to review recent advances in the multivariate autoregressive index model [MAI] and their applications to economic and financial time series. MAI has recently gained momentum because it can be seen as a link between two p...
In this paper, we are interested in modeling the dynamics of cutaneous leishmaniasis (CL) in Errachidia province (Morocco), using epidemiologic data and the most notable climatic factors associated with leishmaniasis, namely humidity, wind speed, rai...
This paper proposes a class of partial cointegrated models allowing for structural breaks in the deterministic terms. Moving-average representations of the models are given. It is then shown that, under the assumption of martingale difference innovat...
The goal of this study is to detect linear and nonlinear causal pathways toward climate change as measured by changes in global mean surface temperature and global mean sea level over time using a data-based approach in contrast to the traditional ph...
Recently, awareness about the significance of water management has risen as population growth and global warming increase, and economic activities and land use continue to stress our water resources. In addition, global water sustenance efforts are c...
Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled...
This research models and forecasts daily AQI (air quality index) levels in 16 cities/counties of Taiwan, examines their AQI level forecast performance via a rolling window approach over a one-year validation period, including multi-level forecast cla...
Global warming and climate change, primarily driven by human activities, with agriculture playing a significant role, have become central topics of scientific research. Livestock production, especially enteric fermentation, is a major source of green...
Probability forecasts of the Swiss franc/euro (CHF/EUR) exchange rate are generated before, surrounding and after the placement of a floor on the CHF/EUR by the Swiss National Bank (SNB). The goal is to determine whether the exchange rate floor has a...
The clean development of China’s power supply structure has become a crucial strategic problem for the low-carbon, green development of Chinese society. Considering the subsistent developments of optimized allocation of energy resources and efficient...
It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary f...
The paper’s primary purpose is to better monitor shocks; therefore, reliable scientific methods should be used to predict, monitor, and implement those events. In this paper, tourism prices are studied as an economic,
We use a cointegrated structural vector autoregressive model to investigate the relation between monetary policy in the euro area and the stock market. Since there may be an instantaneous causal relation, we consider long-run identifying restrictions...
This paper aims to indicate the linkages between crude oil prices and selected food price indexes (dairy, meat, oils, cereals, and sugar) and provide an empirical specification of the direction of the impact. This paper reviews the fuel–food pr...
The Zero Emission Vehicle (ZEV) mandate by Canada’s federal government is a significant initiative towards achieving net zero emissions by 2050. In this context, to quantify the evolution scale of ZEVs alongside charging pile, a system dynamics...
This study investigates the relationship between financial development and economic growth in the Ivory Coast over the period from 1961 to 2014. The final goal of this research is to develop a procedure to identify the effects of financial reforms fo...
The present work aimed to examine the association between Corporate Social performance (CSP) and corporate financial performance (CFP) taking into account corporate social irresponsibility. Here, we used a sample of French non-financial firms listed...
Knowledge of causal relationships is fundamental for understanding the dynamic mechanisms of ecological systems. To detect such relationships from multivariate time series, Granger causality, an idea first developed in econometrics, has been formulat...
In this manuscript, we analyze a bivariate vector auto-regressive (VAR) model in order to draw the design principle of a timeseries with a controlled statistical inter-relationship. We show how to generate bivariate timeseries with given covariance a...
Understanding high-dimensional dependencies in modern financial systems requires time series models that capture both contemporaneous and dynamic linkages. This study develops a sparse spatio-temporal vector autoregressive framework to analyse the ne...
This article extends the Factor-Augmented Vector Autoregression Model (FAVAR) to mixed-frequency and incomplete panel data. Within the scope of a fully parametric two-step approach, the alternating application of two expectation-maximization algorith...
This study focusses on the transmission of carbon pricing mechanisms in shaping trade dynamics between the Eurozone and key partners: the USA and China. Using Bayesian variable selection methods and a Time-Varying Structural Vector Autoregressions (T...
This research aims to predict the efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand for the next 17 years (2020–2036) and analyze the relationships among causal factors by applying a str...
The number of wind-generating resources has increased considerably, owing to concerns over the environmental impact of fossil-fuel combustion. Therefore, wind power forecasting is becoming an important issue for large-scale wind power grid integratio...
Regional innovation is very important for the sustainable development of regional economies. Innovation development between regions is not independent, and there is spatial correlation in innovation spillover. This paper uses the total amount of regi...
This study examines the reaction of four major equity markets of the world to the US equity market fear index, i.e., the Chicago Board of Trade Volatility Index (VIX). The VIX is designed to perform as a leading indicator of the volatility in equity...
Global sea-level rise has been drawing increasingly greater attention in recent years, as it directly impacts the livelihood and sustainable development of humankind. Our research focuses on identifying causal factors and pathways on sea level change...
To improve the overall accuracy of tidal forecasting and ameliorate the low accuracy of single harmonic analysis, this paper proposes a combined tidal forecasting model based on harmonic analysis and autoregressive integrated moving average–sup...
This paper constructs a vector autoregressive (VAR) model and vector error correction (VECM) model, analyzes the air pollution, economic development, and national health of China from 1990 to 2019, and evaluates the economic losses from the respirato...
This research proposes an optimization model for the scheduling of power generation expansion in countries with high hydrological variability, to be implemented in the Colombian system for the 2027–2041 period. The method combines a Vector Auto...
In spite of the development of the Prognostics and Health Management (PHM) during past decades, the reliability prognostics of engineered systems under time-varying external conditions still remains a challenge in such a field. When considering the c...
Accurate time series prediction techniques are becoming fundamental to modern decision support systems. As massive data processing develops in its practicality, machine learning (ML) techniques applied to time series can automate and improve predicti...
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