Autoregressive Research Articles

Vietnam's exports to Korea and the real exchange rate: Post-crisis evidence from the multiple threshold nonlinear autoregressive distributed lag model

In this study, the asymmetric effects of the real exchange rate on Vietnam's exports to Korea were investigated using the multiple threshold nonlinear autoregressive distributed lag (MTNARDL) model. As a result, the MTNARDL model exhibited enhanced estimation accuracy compared to the single-threshold nonlinear autoregressive distributed lag (NARDL) model. Furthermore, the aggregate bias problem was solved using individual export data for 16 major industries instead of relying on the overall export value data. The results revealed the substantial asymmetric impact of real exchange rate fluctuations in the short and long run. Fluctuations in the real exchange rate boosted Vietnam's exports to the Republic of Korea in the long run, with minimal negative influence. This indicates that the depreciation of the Vietnamese dong against the United States (US) dollar encouraged exports in some industries but limited exports in others. However, the short-term effect of the real exchange rate remains uncertain, as it could be either negative or positive depending on the industry and threshold level. The diverse impacts of the real exchange rate fluctuations on exports across industries were attributed to factors such as the capacity to absorb foreign exchange risks, product heterogeneity, and the ease of adjusting inputs and production for each sector. Therefore, these findings may help policymakers develop Vietnam-specific policies to address long- and short-term changes in the threshold levels of real exchange rate fluctuations.

Energy price and returns: a threshold heteroscedasticity model approach

ABSTRACT The energy market is characterized by its intricate dynamics, influenced by various factors that result in erratic behavior, making it challenging to model energy price returns and volatility. In energy market, the changes in Brent and West Texas Intermediate (WTI) crude oil price yields and volatility are the focus of great interest and attention. Due to the influence of different periods and some economic factors, the fluctuation of crude oil price shows asymmetric characteristics. For exploring the asymmetric characteristics of energy market, we introduce the application of the threshold double autoregressive (TDAR) model to handle the complexities of energy price returns and volatility. The TDAR model is able to capture nonlinearities and regime shifts in the oil market, making it particularly suitable for understanding the inherent complexities in crude oil price movements. The empirical results implied that TDAR model demonstrates superior performance in capturing the intricate dynamics of crude oil prices compared to traditional asymmetric GARCH-type models, making it a valuable tool for market practitioners and policymakers. Our findings reveal the subtle movements in Brent and WTI prices and offer valuable insights for risk management and investment strategies, which provide an alternative for market practitioners and policymakers.

Evolution Characteristics of Heilongtan Spring Discharge and Its Response Law to Precipitation in Lijiang City, China

The contradiction between water supply and spring preservation issues is becoming increasingly apparent as Lijiang City develops. An investigation into the dynamic variations in the discharge rate of Heilongtan Spring in Lijiang City and the response law between the water level of the spring and precipitation is crucial for safeguarding the landscape water of Heilongtan Spring. This study employed linear regression analysis, Mann–Kendall (MK) mutation test, wavelet analysis, and vector autoregression (VAR) to examine the fluctuating pattern of the Heilongtan Spring discharge and the response of the Heilongtan Spring water level to precipitation in Lijiang City. Furthermore, the study discussed the influence of human activities on the alteration of Heilongtan Spring. The results indicate that the mean discharge rate of Heilongtan Spring is 0.94 m3/s, with an annual variation of 0.05 m3/s. The time series analysis reveals that the variation pattern of Heilongtan Spring discharge aligns with the precipitation trend in Lijiang City. Nevertheless, there is a distinction between the timing of the Heilongtan Spring discharge station point and the precipitation mutation point in Lijiang City. The significant primary cycle of spring discharge change occurs every 18 months, with a cycle length of 12 months. The vector autoregression (VAR) model demonstrates a lagged relationship between the water level of Heilongtan Spring and the precipitation in Lijiang City. Specifically, the water level of Heilongtan Spring has a four-month lag response to precipitation variability in Lijiang City. The results can provide a beneficial reference for preserving spring water and managing regional water resources.

Cryptocurrencies under climate shocks: a dynamic network analysis of extreme risk spillovers

Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets; however, in this area, climate shocks could be a new kind of impact factor. In this paper, a spillover network based on a time-varying parametric-vector autoregressive (TVP-VAR) model is constructed to measure overall cryptocurrency market extreme risks. Based on this, a second spillover network is proposed to assess the intensity of risk spillovers between extreme risks of cryptocurrency markets and uncertainties in climate conditions, economic policy, and global financial markets. The results show that extreme risks in cryptocurrency markets are highly sensitive to climate shocks, whereas uncertainties in the global financial market are the main transmitters. Dynamically, each spillover network is highly sensitive to emergent global extreme events, with a surge in overall risk exposure and risk spillovers between submarkets. Full consideration of overall market connectivity, including climate shocks, will provide a solid foundation for risk management in cryptocurrency markets.

Dynamic features and wind-resistant strategy of suspended monorail vehicle-track beam systems subjected to turbulent wind

Suspended monorail vehicles (SMV), hung beneath box beams with open bottoms, exhibit significant transverse wobble when exposed to turbulent winds, markedly impacting passenger comfort. This study presents an effective methodology for comprehensively evaluating the dynamical behavior of suspended monorail vehicle-track beam systems (SMVTBS) under wind action. First, based on the multi-rigid body dynamics theory and finite element method, an integrated vehicle-track beam interaction model is established. Then, by applying the auto-regressive (AR) linear filtering method, a turbulent wind velocity field with inherent correlations is adequately simulated through a stochastic process, which is later converted into wind force acting on both the track beam and vehicles. On this basis, the vibration responses of the SMVTBS with and without wind actions are compared, and the effects of the wind load on the vehicle-track beam dynamical behavior are revealed. Furthermore, to improve the wind-resistant performance of the SMV, several crucial design parameters are reasonably selected and effective wind-resistant strategies are proposed. Finally, the dynamic performance of the SMV is evaluated under wind action, and suitable train running speeds were recommended at different wind velocities to guarantee vehicle riding comfort. The research results can provide useful guidance for the design and operation of SMVTBS against crosswinds.

The Dynamic Relationship Between Exchange Rate Volatility and Stock Prices in the Egyptian Real Estate Market and the Moderating Effect of Interest Rates

Among different channels of the financial market, the exchange rates and stock markets play a vital role in the domestic and international markets. This study aims to explore the dynamic relationship between the exchange rate (USD/EGP) and the real estate sector listed companies of the Egyptian Stock Exchange (EGX) from 2013 to 2023. Additionally, the study examines the moderating effect of interest rates on the relationship. To achieve this, the study employs several econometric methods such as Augmented Dicky Fuller (ADF) test, Granger Causality test to determine the direction of the relationship, Vector Autoregressive (VAR) model, and more. The results demonstrate that a long-run relationship exists between the variables. Furthermore, there is a significant unidirectional causality that runs from the exchange rate to real estate stock prices; thus, fluctuations in the exchange rate can significantly affect the profitability of investments in real estate stocks. Therefore, investors should closely monitor the movements in the exchange rate level to predict changes in real estate stock prices. When including the interest rate as a moderator, the outcome changes to bidirectional causality, meaning that the exchange rate and real estate stock price movements affect each other at a 1% significance level. Hence, interest rate could be used as an instrument to control demand in both the stock market and foreign exchange market. Finally, it is recommended that policymakers use the monetary policy while considering this bidirectional relationship in the presence of interest rate as a moderating variable.

Refining Estimation of the Instantaneous Reproduction Number During Early Pandemic Stages: Addressing Case-Reporting Variability and Serial Interval Uncertainty.

During infectious disease outbreaks, estimates for the instantaneous reproduction number, R(t), are essential for understanding transmission dynamics. This study develops and analyzes new methodology to improve estimation of R(t) when observed case counts are subject to reporting patterns and available serial interval estimates are subject to uncertainty and non-representativeness. Specifically, we developed a Bayesian time-since-infection model with layers to adjust for reporting measurement error, integrate multiple candidate serial interval estimates, and estimate transmission with an autoregressive time-series model incorporating factors relevant to transmission. Additionally, we provide practical tools to identify reporting patterns and determine when to smooth case counts for more usable R(t) estimates. We evaluated model performance relative to widely adopted methodology by simulating outbreak data, finding improved R(t) estimation with the proposed methodology. We also used 2020 COVID-19 data to analyze transmission trends and predictors, identifying strong day-of-week and social distancing effects that subsequently reduced estimate volatility. In addition to new approaches for addressing serial interval uncertainty and incorporating transmission predictor information, this study provides an alternative approach for addressing case-reporting patterns without delaying detection or smoothing over relevant transmission signals. These tools and findings may be used or built upon for current and future outbreaks.

Causal fermion systems as an effective collapse theory

Abstract It is shown that, in the non-relativistic limit, causal fermion systems give rise to an effective collapse theory. The nonlinear and stochastic correction terms to the Schr"odinger equation are derived from the causal action principle. The dynamics of the statistical operator is described by a deterministic equation of Kossakowski-Lindblad form. Moreover, the quantum state undergoes a dynamical collapse compatible with the Born rule. The effective model has similarities with the continuous spontaneous localization model, but differs from it by a conservation law for the probability integral as well as a non-locality in time on a microscopic length scale~$\ell_{\min}$.

A Dynamic Impact Evaluation of the High-Quality Development of China’s Construction Industry Using the Panel Vector Autoregressive Model

A Dynamic Impact Evaluation of the High-Quality Development of China’s Construction Industry Using the Panel Vector Autoregressive Model

Image texture analysis of pellets made of lignocellulosic materials

This experiment aimed to find the relation between image texture features of pellets made of various lignocellulosic materials (wood, wheat straw, hay, giant miscanthus, prairie spartina, and giant knotweed) and their physico-mechanical properties (density, compressive energy, maximum compressive strength, modulus of elasticity). Using the Kruskal-Wallis's test, the effect of materials on these properties was examined. Texture features were derived from the grey-level co-occurrence matrix, grey-level run-length matrix, absolute gradient matrix, autoregressive model, and wavelet decomposition, resulting in 86 features, later reduced to 8 factors via explanatory factor analysis. These factors were used as predictors in regression models for physico-mechanical properties. The models for modulus of elasticity achieved R2adj values of 0.91–0.99 (except for hay and wood), compressive stress models achieved 0.65–0.99 (excluding hay and wood), compressive energy models ranged from 0.60 to 0.97 (excluding hay), and density models ranged from 0.56 to 0.97 (excluding wood). The study confirmed a significant correlation between material type, texture parameters and compression resistance, suggesting this method could monitor pellet quality in production.

Interplay of Foreign Direct Investment, Remittances, and Economic Growth: Insights from SAARC Countries

The current study focuses on finding and analyzing the nexus between remittances, foreign direct investment, and economic growth in SAARC countries. This study uses secondary balanced data of selected SAARC countries for the years 2000 to 2019. This study uses gross domestic product i.e., GDP, which serves as the proxy of economic growth, as the dependent variable. The core independent variables of this study include (FDI) foreign direct investment and remittances (RMTN). Additionally, gross capital formation (GCF) and carbon dioxide emissions (CO2) are the other two independent variables in this study. For the proxy for environmental degradation the study uses data of CO2 emissions. The data is collected from the world development indicators, WDI. The study focuses on how all the independent variables affect the growth of economy in selected SAARC countries which include Pakistan, India, Bangladesh, Sri Lanka, and Nepal. The study applies cross sectional dependence test, panel cointegration test, unit root tests, and PARDL (panel auto regressive distributed lag model), also called pooled mean group methodology, to obtain empirical estimates. The results of PARDL show that FDI, RMTN, and GCF are all positively and significantly related to GDP. This means that GDP or alternatively economic growth, is improving with rise in FDI, RMTN, and GCF in selected SAARC countries. The results show that CO2 emissions have a negative significant impact on GDP i.e., increase in pollution levels is harmful for the growth of the selected economies.

Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives

Particulate matter with a diameter of 2.5 microns or less (PM2.5) is a significant type of air pollution that affects human health due to its ability to persist in the atmosphere and penetrate the respiratory system. Accurate forecasting of particulate matter is crucial for the healthcare sector of any country. To achieve this, in the current work, a new time series ensemble approach is proposed based on various linear (autoregressive, simple exponential smoothing, autoregressive moving average, and theta) and nonlinear (nonparametric autoregressive and neural network autoregressive) models. Three ensemble models are also developed, each employing distinct weighting strategies: equal distribution of weight among all single models (ESME), weight assignment based on training average accuracy errors (ESMT), and weight assignment based on validation mean accuracy measures (ESMV). This technique was applied to daily PM2.5 concentration data from 1 January 2019, to 31 May 2023, in Pakistan’s main cities, including Lahore, Karachi, Peshawar, and Islamabad, to forecast short-term PM2.5 concentrations. When compared to other models, the best ensemble model (ESMV) demonstrated mean errors ranging from 3.60% to 25.79% in Islamabad, 0.81%–13.52% in Lahore, 1.08%–7.06% in Karachi, and 1.09%–12.11% in Peshawar. These results indicate that the proposed ensemble approach is more efficient and accurate for short-term PM2.5 forecasting than existing models. Furthermore, using the best ensemble model, a forecast was made for the next 15 days (June 1 to 15 June 2023). The forecast showed that in Lahore, the highest PM2.5 value (236.00 μg/m3) was observed on 8 June 2023. Other days also displayed higher and poor air quality throughout the 15 days. Conversely, Karachi experienced moderate PM2.5 concentration levels between 50 μg/m3 and 80 μg/m3. In Peshawar, the PM2.5 concentration levels were consistently unhealthy, with the highest peak (153.00 μg/m3) observed on 9 June 2023. This forecasting experience can assist environmental monitoring organizations in implementing cost-effective planning to minimize air pollution.

Oil-price forecasting based on ARIMA, exponential smoothing, and autoregressive neural network models

Oil prices have an impact on the global economy. Demand shocks, such as those caused by the recent COVID-19 epidemic, or supply shocks caused by production reductions by major global producers, result in sudden price changes. Oil industry has been collecting price data since the late 19th century. A time series of this magnitude allows for consistent analysis of price behavior forecasts following demand or supply shocks. Financial time series are sensitive to exogenous shocks. From this perspective, this work presents a comparative analysis of predictive crude oil prices scenarios, obtained from a historical series of average annual prices. Two approaches were used: first, a combination of classic strategies based on exponential smoothing, and ARIMA models. Second, an autoregressive neural network model. Both approaches are complementary when used for long-term forecasting of oil prices and show good response to volatile data. Therefore, we are able to present an alternative data analysis, in a field where there is a great amount of relevant historical series, using probabilistic and non-linear models in order to observe predictions and make more effective decisions.

Can environmental taxes and green technological investment ease environmental pollution in China?

This study examines the complex relationship between environmental taxation, green technology, green investment, real GDP, and environmental pollution by employing the Nonlinear autoregressive distributive lag model for China. The findings indicate that environmental tax policies have significant short- and long-term effects, with positive shocks leading to pollution reductions. The positive and negative shocks to green investment increase pollution levels in the long run. Moreover, the real GDP displays pollution reductions in the short run. The results provide valuable insights into China's environmental and economic situation and offer policy recommendations. These findings were confirmed using the Wald test. The findings emphasize China's need for stronger environmental taxes, green technology adoption, evaluated green investments, and sustainable economic growth to achieve cleaner production and environmental sustainability.

Macroeconomic effects of monetary policy in Japan: an analysis using interest rate futures surprises

AbstractWe estimate the effects of monetary policy on the aggregate economy in Japan during the last three decades when the effective lower bound (ELB) on interest rates was occasionally binding. We use monetary policy surprises from the interest rate futures market as the external instrument to identify monetary policy shocks in the vector autoregressive model. We find that monetary policy has been effective in Japan during the last three decades, and the effect was more persistent in the ELB regime than in the non-ELB regime. In a simulation exercise, we further show that a New Keynesian model with forward guidance can replicate our empirical finding.

Financial Markets Integration and the Consequences for Financial Stability

Financial integration, as part of the financial market union, implies closer interaction between global economies. The positive contribution of digitization can be found in the specialized literature, but it fails to provide an overview of the link between financial intermediation indicators and the use of digitization, taking into account the impact on the individual. In this sense, the motivation of this research consists in emphasizing therole of digital financial services in reducing poverty, having a beneficial impact on economic development and the economy welfare.The study relevance resides in capturing the Global Findex Index trend, by measuring the use of financial services every 3 years, starting in 2011. As a result, the research aims to analyze the financial intermediation degree, and, implicitly, digitization, based on a survey of 140 economies, using a Vector Autoregressive model, impulse response functions and the variance decomposition of the included macroeconomic variables.The research resultssummarize the fintech contribution in diminishingthe financial instability risks and outlining the policy implications for governments, benefits such as money laundering and reducing corruption.

Diagnostic analystics in the Bayesian vector autoregressive model

The vector autoregressive model is extensively employed in macroeconomics, finance, and the natural sciences. However, it often encounters the issue of over-parametrization, resulting in undesirable behavior within the model. In this paper, we utilize the Bayesian vector autoregressive model as a tool to tackle this problem. Additionally, we employ the Bayesian local influence method to detect possible extreme observations under the model. We use posterior inference to estimate related parameters and construct Bayesian perturbation schemes for priors, variance, and data perturbations. Bayesian local influence is conducted based on three objective functions: Bayes factor, ϕ divergence, and posterior mean. To demonstrate the effectiveness of the diagnostics, we conduct numerical simulations. In the real data example, we use US inflation, unemployment, and interest rate data to verify this method's effectiveness.

The Impact of Climate Change on Rice Production in Punjab: An Auto Regression Distributed Lag Model

The Impact of Climate Change on Rice Production in Punjab: An Auto Regression Distributed Lag Model

The Chronic Progressive Repeated Measures (CPRM) Model for Clinical Trials Comparing Change Over Time in Quantitative Trait Outcomes

Repeated measures analysis is a common analysis plan for clinical trials comparing change over time in quantitative trait outcomes in treatment versus control. Mixed model for repeated measures (MMRM) assuming an unstructured covariance of repeated measures is the default statistical analysis plan, with alternative covariance structures specified in the event that the MMRM model with unstructured covariance does not converge. We here describe a parsimonious covariance structure for repeated measures analysis that is specifically appropriate for longitudinal repeated measures of chronic progressive conditions. This model has the parsimonious features of the mixed effects model with random slopes and intercepts, but without restricting the repeated measure means to be linear with time. We demonstrate with data from completed trials that this pattern of longitudinal trajectories spreading apart over time is typical of Alzheimer’s disease. We further demonstrate that alternative covariance structures typically specified in statistical analysis plans using MMRM perform poorly for chronic progressive conditions, with the compound symmetry model being anticonservative, and the autoregressive model being poorly powered. Finally, we derive power calculation formulas for the chronic progressive repeated measures model that have the advantage of being independent of the design of the pilot studies informing the power calculations. When data follow the pattern of a chronic progressive condition. These power formulas are also appropriate for sizing clinical trials using MMRM analysis with unstructured covariance of repeated measures.

Goals and Pathways of Public Governance Contribution to Achieve Progress in the Quality of Life

This research examines the importance of effective public governance in maximising the well-being of citizens in EU member states. Our research strategy used an integrated modelling framework involving data mapping and an autoregressive model with distributed lags (ARDL) for 2012–2022 in the EU member countries. The results demonstrated that the World Governance Indicator (WGI) has a mixed impact on Quality of Life (QL). In the short term, the impact is positive if there is an increase in the level of the public governance indicator, and in the long term, the effect of public governance on the quality of life is negative due to the challenges associated with implementing reforms. Additionally, our results highlighted that, to improve citizens’ quality of life, policies should focus on increasing the Purchasing Power (PP) and Safety of Society (SS) as well as on issues related to Pollution (P) and the Cost of Living (CL). There are similarities between the long-term ARDL analysis estimate and the short-term one, with the latter pointing out that they will be able to have quick positive effects, while pollution and increased living costs have immediate adverse effects on quality of life.