Autoregressive Research Articles

Long-Run Trade Relationship between the U.S. and Canada: The Case of the Canadian Dollar with the U.S. Dollar

This study investigates the long-run relationship between the U.S. dollar and the Canadian dollar by analyzing the bilateral exchange rate induced by nominal and real shocks. The methodology centers on a structural vector autoregressive (SVAR) model, including the analysis of impulse response and variance decomposition to account for the impact of nominal and real shocks on exchange rate movements. This study also decomposes real shocks into demand and supply factors from both Canada and the U.S. and compares their impacts on the nominal and real exchange rates. The results are compared to shocks driven by country-specific nominal factors. This study uses quarterly data from December 1972 to December 2023. The findings suggest that real shocks have a permanent impact on both the nominal and real exchange rates, compared to nominal shocks, which have a temporary impact. Country-specific real supply-side factors have a more significant impact than country-specific real demand-side factors. Country-specific nominal factors barely impacted the nominal and real exchange rates between the U.S. and Canada.

Tests for time-varying coefficient spatial autoregressive panel data model with fixed effects

Tests for time-varying coefficient spatial autoregressive panel data model with fixed effects

Forecasting the CBOE VIX and SKEW Indices Using Heterogeneous Autoregressive Models

We analyze the predictability of daily data on the CBOE VIX and SKEW indices, used to capture the average level of risk-neutral risk and downside risk, respectively, as implied by S&P 500 index options. In particular, we use forecast models from the Heterogeneous Autoregressive (HAR) class to test whether and how lagged values of the VIX and of the SKEW may increase the forecasting power of HAR for the SKEW and the VIX. We find that a simple HAR is very hard to beat in out-of-sample experiments aimed at forecasting the VIX. In the case of the SKEW, the benchmarks (the random walk and an AR(1)) are clearly outperformed by HAR models at all the forecast horizons considered and there is evidence that special definitions of the SKEW index based on put options data only yield superior forecasts at all horizons.

The Impact of Climate Change Factors on Financial Markets: Evidence from China

ABSTRACT This paper examines the impact of climate change factors, namely Climate Risk Index (CRI), Climate Policy Index (CPI), and Climate Concern Index (CCI), on China’s financial markets. By employing quantile vector autoregression model and conducting network analysis, this paper intends to reveal the dynamic relationship and the risk transmission mechanism between climate factors and various financial markets under different market conditions based on the data ranging from Nov.1, 2011 to December 31, 2021. We find that climate shocks intensify the risk contagion among China’s financial markets, and the impact induced by each factor would present heterogeneity. The results also indicate that the systemic importance of climate shock index varies across market conditions. The findings shed light on the complex interaction between climate shocks and financial markets, which should contribute to policy implications for financial market stability and risk prevention.

The general budget and its impact on the investment efficiency of the Iraqi economy for the period 2004-2022

In order to provide a stable and supportive environment for investment through public budget tools and variables, it is necessary to research methods that lead to stimulating investment operations and providing the necessary financing for economic activity. Total public revenues, total public expenditures, and the budget surplus or deficit have a significant impact on this. Total public revenues and public expenditures have a significant impact on the budget surplus or deficit, which in turn affects investment in the Iraqi economy. In the event of an increase in public revenues, such as an increase in oil or tax revenues, this will lead to an increase in the budget surplus. A higher budget surplus will provide the government with more financial resources to finance public investments and development projects. Achieving the appropriate environment accelerates economic growth and pushes investors to activate their activities, thus expanding their proceeds to form a development horizon and space that creates a broad developmental investment environment to demonstrate the investment opportunities available to investors. If public expenditures rise, such as increased spending on salaries and wages or government purchases, this will lead to an increase in the budget deficit. A high budget deficit will restrict the government's ability to allocate financial resources to finance public investments and development projects. This may negatively affect the investment climate and encourage investors to invest in the Iraqi economy.Therefore, it can be said that managing public revenues and expenditures in a balanced and effective manner has a significant impact on the budget surplus or deficit, which in turn directly affects investment levels in the Iraqi economy.The results of the standard model were obtained using the distributed slowdown autoregressive model (ARDL), which is considered one of the advanced standard methods, which relies on testing the stability of time series. This model gives results about the nature of the relationship in the short term (error correction model) as well as results for the long term. The research reached a set of conclusions, the most important of which is after testing the soundness and... Stability of the estimated model for the variables. The first step after testing the stability of the variables is to estimate the autoregressive distributed lag (ARDL) model for the function of total investments (Y) with lag periods (2) for the dependent variable (Y), the variable (X1) total public revenues as an independent variable, and the variable (X2) total Public expenditures are an independent variable, and the variable (X3) budget surplus is an independent variable. After conducting the model estimation process, we obtained the results for estimating the ARDL model, as the explanatory power of the estimated model was (R2 = 0.817), meaning that the independent variables included in the estimated model explain 81% of the changes in the variable. Follower. The estimated model also turned out to be significant, so the null hypothesis was rejected and the alternative hypothesis was accepted. Also testing the existence of a cointegration relationship between the variables of the model, i.e. the existence of a long-term equilibrium relationship, by testing the limits.

Scaled envelope models for multivariate time series

Vector autoregressive (VAR) models have become a popular choice for modeling multivariate time series data due to their simplicity and ease of use. Efficient estimation of VAR coefficients is an important problem. The envelope technique for VAR models is demonstrated to have the potential to yield significant gains in efficiency and accuracy by incorporating linear combinations of the response vector that are essentially immaterial to the estimation of the VAR coefficients. However, inferences based on envelope VAR (EVAR) models are not invariant or equivariant upon the rescaling of the VAR responses, limiting their application to time series data that are measured in the same or similar units. In scenarios where VAR responses are measured on different scales, the efficiency improvements promised by envelopes are not always guaranteed. To address this limitation, we introduce the scaled envelope VAR (SEVAR) model, which preserves the efficiency-boosting capabilities of standard envelope techniques while remaining invariant to scale changes. The asymptotic characteristics of the proposed estimators are established based on different error assumptions. Simulation studies and real-data analysis are conducted to demonstrate the efficiency and effectiveness of the proposed model. The numerical results corroborate our theoretical findings.

Trends in COPD severe exacerbations, and all-cause and respiratory mortality, before and after implementation of newer long-acting bronchodilators in a large population-based cohort

BackgroundLittle is known about the trends in morbidity and mortality at the population level that followed the introduction of newer once-daily long-acting bronchodilators for COPD. The purpose of the study was to evaluate whether the availability of new bronchodilators was associated with changes in the temporal trends in severe COPD exacerbations and mortality between 2007 and 2018 in the older population with COPD; and whether this association was homogeneous across sex and socioeconomic status classes.MethodsWe used an interrupted time-series and three segments multivariate autoregressive models to evaluate the adjusted changes in slopes (i.e., trend effect) in monthly severe exacerbation and mortality rates after 03/2013 and 02/2015 compared to the tiotropium period (04/2007 to 02/2013). Cohorts of individuals > 65 years with COPD were created from the nationally representative database of the Quebec Integrated Chronic Disease Surveillance System in the province of Quebec, Canada. Whether these trends were similar for men and women and across different socioeconomic status classes was also assessed.ResultsThere were 130,750 hospitalizations for severe exacerbation and 104,460 deaths, including 24,457 (23.4%) respiratory-related deaths, over the study period (928,934 person-years). Significant changes in trends were seen after 03/2013 for all-cause mortality (-1.14%/month;95%CI -1.90% to -0.38%), which further decreased after 02/2015 (-1.78%/month;95%CI -2.70% to -0.38%). Decreases in respiratory-related mortality (-2.45%/month;95%CI -4.38% to -0.47%) and severe exacerbation (-1,90%/month;95%CI -3.04% to -0.75%) rates were only observed after 02/2015. These observations tended to be more pronounced in women than in men and in higher socioeconomic status groups (less deprived) than in lower socioeconomic status groups (more deprived).ConclusionsThe arrival of newer bronchodilators was chronologically associated with reduced trends in severe exacerbation, all-cause and respiratory-related mortality rates among people with COPD > 65 years. Our findings document population benefits on key patient-relevant outcomes in the years following the introduction of newer once-daily long-acting bronchodilators and their combinations, which were likely multifactorial. Public health efforts should focus on closing the gap between lower and higher socioeconomic status groups.

Balancing quality and efficiency: An improved non-autoregressive model for pseudocode-to-code conversion

Pseudocode can efficiently represent algorithm logic, but manual conversion to executable code requires more time. Recent works have applied autoregressive (AR) models to automate pseudocode-to-code conversion, achieving good results but slow generation speed. Non-autoregressive (NAR) models offer the advantage of parallel generation. However, they face challenges in effectively capturing contextual information, leading to a potential degradation in the quality of the generated output. This paper presents an improved NAR model for balancing quality and efficiency in pseudocode conversion. Firstly, two strategies are proposed to address out-of-vocabulary and repetition problems. Secondly, an improved NAR model is built using linear smoothing and adaptive techniques in the transition matrix, which can mitigate the “winner takes all” effect. Finally, a new synthesis potential metric is proposed for evaluating pseudocode conversion. Experimental results show that the proposed method matches AR model performance while accelerating generation over 10-fold. Further, the proposed NAR model reduces the gap with the AR model in terms of the BLEU score on the EN-DE and DE-EN tasks of the WMT14 machine translation.

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.