Autoregressive Research Articles

Exploring the impact of oil revenue on Nigeria's economic growth: A non‐linear autoregressive distributed lag model

AbstractThe influence of oil proceeds on an economy remains a subject of debate among scholars. Most scholars agree that income from oil has direct effect on economic expansion. We explore the impact of revenue from oil on economic growth in Nigeria from 1986 to 2016. Data were collected from the Nigerian Central Bank and the World Data Indicators. We employed a non‐linear autoregressive distributive lag approach to analyse the data. The outcome exposes a direct and significant association between revenue from oil and GDP both in the short and long term. Trade openness exhibits a significant negative effect on GDP from both short‐ and long‐term perspectives. Gross capital formation directly influences GDP in the short and long run, with significant impacts primarily in the long term. The study suggests that revenue from oil has a direct and significant impact on the Nigerian economy during the period from 1981 to 2016. We recommend that the Nigerian government create a conducive environment for private investment to thrive, thereby sustaining the nation's growth potential. Additionally, the government should wisely utilise revenue from oil to revitalise non‐booming productive sectors in the economy, thus diversifying revenue sources.

Pure contagion vs. financial interconnection in the subprime crisis context: Short- and long-term dynamics

This paper examines the difference between pure contagion and financial interconnection by studying the U.S. and some American and Asian markets in the subprime crisis context. These markets are affected by the mortgage crisis, with data available from January 1, 2003 to December 30, 2011. The paper first identifies the turmoil period via the wavelet technique and adopts cointegration and Granger causality approaches by estimating vector autoregressive (VAR) and vector error correction models (VECM) models. Based on daily returns from stock market indices in five American countries (Mexico, Brazil, Canada, Argentina, and the U.S.) and eight Asian ones (Hong Kong, Japan, India, Indonesia, Malaysia, Singapore, Korea, and China), the results show eight cases of pure contagion and 10 cases of financial interconnection. In addition, there were high co-movements in the short term and low co-movements in the long term for financial interconnection cases. These findings have several implications for investors looking to diversify their portfolios internationally and for portfolio managers to expect and limit market risk. The results provide additional guidance to regulators and policymakers.

Systematic evaluation of generative machine learning capability to simulate distributions of observables at the large hadron collider

Monte Carlo simulations are a crucial component when analysing the Standard Model and New physics processes at the Large Hadron Collider. This paper aims to explore the performance of generative models for complementing the statistics of classical Monte Carlo simulations in the final stage of data analysis by generating additional synthetic data that follows the same kinematic distributions for a limited set of analysis-specific observables to a high precision. Several deep generative models are adapted for this task and their performance is systematically evaluated using a well-known benchmark sample containing the Higgs boson production beyond the Standard Model and the corresponding irreducible background. The paper evaluates the autoregressive models and normalizing flows and the applicability of these models using different model configurations is investigated. The best performing model is chosen for a further evaluation using a set of statistical procedures and a simplified physics analysis. By implementing and performing a series of statistical tests and evaluations we show that a machine-learning-based generative procedure can be used to generate synthetic data that matches the original samples closely enough and that it can therefore be incorporated in the final stage of a physics analysis with some given systematic uncertainty.

Examining contemporaneous and temporal associations of real-time suicidal ideation using network analysis.

Suicidal ideation arises from a complex interplay of multiple interacting risk factors over time. Recently, ecological momentary assessment (EMA) has increased our understanding of factors associated with real-time suicidal ideation, as well as those predicting ideation at the level of hours and days. Here we used statistical network methods to investigate which cognitive-affective risk and protective factors are associated with the temporal dynamics of suicidal ideation. The SAFE study is a longitudinal cohort study of 82 participants with current suicidal ideation who completed 4×/day EMA over 21 days. We modeled contemporaneous (t) and temporal (t + 1) associations of three suicidal ideation components (passive ideation, active ideation, and acquired capability) and their predictors (positive and negative affect, anxiety, hopelessness, loneliness, burdensomeness, and optimism) using multilevel vector auto-regression models. Contemporaneously, passive suicidal ideation was positively associated with sadness, hopelessness, loneliness, and burdensomeness, and negatively with happiness, calmness, and optimism; active suicidal ideation was positively associated with passive suicidal ideation, sadness, and shame; and acquired capability only with passive and active suicidal ideation. Acquired capability and hopelessness positively predicted passive ideation at t + 1, which in turn predicted active ideation; acquired capability was positively predicted at t + 1 by shame, and negatively by burdensomeness. Our findings show that systematic real-time associations exist between suicidal ideation and its predictors, and that different factors may uniquely influence distinct components of ideation. These factors may represent important targets for safety planning and risk detection.

Research on the synergy of the water–energy–food composite system in the Beijing–Tianjin–Hebei region

ABSTRACT Water, energy, and food are interdependent and mutually restrictive, and changes in any one resource may have an impact on the others. In order to promote the sustainable utilization of resources, it is important to study the synergy of the water–energy–food composite system (WEFCS). Taking the Beijing–Tianjin–Hebei region as an example, this study adopts the composite system synergy degree model to quantitatively measure the synergy degree of WEFCS from 2011 to 2021, then constructs an evolutionary model to identify the interactions between the subsystems, and finally analyzes the interactions dynamically by using the panel vector autoregression model. The results show that the WEFCS synergy level in the Beijing–Tianjin–Hebei region is generally basic and shows low stability during the study period. Regional subsystems are more dependent than collaborative or competitive. Moreover, the regional water subsystem has a short-term positive impact on the energy subsystem, the energy subsystem has a short-term negative impact on the food subsystem, and the food subsystem has a delayed and complex influence on the water and energy subsystems, with the water subsystem being particularly affected. This study provides a decision-making basis for policymakers to optimize resource allocation.

Trend Analysis of Tourist-arrivals in Nepal Using Auto- Regressive Integrated Moving Average (ARIMA) Model

Nepal, a diverse and geographically varied landlocked country with a rich tapestry of cultures, and natural heritages, boasts iconic attractions such as Mt. Everest as the world’s highest peak, Lumbini as the birthplace of Lord Buddha, Pashupatinath Temple as one of the world’s most celebrated pilgrimages for the Hindus, Chitwan National Park, home to the one-horned rhino enlisted by United Nations Educational, Scientific and Cultural Organization world’s natural treasure, etc. Attracting tourists from all corners of the globe, Nepal’s unique blend of landscapes and cultural heritage has made this country a prominent destination. This research delves into a comprehensive trend analysis of tourist arrivals in Nepal from 1964 to 2023, employing the Auto Regressive Integrated Moving Average (ARIMA) model based on the secondary data employed from the Ministry of Finance, Government of Nepal. The model provides insights into past trends and forecasts future tourist arrivals, evidenced by the rigorous test using the Root Mean Square Error. The analysis employed several ARIMA models, including ARIMA (2, 1, 3), ARIMA (2, 1, 2), ARIMA (3, 1, 3), and ARIMA (3, 1, 2), with model performance assessed using criteria such as Akaike Information Criterion (AIC) and Mean Absolute Error (MAE). Findings indicate that the ARIMA (2 1, 3) model provides the most accurate forecasts compared to the other models tested. This finding has significant implications for Nepal’s tourism industry. The results underscore the importance of adopting flexible and strategic planning approaches to enhance the resilience of Nepal’s tourism sector. The study’s insights are valuable for policymakers, industry stakeholders, and researchers, contributing to the development of targeted strategies that ensure sustainable growth in the face of ongoing volatility.

The Relationship between Renewable Energy Consumption and Economic Growth: Insights from Iceland and Azerbaijan

Iceland leads globally in per capita green energy production, while oil-dependent Azerbaijan endeavors to transition its energy policy towards sustainable alternatives. Investigating the impacts of renewable energy consumption on the economy represents a significant contemporary scientific inquiry. The objective of this research is to empirically examine the causal relationship between GDP per capita, a key metric of economic growth, and renewable energy consumption, a fundamental aspect of the green economy. In this research, the Toda-Yamamoto causality test framework of the vector autoregressive (VAR) model was utilized to examine the causal relationship between the variables. The outcomes of this investigation reveal a significant causality between renewable energy consumption and economic growth in Iceland and Azerbaijan.

Modelling the Relationship between Air Pollution and Economic Growth in Somalia

Throughout human history, there has been a clear connection between pollution and the economy. However, the relationship between environmental harm and economic progress is complex, and disagreements between various academic disciplines have made it difficult to understand this phenomenon fully. A unit root test was conducted to gain more insight, which proposed the possibility of further empirical analysis. The Johansen cointegration method was also used to show a long-term connection between air pollution and economic growth. At the same time, the vector autoregressive model (VAR) indicated a short-term connection between the two. A Vector Error Correction Model (VECM) was then employed to estimate the relationship, and the findings confirmed the existence of the Environmental Kuznets Curve (EKC). The EKC demonstrates that economic growth has positive and negative effects on air pollution. The Granger causality test conducted in Somalia showed a causal link between economic growth and air pollution. The EKC model further supported this finding by demonstrating that economic growth has a statistically significant positive impact on greenhouse gas (GHG) emissions. This implies that Somalia has seen increased GHG emissions, leading to environmental degradation. Therefore, the Somali government must prioritise the implementation of a sustainable energy policy to improve air quality and prevent further environmental deterioration.

Spillover Effects between Oil, Gold, Stock, and Exchange Rate Returns in Thailand: An Extended Joint Connected TVP-VAR Approach

The objective of this study is to analyze the spillover effects among the returns of oil, gold, the stock market, and exchange rates in Thailand. Using the time-varying parameter vector autoregression model (TVP-VAR) with extended joint connectedness and data from April 2002 to March 2024, our analysis reveals a moderate level of the dynamic linkage among these returns. We observe that the dynamic connectedness among all returns varies over time, influenced by global economic events and country situations. Notably, in Thailand's landscape, stock market and gold returns act as net shock transmitters, with the stock market exhibiting the highest volatility among all variables, while oil and exchange rate markets function as net recipients. These insights significantly contribute to understanding asset and commodity markets and offer valuable policy implications for effectively managing these markets.

A zero-inflated Poisson integer-valued autoregressive model with time-varying coefficients covariates

For integer-valued time series, we may be interested in how a covariate affects the series. In the existing models, it is commonly assumed that the coefficients of the covariates are constant, and the innovation process follows a Poisson distribution. However, in practice, the influence of covariates may vary with time, and there may be a large number of zeros. To address this situation, this paper proposes an integer-valued autoregressive model with time-varying coefficients on covariates, incorporating a zero-inflated Poisson innovation process. The binomial thinning parameters and unknown functions of the proposed model are estimated based on approximate conditional least squares and the cubic spline method. The consistency and asymptotic properties of the binomial thinning parameters are investigated. Simulation studies and real data analysis support the flexibility and good performance of the proposed model.

Two-stage algorithm of spectral analysis for automatic speech recognition systems

Within the framework of a dynamically developing direction of research in the field of acoustic measurements, the task of spectral analysis of speech signals in automatic speech recognition systems is considered. The low efficiency of the systems in unfavorable speech production conditions (noise, insufficient intelligibility of speech sounds) compared to human perception of oral speech is noted. To improve the efficiency of automatic speech recognition systems, a two-stage algorithm for spectral analysis of speech signals is proposed. The first stage of speech signal processing consists of its parametric spectral analysis using an autoregressive model of the vocal tract of a conditional speaker. The second stage of processing is the transformation (modification) of the obtained spectral estimate according to the principle of frequency-selective amplification of the amplitude of the main formants of the intra-periodic power spectrum. The software implementation of the proposed algorithm based on the high-speed computational procedure of the fast Fourier transform is described. Using the author’s software, a full-scale experiment was carried out: an additive mixture of vowel sounds of the control speaker’s speech with white Gaussian noise was studied. Based on the results of the experiment, it was concluded that the amplitude of the main speech signal formants were amplified by 10–20 dB and, accordingly, a significant improvement in the speech sounds intelligibility. The scope of possible application of the developed algorithm covers automatic speech recognition systems based on speech signal processing in the frequency domain, including the use of artificial neural networks.

On autoregressive processes with Lindley-distributed innovations: modeling and simulation

In this paper, we develop an autoregressive process of order one, assuming that the innovation random variable has a Lindley distribution. The key properties of the process are investigated. Five distinct estimation techniques are used to estimate the parameters and simulation studies are conducted. The stationarity of the process is tested using a unit root test. The application of the proposed process to the analysis of time series data is demonstrated using real data sets. Based on some important statistical measures, the analysis of the data sets reveals that the proposed model fits well, and the errors are independent and Lindley-distributed.

Exploring the usefulness of the INLA model in predicting levels of crime in the City of Johannesburg, South Africa

Crime prediction serves as a valuable tool for deriving insightful information that can inform policy decisions at both operational and strategic tiers. This information can be used to identify high-crime areas, and optimise resource allocation and personnel management for crime prevention. Traditionally, techniques such as the Poisson model and regression analysis have been widely used for crime prediction. However, recent statistical advancements have introduced Integrated Nested Laplace Approximations (INLA) as a promising alternative for spatial and temporal data analysis. This study focuses on crime prediction using the INLA model. Specifically, the first-order autoregressive model under the INLA modelling framework is employed on longitudinal data for crime predictions in different regions of the City of Johannesburg, South Africa. The model parameters and hyperparameters considering space and time are estimated through the INLA model. In this work, the suitability and performance of the INLA model for crime prediction is assessed, which effectively captures spatial and temporal patterns. This study contributes to research by first introducing a novel approach for South African crime prediction. Secondly, it develops a model using no demographic information other than clustering attributes as an exogenous variable. Thirdly, it quantifies prediction uncertainty. Finally, it addresses data scarcity through demonstrating how INLA can provide reliable crime predictions, where conventional methods are limited. Based on our findings, the INLA model ranked areas by crime levels, obtaining a 29.3% Mean Absolute Percentage Error (MAPE) and 0.8 R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} value for crime predictions. These findings and contributions presents the potential of INLA in advancing evidence-based decision-making for crime prevention.

Debt erosion: Asymmetric response to demand and supply shocks

This paper explores the effect of inflation supply and demand shocks on government debt for a sample of 32 advanced economies and 24 emerging markets from 1970 to 2022. The shocks are identified using a sign-restricted structural vector autoregression model with quarterly data. Estimations of dynamic panel regressions and local projections suggest that supply shocks lead to persistent increases in government debt, while demand shocks result in long-lasting declines. Furthermore, high debt levels amplify the impacts of both supply and demand shocks by more than three times. Specifically, supply shocks contribute to an increase in debt through elevated borrowing costs and prolonged depreciation, whereas demand shocks erode debt through persistent improvements in the primary balance, driven by increased revenues.

Synergistic impacts of technological advancement and environmental hazards on social change and human well-being in South Asia

Sustainable Development Goals (SDGs) and the challenges in attaining these goals are always an eye-catching research theme for academics, politicians, and stakeholders around the world. Subjective well-being (SWB) is one of the central SDGs but is under-researched so far. Addressing this concern is crucial to maintaining societal welfare and promoting the realization of a sustainable future. Hence, this study explores the determinants of SWB with a particular focus on technological advancement and environmental damage. The paper examines SWB determinants in six South Asian nations from 2005 to 2022 using the cross-sectional augmented auto-regressive distributed lag model. The conclusions demonstrated that technology advancement improved SWB in the long term. Meanwhile, environmental deterioration has no appreciable short-term effects and harms SWB in the long term. This study adds to the field by examining how technological advancement and environmental deterioration explain SWB in South Asian economies. The study recommends policies that aim at improving innovations and reducing CO2 emissions.

Dynamics of Health Expectancy: An Introduction to the Multiple Multistate Method (MMM)

Many studies have compared individual measures of health expectancy across older populations by time-invariant characteristics. However, very few have included time-varying variables when calculating health expectancy. Even among older adults, socioeconomic and demographic characteristics are likely to change over the life course, and these changes may have substantial implications for health outcomes. This paper proposes a multiple multistate method (MMM) that situates the multistate model within the broader family of vector autoregressive models. Our approach allows the incorporation of the coevolution of multiple life course factors and provides a flexible yet simple way to model two or more time-varying variables with the multistate model. We demonstrate the MMM in two empirical applications, showing the flexibility of the approach to explore health expectancies with complex state spaces.

Robust estimation for semiparametric spatial autoregressive models via weighted composite quantile regression

In this article, the robust estimation for a class of semiparametric spatial autoregressive models has been investigated. By combining the QR decomposition technique for matrix and the weighted composite quantile regression method, we propose a robust estimation procedure for the parametric and non parametric components. Under certain regularity conditions, asymptotic properties of the resulting estimators are proved. Several simulation analyses have been conducted for further illustrating the performance of the proposed method, and the simulation results demonstrate that the proposed method improve the robustness of the models.

Syndromic Surveillance in Public Health Emergencies: A Systematic Analysis of Cases Related to Exposure to 2023 Floodwaters in Romagna, Italy.

In May 2023, Romagna, Italy, faced a devastating flood resulting in 16 fatalities, forced displacement of 26,000 citizens, and significant economic losses. Due to potential water contamination, implementing public health strategies became imperative for the Local Health Authority to mitigate the health consequences, analyze the flood's impact on the local population's health, and detect early anomalies requiring timely public health interventions. Between June and July 2023, general practitioners who were part of the RespiVirNet surveillance network completed weekly structured forms. These forms collected data on individuals exposed or not to floodwaters and clinical syndromes. Rates per 1000 resident population aged > 14 were stratified by district, week of observation, and symptomatology. Missing data were addressed by imputation using second-order autoregressive modeling. An incidence of 3.52 syndromes potentially related to flood water exposure per 1000 individuals (95% CI 2.82-4.35) was estimated. Ravenna, the city most affected by the flood, recorded the highest rate (6.05 per 1000, 95% CI 4.59-7.82). Incidence decreased in the weeks post-event. Anxiety, or trauma and stress symptoms, exhibited higher rates among the exposed, diminishing over weeks. The incidence for the non-exposed (12.76 per 1000, 95% CI 10.55-15.29) showed no significant territorial differences compared to the exposed ones. Syndromic surveillance provided timely information on the flood's health impact, revealing a higher incidence of individual syndromes among the non-exposed. This study contributes to guiding the implementation of future public health preparedness and response strategies for populations facing similar natural disasters.

Prediction of COVID-19 cases using SIR and AR models: Tokyo-specific and nationwide application

With fast infectious diseases such as COVID-19, the SIR model may not represent the number of infections due to the occurrence of distribution shifts. In this study, we use simulations based on the SIR model to verify the prediction accuracy of new positive cases by considering distribution shifts. Instead of expressing the overall number of new positive cases in the SIR model, the number of new positive cases in a specific region is simulated, the expanded estimation ratio is expressed in the AR model, and these are multiplied to predict the overall number. In addition to the parameters used in the SIR model, we introduced parameters related to social variables. The parameters for the simulation were estimated daily from the data using approximate Bayesian computation (ABC). Using this method, the average absolute percent error in predicting the number of positive cases for the peak of the eighth wave (2022/12/22–12/28) for all of Japan was found to be 62.2% when using data up to two months before the peak and 6.2% when using data up to one month before the peak. Our simulations based on the SIR model reproduced the number of new positive cases across Japan and produced reasonable results when predicting the peak of the eighth wave.

Promoting human-centered manufacturing through Lean Ergonomics – a structural equation model for ergonomics and management data

ABSTRACT The Stress-Strain Concept (SSC) describes central mechanisms in Lean Ergonomics (LE) and Human Factors Engineering (HFE). The SSC can create additional value through content and methodological enrichment based on current economic, technological, and demographic constraints. This article uses the SSC to explore mechanisms of interactions of productivity and ergonomics in empirical shop floor data. Ergonomic (Borg, NASA-Task Load Index (N-TLX), Ergonomic Assessment Worksheet (EAWS), Health, Age) and business data (normalized and absolute standard deviations of work process times) were collected for 24 manual work processes operating large-scale chemical reactors. Data is analyzed using several Structural Equation Models (SEM). Stress (EAWS) and – depending on Health – Physical Strain with a model quality of Adjusted Goodness-of-Fit ≥0.9 were identified as statistically significant regressors for time data. In addition, carry-over effects from ergonomics to productivity were identified in an autoregressive model. Fit indices are in the good to acceptable range.