Autoregressive Modeling Approach Research Articles

Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations.

The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary auditory pathway. Due to technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. Advances in ultrahigh-field functional magnetic resonance imaging (fMRI) acquisition have enabled novel noninvasive investigations of the human auditory subcortex, including fundamental features of auditory representation such as tonotopy and periodotopy. However, functional connectivity across subcortical networks is still underexplored in humans, with ongoing development of related methods. Traditionally, functional connectivity is estimated from fMRI data with full correlation matrices. However, partial correlations reveal the relationship between two regions after removing the effects of all other regions, reflecting more direct connectivity. Partial correlation analysis is particularly promising in the ascending auditory system, where sensory information is passed in an obligatory manner, from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli. While most existing methods for learning conditional dependency structures based on partial correlations assume independently and identically Gaussian distributed data, fMRI data exhibit significant deviations from Gaussianity as well as high-temporal autocorrelation. In this paper, we developed an autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system while appropriately accounting for autocorrelations between successive fMRI scans. Our results show strong positive partial correlations between successive structures in the primary auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. These results are highly stable when splitting the data in halves according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. In contrast, full correlation-based analysis identified a rich network of interconnectivity that was not specific to adjacent nodes along the pathway. Overall, our results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.

Uncertainty dynamics in energy planning models: An autoregressive and Markov chain modeling approach

This paper deals with the modeling of stochastic processes in long-term multi-stage energy planning problems when limited information is available about the distributions of such processes. Starting from simple estimates of variation intervals for uncertain parameters, such as energy demands and costs, we model the temporal correlation of these parameters through carefully constructed autoregressive (AR) models that respect the intervals defined in each period. We introduce a coefficient for “zigzag” effects in the evolution of uncertain processes, which controls the correlation across periods and also the likelihood of extreme scenarios. To preserve the convexity of the stochastic problem, we discretize the AR models associated with the cost parameters involved in the objective function by Markov chains. The resulting formulation is then solved using a Stochastic Dual Dynamic Programming (SDDP) algorithm available in the literature that handles finite-state Markov chains. Our numerical experiments, carried out on the Swiss energy system, show a very desirable adaptation strategy of investment decisions to uncertainty scenarios, a behavior that is not observed when the temporal correlation is ignored. Moreover, the solutions lead to better out-of-sample cost performances, especially on extreme scenario realizations, than the non-correlated ones which usually result in overcapacities to protect against high, but unlikely, parameter variations over time.

Sensor event sequence prediction for proactive smart home: A GPT2-based autoregressive language model approach

We propose a framework for predicting sensor event sequences (SES) in smart homes, which can proactively support residents’ activities and alert them if activities are not completed as intended. We leverage ongoing activity recognition to enhance the prediction performance, employing a GPT2-based model typically used for sentence generation. We hypothesize that the relationship between ongoing activities and SES patterns is akin to the relationship between topics and word sequence patterns in natural language processing (NLP), enabling us to apply the GPT2-based model to SES prediction. We empirically evaluated our method using two real-world datasets in which residents performed their usual daily activities. Our experimental results demonstrates that the use of the GPT2-based model significantly improves the F1 value of SES prediction from 0.461 to 0.708 compared to the state-of-the-art method, and that leveraging knowledge on ongoing activity can further improve performance to 0.837. Achieving these SES predictions using the ongoing activity recognition model required simple feature engineering and modeling, yielding a performance rate of approximately 80%.

On Generalized Additive Models for Representation of Solar EUV Irradiance

AbstractIn the context of space weather forecasting, solar EUV irradiance specification is needed on multiple time scales, with associated uncertainty quantification for determining the accuracy of downstream parameters. Empirical models of irradiance often rely on parametric fits between irradiance in several bands and various solar indices. We build upon these empirical models by using Generalized Additive Models (GAMs) to represent solar irradiance. We apply the GAM approach in two steps: (a) A GAM is fitted between FISM2 irradiance and solar indices F10.7, Revised Sunspot Number, and the Lyman‐α solar index. (b) A second GAM is fit to model the residuals of the first GAM with respect to FISM2 irradiance. We evaluate the performance of this approach during Solar Cycle 24 using GAMs driven by known solar indices as well as those forecasted 3 days ahead with an autoregressive modeling approach. We demonstrate negligible dependence of performance on solar cycle and season, and we assess the efficacy of the GAM approach across different wavelengths.

Economic and governance drivers of global remittances: a comparative study of the UK, US, and UAE to India

PurposeThe paper aims to analyse the impact of economic and governance factors on remittance inflows to India from the UK, USA and UAE. India is globally recognised as the largest recipient of remittances.Design/methodology/approachUsing a comprehensive time series data set spanning 1996 to 2022, the authors use an innovative non-linear autoregressive distributed lag model approach to examine the influence of economic growth, corruption control and employer availability in the three source countries on remittance inflows to India.FindingsThe results indicate that in the UAE, changes in economic growth and corruption control directly affect remittance outflows. However, the presence of employers in the UAE has minimal impact on remittance outflows to India. Regarding the UK, fluctuations in economic growth primarily drive remittance outflows to India. The effect of corruption control and employment opportunities on remittance outflows is marginal. In the USA, economic growth does not notably impact remittance outflows, whereas corruption control and employment opportunities significantly influence the outflows to India.Originality/valueThese findings have important implications for policymakers. Analysing macroeconomic factors from key remittance-sending nations offers valuable insights for Indian policymakers and their international counterparts to enhance remittance inflows. The study focuses on three countries that collectively contribute to about 50% of India's remittances, providing a unique contribution compared to the usual country-specific or regional focus in existing literature. Finally, leveraging these findings, NITI Aayog, an organisation dedicated to achieving India's sustainable development goals, can effectively monitor macroeconomic indicators related to significant remittance-sending countries.

Do mineral imports increase in response to decarbonization indicators other than renewable energy?

While previous studies examine the impact of renewable energy generation capacity on mineral imports where minerals are broadly defined, this study examines the role of three decarbonization indicators (energy efficiency, energy's carbon footprint, and renewable generation) in driving import demand for four specific minerals used in multiple decarbonization technologies: copper, aluminum, nickel, and manganese. Our results, based on the cross-sectional autoregressive distributed lag modeling approach applied to panel data from 33 countries, suggest that imports for some minerals increase in response to (1) investments in energy efficiency (aluminum and manganese), (2) reducing energy's carbon footprint (copper), and (3) investments in renewable energy generation (copper and manganese). Nickel imports were found to increase with higher levels of energy use per GDP suggesting their role in cleaner vehicles, which heavily rely on electrification, higher electricity use, and urbanization. When imports are broadly defined to include all types of mineral groups, import values were found to be responsive to higher renewable generation but not the other decarbonization indicators. Our findings provide insights into developing mineral-specific strategies and policy frameworks to ensure supply chain resilience.

Nexus Between Pension Funds and Stock Market Sustainability Development in Nigeria

Objective: It has long been recognized that pension systems across the emerging markets including Nigeria have undergone dramatic reforms over the last twenty-eight years. This process has mainly been fueled by the demographic problem faced by modern financial systems. Hence, this study examines the short-run and long-run dynamic of pension funds on capital market development in Nigeria using ex-post facto research design. Method: Data were collected from the Central Bank of Nigeria (CBN) statistical bulletin and annual report of the pension fund commission from 1995 to 2022. Data collected were analyzed using descriptive statistics, unit root test and auto regressive lag model (ARDL) and Toda Yamamoto multivariate granger causality approach. The unit root test reveal that all the variables used in the study are non-stationary at first difference except inflation which is stationary at level. Results: The findings from the ARDL analysis reveal a short-run dynamic impact between pension funds and capital market development due to the coefficient of error correction mechanism (speed of adjustment) which is negative and significant at 5% level of significant. The result suggests that over 22% of the short run disequilibrium is corrected in the long run equilibrium. However, the results from the long-run dynamic impact reveal a positively significant impact between the proxies of pension funds (pension contributory fund, pension investment) and control variable - inflation to be positively significant with capital market development proxied by market capitalization as a ratio to GDP at 5% level of significant. The results of the Toda Yamamoto granger causality results suggest a bidirectional relationship between pension funds (pension contributory fund, pension investment) and market capitalization as a ratio of Gross Domestic Product (GDP) in Nigeria at aggregate level. This suggest that the relationship between pension funds (pension contributory fund, pension investment) and market capitalization as a ratio of Gross Domestic Product (GDP) in Nigeria was two-ways. Conclusion: The study concludes that pension fund administrators in Nigeria should understand that the rate of inflation is dynamic in Nigeria and the value of money is being lost as money is not worth it values in the next five years.

Impact of Digital Finance, Tax Policy, Economic Expansion and Urbanization on Carbon Emission in the Context of Pakistan

The objective of this study is to investigate the impact of digital finance, tax policy, economic expansion, and urbanisation on carbon emissions. To fulfil the objective of this study, an autoregressive distribution lag model approach is used on the time series data from Pakistan. According to the results of this study, there is a long-term as well as a short-term relationship in the model. In this regard, authorities focus on tax policies and improvements in digital financial transactions that help reduce carbon emissions. Moreover, this study also helps identify which of the influences has a stronger impact on reducing carbon emissions.

A multiplicative Holt–Winters model and autoregressive moving-average for hyponatremia mortality rates

This study presents the effective utilization of data analysis in efficiently managing medical resources — a crucial factor in ensuring high-quality healthcare delivery and positive patient outcomes. We employ a multiplicative Holt-Winters model and a linear model with an autoregressive moving-average time series error term and analyze time series mortality rates associated with hyponatremia, a condition that leads to a systemic electrolyte imbalance. The time series data consistently reveals noticeable trends and recurring seasonal patterns. To address these intricate patterns, the proposed data analysis approaches adeptly incorporate these features, thereby yielding relatively accurate point forecasts and reliable forecast intervals. Beyond mere forecasting, the autoregressive moving-average model approach offers insights into the overarching trend and facilitates the comparison of seasonal components. One of the approaches is well-suited for the specific time series related to hyponatremia and holds the potential for extension to incorporate more complex trends beyond linearity. The research demonstrates the synergistic relationship between data analysis and medical expertise, working in tandem to achieve the optimal allocation of medical resources. We strongly advocate for the routine integration of statistical analyses as an essential practice to foster this harmonious synergy.

Does transport infrastructure make South Asian economies growth more inclusive? An application of a new transportation infrastructure index

Inadequate transport infrastructure in South Asian economies is one of the critical factors as this phenomenon hinders market competition due to the lack of goods transportation from urban to rural areas or vice versa. Given this, we solve the plausible research question of whether transport infrastructure development is adequate for inclusive growth in South Asian countries. This study contributes to constructing inclusive growth and transport infrastructure indices. However, this study measures transport infrastructure's impact on inclusive growth by applying the cross-sectional auto-regressive distributed lag model (CS-ARDL) approach from 1990 to 2020. Our empirical findings confirm that transport infrastructure significantly elevates inclusive growth by declining income inequalities in South Asian Economies in both the short and long runs. Finally, the two-way fixed effect with Driscoll and Ķraay standard error (DKSE), the dynamic ordinary least square (DOLS) and the fully modified ordinary least square (FMOLS) techniques are applied for the findings' robustness test. The results from the CS-ARDL method are consistent with the outcomes found from these three alternative parameters, i.e., DKSE, DOLS and FMOLS. Therefore, our findings suggest reinforcing the transport infrastructure to promote spectacular inclusive growth in South Asian countries.

Measuring the Contribution of Strategic Commodities to Persistent Inflation; An Effort to Stabilize the Economy

This study aims to analyze the inflation persistence level of the spending group in Blitar Regency and the contribution of 11 commodities or goods/services groups to the formation of inflation persistence in Blitar Regency. In addition, this study also looks at the role of the Regional Inflation Control Team (TPID) in controlling inflation in the regions. This study uses monthly inflation time series data and CPI for 11 commodity groups in Blitar Regency from 2020 to 2020 to 2020 s.d. 2022 obtained from the Blitar Regency Central Bureau of Statistics and publications from Bank Indonesia, the estimation method in this study uses the Univariate Autoregressive Model (AR) and Partial Adjustment Model (PAM) approaches. The results of the study prove that inflation in Blitar Regency has a high level of persistence which is reflected in the time it takes for inflation to return to its equilibrium level, which is 19 months. The persistence of inflation in Blitar Regency was influenced by shocks that occurred in the housing, water, electricity, gas, and fuel group as well as the transportation group as a representative of the administered prices component and the food, beverage, and tobacco group as a volatile food component. group. This research implies that this research can be used as a reference in determining the right time in determining policies by the government, especially in the regulated price group. In addition to controlling inflation in volatile food components due to shocks such as bad weather, TPID can collaborate with relevant agencies to use technology to minimize the impact of climate change and maximize the potential of regional superior products/commodities, and maximize production to maintain supply availability.

Effect of Monetary Policy on Food Inflation in Nigeria: A Nardl Structural Approach

Abstract The relationship between Nigeria's monetary policy and food inflation has been experimentally examined in this study. A quantitative research method based on ex-post facto research design was adopted using the non-linear autoregressive distributed lag model approach (NARDL) in order to examine the impact of monetary policy on food inflation in Nigeria between the periods of 1980 and 2021. As a dependent variable, Food Inflation (FINF) was employed in the study. Exogenous variables also included Treasury Bills Rate (TBR), Exchange Rate (EXG), Monetary Policy Rate (MPR), and Broad Money Supply (M2). The study used time series data from the World Bank data repository (WDI), the National Bureau of Statistics, and the Central Bank of Nigeria's (CBN) Statistical Bulletin. Data analysis showed that the exchange rate significantly and negatively affects the price of food in Nigeria. Similar to this, empirical data demonstrates a long-run association between Nigeria's monetary policy rates and food inflation. Both the money supply and the monetary policy rate have a favorable impact on food inflation in Nigeria, which is both positive and significant. As a result, it was advised that Nigeria's monetary policy be set up so that the goals to be attained are clearly specified. JEL classification numbers: E31, E52, E51, C5. Keywords: Inflation, Monetary Policy, Money Supply, NARDL.

Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling

Streamflow forecasts from continental to global scale hydrological models have gained attention, but their performance against operational forecasts at local to regional scales must be evaluated. This study assesses the skill of medium-range, weekly streamflow forecasts for 147 large Brazilian hydropower plants (HPPs) and compares their performance with forecasts issued operationally by the National Electric System Operator (ONS). A continental-scale hydrological model was forced with ECMWF medium-range forecasts, and outputs were corrected using quantile mapping (QM) and autoregressive model approaches. By using both corrections, the percentage of HPPs with skillful forecasts against climatology and persistence for 1–7 days ahead increased substantially for low to moderate (9% to 56%) and high (72% to 94%) flows, while using only the QM correction allowed positive skill mainly for low to moderate flows and for 8–15 days ahead (29% to 64%). Compared with the ONS, the corrected continental-scale forecasts issued for the first week exhibited equal or better performance in 60% of the HPPs, especially for the North and Southeast subsystems, the DJF and MAM months, and for HPPs with less installed capacity. The findings suggest that using simple corrections on streamflow forecasts issued by continental-scale models can result in competitive forecasts even for regional-scale applications.

A Model to Explain the Impact of Government Revenue on the Quality of Governance and the SDGs

This paper empirically investigates the link between the level of government revenue per capita and six indicators of the quality of governance in an unbalanced panel data set consisting of all countries in the world (217 countries; due to some missing data, (this was reduced to 196) using data from 1996 to 2020. It uses single-equation generalised method of moment (GMM) techniques and a vector autoregressive (VAR) and vector error correction model (VECM) approach to investigate this issue. The results suggest a strong effect over time whereby an increase in government revenue leads to a steady improvement in governance. These findings suggest an important virtuous circle between government revenue and governance. As a result, additional government revenue can significantly impact the Sustainable Development Goals more than our previous work has suggested.

Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm

The study aims to investigate the financial technology (FinTech) factors influencing Chinese banking performance. Financial expectations and global realities may be changed by FinTech’s multidimensional scope, which is lacking in the traditional financial sector. The use of technology to automate financial services is becoming more important for economic organizations and industries because the digital age has seen a period of transition in terms of consumers and personalization. The future of FinTech will be shaped by technologies like the Internet of Things, blockchain, and artificial intelligence. The involvement of these platforms in financial services is a major concern for global business growth. FinTech is becoming more popular with customers because of such benefits. FinTech has driven a fundamental change within the financial services industry, placing the client at the center of everything. Protection has become a primary focus since data are a component of FinTech transactions. The task of consolidating research reports for consensus is very manual, as there is no standardized format. Although existing research has proposed certain methods, they have certain drawbacks in FinTech payment systems (including cryptocurrencies), credit markets (including peer-to-peer lending), and insurance systems. This paper implements blockchain-based financial technology for the banking sector to overcome these transition issues. In this study, we have proposed an adaptive neuro-fuzzy-based K-nearest neighbors’ algorithm. The chaotic improved foraging optimization algorithm is used to optimize the proposed method. The rolling window autoregressive lag modeling approach analyzes FinTech growth. The proposed algorithm is compared with existing approaches to demonstrate its efficiency. The findings showed that it achieved 91% accuracy, 90% privacy, 96% robustness, and 25% cyber-risk performance. Compared with traditional approaches, the recommended strategy will be more convenient, safe, and effective in the transition period.

Dynamic structure of posttraumatic growth among victims of the 2021 Henan floods: A 6-month, three-wave longitudinal study.

Posttraumatic growth (PTG) following traumatic events is a dynamic and transformational process. However, its dynamic structure is currently unknown. The study aimed to estimate the dynamic structure of PTG at the nuance level based on PTG measurement items using network analysis. A three-wave longitudinal study was conducted from July 20, 2021, to January 30, 2022, among the victims experiencing the 2021 Henan floods. The final sample (n = 297) completed reports of PTG after 0, 3, and 6 months of the disaster. We employed the graphical vector autoregressive model approach to estimate extended network models. Contemporaneous network results revealed strong positive associations between domains of PTG in the same measurement window, especially between new possibilities and personal strength. Moreover, temporal network results-the internal interplays among PTG items across measurement windows-revealed that the domain of relating to others plays a central role in the dynamics of PTG. Although other domains predicted an increase in relating to others, relating to others inhibited the development of other domains, especially new possibilities and personal strength. Our study identifies the culture-specific process of PTG and provides empirical evidence on the explanatory models of PTG and the Janus-Face model of PTG.

Does financial stability matter for environmental degradation?

There is a body of evidence that financial development contributes to the quality of the environment, either positively or negatively. However, a limited number of studies consider the effect of financial stability or financial risk on environmental degradation. As part of the proposed research, the present study contributes to the existing literature on asymmetric financial stability's impact on Ireland's environmental quality, as a major gap needs to be added to the literature. Based on this aim, the present study explores the effect of financial stability on environmental degradation in Ireland while controlling primary energy consumption, economic growth, and renewable energy in Ireland. The study employs a novel Fourier autoregressive distributed lag model (FARDL) approach for Ireland, covering the period 1990Q1‐2019Q4 with quarterly data. FARDL cointegration test results indicate a long‐term relationship between financial stability, primary energy consumption, renewable energy consumption, and CO2 emissions. The outcomes from the long‐term coefficient estimation model based on the FARDL model reveal that (i) financial stability leads to a decrease in environmental degradation in Ireland; (ii) renewable energy consumption promotes environmental sustainability; (iii) primary energy consumption has a negative effect on Ireland's environmental quality. These results denote that Ireland should enhance financial stability and promote renewable energy to reduce fossil fuel consumption to achieve net‐zero targets by 2050. Ireland to implement long‐term financial stability policies to fill the emission gap and achieve a net zero emissions target by 2050. Specific policy recommendations for Ireland's environmental policies are also discussed in the study.

Analyzing the impact of healthcare facilities on the spread of COVID-19: A spatial autoregressive exogenous model approach

Analyzing the impact of healthcare facilities on the spread of COVID-19: A spatial autoregressive exogenous model approach

Environmental Tax – Economic Growth Nexus in ASEAN-4 Countries

In the modern world, energy consumption, carbon emissions, and economic growth are concerns for all nations that want to continue expanding by striking a balance between energy and carbon emissions. One reason for this is that these fuels will cause global warming due to climate change. Environmental taxes are increasingly seen as a crucial component of economic policy, where a well-constructed tax can encourage innovation and economic incentives. The nexus of the economic growth and environment is now becoming one of the essential relations for policymakers. Nevertheless, most of the previous studies are limited to European countries. Thus, this research investigates the cointegration of Environmental Tax (ET) towards Economic Growth (EG) with Interest Rate (IR) as a control variable for the case of selected ASEAN countries, namely Malaysia, Philippines, Thailand, and Vietnam. The yearly data set covering the period from 2014 to 2021 was utilized as the sample period for the panel autoregressive distributed lag model (ARDL) approaches. Results revealed that both the Environmental Tax and Interest Rate have a long-run negative effect on Economic growth but have a positive influence in the short run. From this finding, the implication of environmental tax toward economic growth also may depend on the economic conditions of an economy.

Response of Pakistan's economic growth to macroeconomic variables: an asymmetric analysis.

This study examines the impact of several important macroeconomic variables such as quality of education, infrastructure development, foreign direct investment inflow, and green energy transitions on economic growth. We analyzed annual time series data sample for estimation of the above macroeconomic indicators during 1990 to 2020. We use nonlinear auto-regressive distributive lag model (NARDL) approach to detect the short-term and long-term effects of undermentioned macroeconomic variables on economic growth of Pakistan. The results primarily reveal that the quality education, foreign direct investment inflow, and infrastructure development are playing a significant positive role in the economic growth of Pakistan. Similarly, in short term the foreign direct investment inflow, infrastructure, and green energy transition coefficients are significantly positive related to sustainable development goals. However, the education found as unsubstantial as contributive as other variables. Moreover, the Granger causality and structural break estimations are employed to estimate the causal association between the selected parameters and unexpected change over the economy. The estimated outcomes find the unidirectional causality from education and green energy transition towards economic growth, where education is found within relation to infrastructure. Additionally, bidirectional causal relationship is found between FDI and infrastructure towards economic growth which shows that the increase in foreign investment has the potential to boost the economic growth. Finally, all the estimated indexes are considered as important sources towards the economic growth.