Autoregressive Component Research Articles

Fitting Single- and Multiple-Indicator STARTS Models as Dynamic Structural Equation Models

The STARTS model is a structural equation model that decomposes individual differences in a longitudinally assessed variable into a time-invariant stable component, a time-varying autoregressive component, and a time-point specific state component. Typically, the model is estimated with Maximum Likelihood in standard SEM software. Here, we show how different STARTS models (the single-indicator univariate and multivariate and the multiple-indicator univariate model) can be incorporated into the Dynamic Structural Equation Model framework. We use the data of an experience sampling study to illustrate the expositions and we provide codes to fit the models in Mplus. As the DSEM parameters are estimated with a Bayesian approach, we also report the results of three simulation studies in which we examined the statistical properties of the parameter estimates obtained in the DSEM framework. Finally, we discuss how to estimate variants of the STARTS model and we make suggestions for future applied and methodological research.

Evolution of COVID-19 dynamics in Guangdong Province, China: an endemic-epidemic modeling study

BackgroundFrom January 2020 to June 2022, strict interventions against COVID-19 were implemented in Guangdong Province, China. However, the evolution of COVID-19 dynamics remained unclear in this period.ObjectivesThis study aims to investigate the evolution of within- and between-city COVID-19 dynamics in Guangdong, specifically during the implementation of rigorous prevention and control measures. The intent is to glean valuable lessons that can be applied to refine and optimize targeted interventions for future crises.MethodsData of COVID-19 cases and synchronous interventions from January 2020 to June 2022 in Guangdong Province were collected. The epidemiological characteristics were described, and the effective reproduction number (Rt) was estimated using a sequential Bayesian method. Endemic-epidemic multivariate time-series model was employed to quantitatively analyze the spatiotemporal component values and variations, to identify the evolution of within- and between-city COVID-19 dynamics.ResultsThe incidence of COVID-19 in Guangdong Province was 12.6/100,000 population (15,989 cases) from January 2020 to June 2022. The Rt predominantly remained below 1 and increased to a peak of 1.39 in Stage 5. As for the evolution of variations during the study period, there were more spatiotemporal components in stage 1 and 5. All components were fewer from Stage 2 to Stage 4. Results from the endemic-epidemic multivariate time-series model revealed a strong follow-up impact from previous infections in Dongguan, Guangzhou and Zhanjiang, with autoregressive components of 0.48, 0.45 and 0.36, respectively. Local risk was relatively high in Yunfu, Shanwei and Shenzhen, with endemic components of 1.17, 1.04 and 0.71, respectively. The impact of the epidemic on the neighboring regions was significant in Zhanjiang, Shenzhen and Zhuhai, with epidemic components of 2.14, 1.92, and 1.89, respectively.ConclusionThe findings indicate the presence of spatiotemporal variation of COVID-19 in Guangdong Province, even with the implementation of strict interventions. It’s significant to prevent transmissions within cities with dense population. Preventing spatial transmissions between cities is necessary when the epidemic is severe. To better cope with future crises, interventions including vaccination, medical resource allocation and coordinated non-pharmaceutical interventions were suggested.

An empirical investigation of health dynamics of elders in China

Using data from the China Health and Nutrition Survey (CHNS), an index of health frailty is developed for individuals ages fifty-five and older. The evolution of the Frailty Index with age is first contrasted with self-reports of health. Then the temporal moments of the data are examined by considering the covariance structure of shocks that arrive at different ages with frailty in subsequent years. Then a formal model is estimated that incorporates both age-dependent auto-regressive and transitory components. The covariance structure of frailty is found to be convex in age. Larger shocks arrive as the sample ages but they decline rapidly. The evolutions of shocks beyond a specific age are modestly persistent (auto-regressive). The initial age-dependent transitory shocks become a larger portion of the total health risk as age increases.

Network-based time series modeling for COVID-19 incidence in the Republic of Ireland

Network-based time series models have experienced a surge in popularity over the past years due to their ability to model temporal and spatial dependencies, arising from the spread of infectious disease. The generalised network autoregressive (GNAR) model conceptualises time series on the vertices of a network; it has an autoregressive component for temporal dependence and a spatial autoregressive component for dependence between neighbouring vertices in the network. Consequently, the choice of underlying network is essential. This paper assesses the performance of GNAR models on different networks in predicting COVID-19 cases for the 26 counties in the Republic of Ireland, over two distinct pandemic phases (restricted and unrestricted), characterised by inter-county movement restrictions. Ten static networks are constructed, in which vertices represent counties, and edges are built upon neighbourhood relations, such as railway lines. We find that a GNAR model based on the fairly sparse Economic hub network explains the data best for the restricted pandemic phase while the fairly dense 21-nearest neighbour network performs best for the unrestricted phase. Across phases, GNAR models have higher predictive accuracy than standard ARIMA models which ignore the network structure. For county-specific predictions, in pandemic phases with more lenient or no COVID-19 regulation, the network effect is not quite as pronounced. The results indicate some robustness to the precise network architecture as long as the densities of the networks are similar. An analysis of the residuals justifies the model assumptions for the restricted phase but raises questions regarding their validity for the unrestricted phase. While generally performing better than ARIMA models which ignore network effects, there is scope for further development of the GNAR model to better model complex infectious diseases, including COVID-19.

Estimating the risk of SARS-CoV-2 infection in New Zealand border arrivals

BackgroundTravel restrictions and border controls were used extensively during the COVID-19 pandemic. However, the processes for making robust evidence-based risk assessments of source countries to inform border control policies was in many cases very limited.MethodsBetween April 2020 and February 2022, all international arrivals to New Zealand were required to spend 14 days in government-managed quarantine facilities and were tested at least twice. The infection rates among arrivals in the years 2020, 2021 and 2022 were respectively 6.3, 9.4 and 90.0 cases per thousand arrivals (487, 1064 and 1496 cases). Test results for all arrivals were linked with travel history, providing a large and comprehensive dataset on the number of SARS-CoV-2-positive and negative travellers from different countries over time. We developed a statistical model to predict the country-level infection risk based on infection rates among recent arrivals and reported cases in the country of origin. The model incorporates a country-level random effect to allow for the differences between the infection risk of the population of each country and that of travellers to New Zealand. A time dependent auto-regressive component of the model allows for short term correlation in infection rates.ResultsA model selection and checking exercise found that the model was robust and reliable for forecasting arrival risk for 2 weeks ahead. We used the model to forecast the number of infected arrivals in future weeks and categorised countries according to their risk level. The model was implemented in R and was used by the New Zealand Ministry of Health to help inform border control policy during 2021.ConclusionsA robust and practical forecasting tool was developed for forecasting infection risk among arriving passengers during a period of controlled borders during the COVID-19 pandemic. The model uses historical infection rates among arrivals and current infection rates in the source country to make separate risk predictions for arrivals from each country.

Sparse linear parameter-varying autoregressive moving average modelling of non-stationary gearbox vibration signals

Gearboxes frequently operate under variable speed conditions, leading to the non-stationarity of collected monitoring signals. This paper proposes a sparse linear parameter-varying autoregressive moving average (Spa LPV-ARMA) model for fault detection of gearboxes under variable speed conditions. The Spa LPV-ARMA model integrates the advantages of LPV concepts, sparse models, and ARMA models. The proposed model has a more compact structure compared to the reported Spa LPV-AR model, which results in a lower ratio of parameter number to data amount, and the compact model structure also allows the Spa LPV-ARMA model to have higher computational efficiency during the testing phase. A simulation study was conducted to validate the performance of the proposed method. The results demonstrate that the Spa LPV-ARMA model exhibits superior modeling accuracy compared to the reported Spa LPV-AR model. Additionally, the fault detection method based on the Spa LPV-ARMA model achieves a higher fault detection rate when contrasted with a model that solely considers the autoregressive component.

Spatial-temporal drivers and incidence heterogeneity of hemorrhagic fever with renal syndrome transmission in Shandong Province, China, 2016–2022

BackgroundHemorrhagic fever with renal syndrome (HFRS) signals a recurring risk in Eurasia in recent years owing to its continued rise in case notifications and the extension of geographical distribution. This study was undertaken to investigate the spatiotemporal drivers and incidence heterogeneity of HFRS transmission in Shandong Province.MethodsThe epidemiological data for HFRS, meteorological data and socioeconomic data were obtained from China Information System for Disease Control and Prevention, China Meteorological Data Sharing Service System, and Shandong Statistical Yearbook, respectively. The spatial-temporal multicomponent model was employed to analyze the values of spatial-temporal components and the heterogeneity of HFRS transmission across distinct regions.ResultsThe total effect values of the autoregressive, epidemic, and endemic components were 0.451, 0.187, and 0.033, respectively, exhibiting significant heterogeneity across various cities. This suggested a pivotal role of the autoregressive component in propelling HFRS transmission in Shandong Province. The epidemic component of Qingdao, Weifang, Yantai, Weihai, and Jining declined sharply at the onset of 2020. The random effect identified distinct incidence levels associated with Qingdao and Weifang, signifying regional variations in HFRS occurrence.ConclusionsThe autoregressive component emerged as a significant driver in the transmission of HFRS in Shandong Province. Targeted preventive measures should be strategically implemented across various regions, taking into account the predominant component influencing the epidemic.

Forecasting weekly dengue incidence in Sri Lanka: Modified Autoregressive Integrated Moving Average modeling approach.

Dengue poses a significant and multifaceted public health challenge in Sri Lanka, encompassing both preventive and curative aspects. Accurate dengue incidence forecasting is pivotal for effective surveillance and disease control. To address this, we developed an Autoregressive Integrated Moving Average (ARIMA) model tailored for predicting weekly dengue cases in the Colombo district. The modeling process drew on comprehensive weekly dengue fever data from the Weekly Epidemiological Reports (WER), spanning January 2015 to August 2020. Following rigorous model selection, the ARIMA (2,1,0) model, augmented with an autoregressive component (AR) of order 16, emerged as the best-fitted model. It underwent initial calibration and fine-tuning using data from January 2015 to August 2020, and was validated against independent 2000 data. Selection criteria included parameter significance, the Akaike Information Criterion (AIC), and Schwarz Bayesian Information Criterion (SBIC). Importantly, the residuals of the ARIMA model conformed to the assumptions of randomness, constant variance, and normality affirming its suitability. The forecasts closely matched observed dengue incidence, offering a valuable tool for public health decision-makers. However, an increased percentage error was noted in late 2020, likely attributed to factors including potential underreporting due to COVID-19-related disruptions amid rising dengue cases. This research contributes to the critical task of managing dengue outbreaks and underscores the dynamic challenges posed by external influences on disease surveillance.

A Multilevel Spatial Model to Investigate Voting Behaviour in the 2019 UK General Election

This paper presents a modelling framework which can detect the simultaneous presence of two different types of spatial process. The first is the variation from a global mean resulting from a geographical unit’s ‘vertical’ position within a nested hierarchical structure such as the county and region where it is situated. The second is the variation at the smaller scale of individual units due to the ‘horizontal’ influence of nearby locations. The former is captured using a multi-level modelling structure while the latter is accounted for by an autoregressive component at the lowest level of the hierarchy. Such a model not only estimates spatially-varying parameters according to geographical scale, but also the relative contribution of each process to the overall spatial variation. As a demonstration, the study considers the association of a selection of socio-economic attributes with voting behaviour in the 2019 UK general election. It finds evidence of the presence of both types of spatial effects, and describes how they suggest different associations between census profile and voting behaviour in different parts of England and Wales.

Estimation of scrub typhus incidence and spatiotemporal multicomponent characteristics from 2016 to 2023 in Zhejiang Province, China.

China is one of the main epidemic areas of scrub typhus, and Zhejiang Province, which is located in the coastal area of southeastern China, is considered a key region of scrub typhus. However, there may be significant bias in the number of reported cases of scrub typhus, to the extent that its epidemiological patterns are not clearly understood. The purpose of this study was to estimate the possible incidence of scrub typhus and to identify the main driving components affecting the occurrence of scrub typhus at the county level. Data on patients with scrub typhus diagnosed at medical institutions between January 2016 and December 2023 were collected from the China Disease Control and Prevention Information System (CDCPIS). The kriging interpolation method was used to estimate the possible incidence of scrub typhus. Additionally, a multivariate time series model was applied to identify the main driving components affecting the occurrence of scrub typhus in different regions. From January 2016 to September 2023, 2,678 cases of scrub typhus were reported in Zhejiang Province, including 1 case of reported death, with an overall case fatality rate of 0.04%. The seasonal characteristics of scrub typhus in Zhejiang Province followed an annual single peak model, and the months of peak onset in different cities were different. The estimated area with case occurrence was relatively wider. There were 41 counties in Zhejiang Province with an annual reported case count of less than 1, while from the estimated annual incidence, the number of counties with less than 1 case decreased to 21. The average annual number of cases in most regions fluctuated between 0 and 15. The numbers of cases in the central urban area of Hangzhou city, Jiaxin city and Huzhou city did not exceed 5. The estimated random effect variance parameters , , and were 0.48, 1.03 and 3.48, respectively. The endemic component values of the top 10 counties were Shuichang, Cangnan, Chun'an, Xinchang, Pingyang, Xianju, Longquan, Dongyang, Yueqing and Qingyuan. The spatiotemporal component values of the top 10 counties were Pujiang, Anji, Pan'an, Dongyang, Jinyun, Ninghai, Yongjia, Xiaoshan, Yinwu and Shengzhou. The autoregressive component values of the top 10 counties were Lin'an, Cangnan, Chun'an, Yiwu, Pujiang, Longquan, Xinchang, Luqiao, Sanmen and Fuyang. The estimated incidence was higher than the current reported number of cases, and the possible impact area of the epidemic was also wider than the areas with reported cases. The main driving factors of the scrub typhus epidemic in Zhejiang included endemic components such as natural factors, but there was significant heterogeneity in the composition of driving factors in different regions. Some regions were driven by spatiotemporal spread across regions, and the time autoregressive effect in individual regions could not be ignored. These results that monitoring of cases, vectors, and pathogens of scrub typhus should be strengthened. Furthermore, each region should take targeted prevention and control measures based on the main driving factors of the local epidemic to improve the accuracy of prevention and control.

Bayesian inference for long memory term structure models

In this study, we propose a novel adaptation of the Dynamic Nelson–Siegel term structure model, incorporating long memory properties to enhance its forecasting accuracy. Our approach involves modelling the evolution of latent factors using fractional Gaussian noise processes, approximated by a weighted sum of independent first-order autoregressive components. The resulting formulation allows for a Gaussian Markov Random Field representation, facilitating the application of computationally efficient Bayesian techniques through Integrated Nested Laplace Approximations. Extensive simulation and empirical analysis demonstrate that integrating long memory significantly improves the model's forecasting performance, particularly for longer time horizons. By shedding light on the potential benefits of incorporating long memory concepts into traditional term structure models, our research highlights its utility in capturing intricate temporal dependencies and enhancing prediction precision.

Default risk in Microfinance Institutions: Case study of a Costa Rican company

The most recent Economic Report of the Inter-American Development Bank ranks Costa Rica as the second country in Central America with the highest household indebtedness, and almost half of households have no savings. These conditions have led to increases in the default rates of loan creditors. This research analyzes the factors affecting default in a Costa Rican Microfinance Institution that grants microcredits to individuals and small businesses without access to traditional bank financing. An econometric approach with panel data was applied and the results show that default is affected by autoregressive components and the financed activity. The application of these methodologies allows strengthening credit analysis and the selection of market niches to mitigate the default risk.

Scaling law characteristics and spatiotemporal multicomponent analysis of syphilis from 2016 to 2022 in Zhejiang Province, China.

Syphilis has caused epidemics for hundreds of years, and the global syphilis situation remains serious. The reported incidence rate of syphilis in Zhejiang Province has ranked first in the province in terms of notifiable infectious diseases for many years and is the highest in China. This study attempts to use the scaling law theory to study the relationship between population size and different types of syphilis epidemics, while also exploring the main driving factors affecting the incidence of syphilis in different regions. Data on syphilis cases and affected populations at the county level were obtained from the China Disease Control and Prevention Information System. The scaling relationship between different stages of syphilis and population size was explained by scaling law. The trend of the incidence from 2016 to 2022 was tested by the joinpoint regression. The index of distance between indices of simulation and observation (DISO) was applied to evaluate the overall performance of joinpoint regression model. Furthermore, a multivariate time series model was employed to identify the main driving components that affected the occurrence of syphilis at the county level. The p value less than 0.05 or confidence interval (CI) does not include 0 represented statistical significance for all the tests. From 2016 to 2022, a total of 204,719 cases of syphilis were reported in Zhejiang Province, including 2 deaths, all of which were congenital syphilis. Latent syphilis accounted for 79.47% of total syphilis cases. The annual percent change (APCs) of all types of syphilis, including primary syphilis, secondary syphilis, tertiary syphilis, congenital syphilis and latent syphilis, were - 21.70% (p < 0.001, 95% CI: -26.70 to -16.30), -16.80% (p < 0.001, 95% CI: -20.30 to -13.30), -8.70% (p < 0.001, 95% CI: -11.30 to -6.00), -39.00% (p = 0.001, 95% CI: -49.30 to -26.60) and - 7.10% (p = 0.008, 95% CI: -11.20 to -2.80), respectively. The combined scaling exponents of primary syphilis, secondary syphilis, tertiary syphilis, congenital syphilis and latent syphilis based on the random effects model were 0.95 (95% CI: 0.88 to 1.01), 1.14 (95% CI: 1.12 to 1.16), 0.43 (95% CI: 0.37 to 0.49), 0.0264 (95% CI: -0.0047 to 0.0575) and 0.88 (95% CI: 0.82 to 0.93), respectively. The overall average effect values of the endemic component, spatiotemporal component and autoregressive component for all counties were 0.24, 0.035 and 0.72, respectively. The values of the autoregressive component for most counties were greater than 0.7. The endemic component of the top 10 counties with the highest values was greater than 0.34. Two counties with value of the spatiotemporal component higher than 0.1 were Xihu landscape county and Shengsi county. From 2016 to 2022, the endemic and autoregressive components of each county showed obvious seasonal changes. The scaling exponent had both temporal trend characteristics and significant heterogeneity in the association between each type of syphilis and population size. Primary syphilis and latent syphilis exhibited a linear pattern, secondary syphilis presented a superlinear pattern, and tertiary syphilis exhibited a sublinear pattern. This suggested that further prevention of infection and transmission among high-risk populations and improvement of diagnostic accuracy in underdeveloped areas is needed. The autoregressive components and the endemic components were the main driving factors that affected the occurrence of syphilis. Targeted prevention and control strategies must be developed based on the main driving modes of the epidemic in each county.

Inflation forecasting using autoregressive distributed lag (ARDL) models

This study attempts to evaluate and compare the inflation-predicting performance of several ARDL models. Since there was no cointegration, the ARDL model does not employ an error correction term. Subsequently, model development showed that ARDL(2,2) should be used. Besides the formally developed model, some other more arbitrarily chosen ARDL models were also included, i.e., ARDL(1,1), ARDL(2,0), ARDL(1,0), ARDL(0,1), and ARDL(0,2). This research measures forecasting performance with inflation as the forecasting object. The duration of the monthly inflation statistics ranged from January 2011 to July 2022. The data were separated into two categories. The training data ranged between January 2011 and December 2021. After getting the appropriate parameters from the training data, the models generated projections from January 2022 to July 2022. The research determined that ARDL (1,0) was the most accurate inflation forecasting model, followed by ARDL (0,2) and formally constructed ARDL(2,2) finished in fourth place. This study suggests that the formal development of ARDL for forecasting purposes is unnecessary. Formal ARDL development is more appropriate for root cause analysis. In addition, the single autoregressive component indicates that most of the inflation value's information originated from the prior period. This suggests that the previous period's value is Indonesia's most significant predictor of inflation. The impact of greater period lags on inflation forecasting diminishes immediately.

Workers' emotional exhaustion and mental well-being over the COVID-19 pandemic: a Dynamic Structural Equation Modeling (DSEM) approach.

The COVID-19 pandemic has presented significant challenges to the workforce, particularly concerning emotional and mental well-being. Given the prolonged periods of work-related stress, unexpected organizational changes, and uncertainties about work faced during the pandemic, it becomes imperative to study occupational health constructs under a dynamic methodological perspective, to understand their stable and unstable characteristics better. In this study, drawing on the Dynamic Structural Equation Modeling (DSEM) framework, we used a combination of multilevel AR(1) models, Residual-DSEM (RDSEM), multilevel bivariate VAR(1) models, and multilevel location-scale models to investigate the autoregression, trend, and (residual) cross-lagged relationships between emotional exhaustion (EmEx) and mental well-being (MWB) over the COVID-19 pandemic. Data were collected weekly on 533 workers from Germany (91.18%) and Italy (8.82%) who completed a self-reported battery (total number of observations = 3,946). Consistent with our hypotheses, results were as follows: (a) regarding autoregression, the autoregressive component for both EmEx and MWB was positive and significant, as well as it was their associated between-level variability; (b) regarding trend, over time EmEx significantly increased, while MWB significantly declined, furthermore both changes had a significant between-level variability; (c) regarding the longitudinal bivariate (cross-lagged) relationships, EmEx and MWB negatively and significantly affected each other from week to week, furthermore both cross-lagged relationships showed to have significant between-level variance. Overall, our study pointed attention to the vicious cycle between EmEx and MWB, even after controlling for their autoregressive component and trend, and supported the utility of DSEM in occupational health psychology studies.

Structural Time Series Model using Hamiltonian Monte Carlo for Rice Price

Although forecasts of future events are simply uncertain, predicting is one of the most important aspects of future planning. Accurate rice price predictions tend to be helpful for wholesalers, producers, and farmers to develop plans and strategies to reduce the risks that can be faced. Structural time series models are the most plausible alternative for long-term forecasting. This paper proposes an alternate method for modeling average rice prices using structural time series along with Bayesian parameter inference via Hamiltonian Monte Carlo (HMC). The model has been built using the monthly average wholesale rice price from January 2010 to December 2019. For working out both structural time series and HMC, the TensorFlow Probability Library was used. Linear trend, seasonal, and autoregressive components were combined as an additive model to the structural time model. The proposed Hamiltonian parameter produces an optimal acceptance rate. Their trace plot was used to diagnose the convergence of their chain. One of the predictive accuracy of models was assessed using the mean absolute percent error (MAPE). Through both single and multiple chain iterations, the prediction accuracy of a year-ahead is highly accurate, with MAPE less than 2%. Long-term iteration draws during Hamiltonian Monte Carlo should be considered when attempting to achieve more convergence.

Forecasting the Consumer Price Index in the Regions of the Philippines using Machine Learning for Time Series Models

The core objective of this study is to showcase the enhanced forecasting capabilities of a hybrid model that combines the strengths of Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) in predicting the Consumer Price Index (CPI). By harnessing the intricate non-linear pattern capturing ability of ANN and the capabilities of ARIMA in modeling linear and autoregressive components, the hybrid model aims to outperform the standalone ARIMA model in accurately forecasting the CPI. Real-world CPI data will be utilized for empirical evaluation and comparison, providing valuable insights into the effectiveness and practical applicability of the hybrid ARIMA-ANN approach in improving CPI forecasting accuracy. The performance of Box Jenkins Models which gives the resulted value of R-squared values for both stationary and non-stationary data are high, indicating that the models explain a significant portion of the variability in the CPI data. The RMSE, MAPE, and MAE values are relatively low, suggesting that the Box-Jenkins models' predictions are close to the actual values. The Ljung-Box Q statistic indicates that all Box-Jenkins models best fit their respective CPI data. The study also employs rigorous statistical methods of machine learning model accuracy assessment, including the Akaike Information Criterion (AIC), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE), to assess the forecasting performance of both models. The results demonstrate that the hybrid ARIMA-ANN model consistently outperforms the standalone ARIMA model, delivering more accurate and reliable forecasts over an extended forecast horizon. The integration of Artificial Neural Networks (ANN) using Multilayer Perceptron (MLP) in the ARIMA models improved the accuracy of the fitted and forecasted values. RMSE and MSE values for the Hybrid ARIMA-ANN models are lower compared to the original Box-Jenkins/ARIMA models, validating the goal of enhancing accuracy through ANN integration.

Exchange Rate Volatility and Nigerian Stock Market Development

Purpose: The study aims to evaluate the impact of exchange rate volatility on the indicators of stock market, such as the returns, capitalization, liquidity, transaction volume, based on the Nigerian evidence. Theoretical framework: The paper considers extant model of exchange rate-stock market interactions. Because the diversity of the market is important the paper follows suggestions by El-Wassal (2013) to examine variety of stock market indicators in order to offer accurate depiction of how the stock market drives exchange rate volatility. Design/Methodology/Approach: The study employs a simple static regression model of stock market indicators with autoregressive adjustment component which absorb autocorrelation. The models are tested on annual data (1985-2020). Findings: The study finds that the exchange rate volatility has negative impact on stock market development - returns, capitalization and volume. The paper reveals further that other correlated controls factors impinge different impacts on the stock market indicators. Research, Practical &amp; Social implications: One implication of the finding is that the volatility may discourage investors, reduce firm performance, and lead to reduction on the returns of firm shares. Policy makers should put in place growth-inducing infrastructural investments to make the business environment more conducive to attract foreign capital, while also positioning the capital market with several initiatives to increase trading activities. Originality/Value: The connection between the foreign exchange and financial markets is of policy importance. Although the focus is on the Nigerian markets, the evidence could be extended to other African and global markets, to serve as guide to foreign investors.

A generalized Burr mixture autoregressive models for modeling non linear time series

A more flexible type of mixture autoregressive model, namely the Burr mixture autoregressive, BMAR model is studied in this article for modeling non linear time series. The model consists of a mixture of K autoregressive components with each conditional distribution of the component following a Burr distribution. The BMAR model enjoys some nice statistical properties which allow it to capture time series with: (1) unimodal or multimodal; (2) asymmetry or symmetry conditional distribution; (3) conditional heteroscedasticity; (4) cyclical or seasonal; and (5) conditional leptokurtic distribution. Sufficient and less restrictive conditions for the ergodicity of the BMAR model are derived and discussed. A more robust constrained optimization algorithm (EM – sequential quadratic programming method) is proposed for the non linear optimization problem. From the simulation studies carried out, the parameters estimation method showed satisfying results. The variance of the estimated parameters is also addressed with the missing information principle. Real datasets from two different fields of study are used to assess the performance of the BMAR model compared to other competing models. The comparison done in the empirical examples reveals the supremacy of the BMAR model in capturing the data behavior.

Short-term impact of extreme temperatures, relative humidity and air pollution on emergency hospital admissions due to kidney disease and kidney-related conditions in the Greater Madrid area (Spain)

While some studies report a possible association between heat waves and kidney disease and kidney-related conditions, there still is no consistent scientific consensus on the matter or on the role played by other variables, such as air pollution and relative humidity.Ecological retrospective time series study 01-01-2013 to 31-12-2018). Dependent variables: daily emergency hospitalisations due to kidney disease (KD), acute kidney injury (AKI), lithiasis (L), dysnatraemia (DY) and hypovolaemia (HPV). Independent variables: maximum and minimum daily temperature (Tmax, Tmin, °C), and daily relative humidity (RH, %). Other variables were also calculated, such as the daily temperature for risk of kidney disease (Theat, °C) and low daily hazardous relative humidity (HRH%).As variables of air pollution, we used the daily mean concentrations of PM10, PM2.5, NO2 and O3 in μg/m3. Based on these, we then calculated their daily excesses over World Health Organisation (WHO) guideline levels (hPM10, hPM2.5, hNO2 and hO3 respectively). Poisson family generalised linear models (GLMs) (link = log) were used to calculate relative risks (RRs), and attributable risks and attributable admissions. In the models, we controlled for the covariates included: seasonalities, trend, autoregressive component, day of the week, month and year.A statistically significant association was found between Theat and all the dependent variables analysed. The greatest AKI disease burden was attributable to Theat (2.2 % (1.7, 2.6) of attributable hospital admissions), followed by hNO2 (1.7 % (0.9, 3.4)) and HRH (0.8 (0.6, 1.1)). In the case of hypovolaemia and dysnatraemia, the greatest disease burden again corresponded to Theat, with 6.9 % (6.2, 7.6) and 5.7 (4.8, 6.6) of attributable hospital admissions respectively.Episodes of extreme heat exacerbate daily emergency hospital admissions due to kidney disease and kidney-related conditions; and attributable risks are likewise seen for low relative humidity and high ozone levels.