Autoregressive Error Model Research Articles

Research on the volatility forecasting model of KOSPI index returns using AR(M)-GARCH(P,Q) model

In this study, we estimated the volatility of the KOSPI index returns and analyzed volatility trends. The data used in the study consisted of monthly observations from January 2005 to December 2022, and the KOSPI index raw data was transformed into log returns. The volatility estimation model used the AR(m)-GARCH(p,q) model, which combines the autoregressive error model and the GARCH model that explains the persistence of volatility at low orders. The goodness of fit of the model was confirmed using the Portmanteau Q-test and LM-test. Applying the autoregressive error model revealed significant autocorrelation in the log returns of the KOSPI index at lags 3 and 6. Residual analysis indicated that the residuals followed white noise, but the squared residuals exhibited heteroscedasticity. Therefore, after fitting the autoregressive error model, we applied the GARCH model and conducted residual analysis, finding both the residuals and squared residuals significant at a 5% significance level. The volatility forecasting results indicated a continuous increase in volatility. The findings of this study are expected to provide important implications for policymakers responsible for risk management in the Korean stock market.

Gaussian Mixture–Based Autoregressive Error Model with a Conditionally Heteroscedastic Hierarchical Framework for Bayesian Updating of Structures

Gaussian Mixture–Based Autoregressive Error Model with a Conditionally Heteroscedastic Hierarchical Framework for Bayesian Updating of Structures

The Impact of the COVID-19 Pandemic on Pattern of Antibiotic and Opioid Prescriptions by Dentists in Alberta, Canada.

After the shutdown of most dental services during the COVID-19 lockdown, the oral health community was concerned about an increase in prescribing opioids and antibiotics by dentists due to patients' limited access to dental offices. Therefore, the objective of this study was to investigate the impact of COVID-19 pandemic on the pattern of antibiotic and opioid prescriptions by dentists in Alberta, Canada. Data obtained from the Tracked Prescription Program were divided into antibiotics and opioids. Time periods were outlined as pre-, during-, and postlockdown (phase 1 and 2). For the number of prescriptions and average supply, each monthly average was compared to the corresponding prelockdown monthly average, using descriptive analysis. Time series analyses were conducted using regression analyses with an autoregressive error model. Data were trained and tested on monthly observations before lockdown and predicted for during- and postlockdown. A total of 1.1 million antibiotics and 400,000 opioids dispense were tracked. Decreases in the number of prescriptions during lockdown presented for antibiotics (n = 24,933 vs. 18,884) and opioids (n = 8892 vs. 6051). Average supplies (days) for the antibiotics (n = 7.10 vs. 7.55) and opioids (n = 3.92 vs. 4.05) were higher during the lockdown period. In the trend analyses, the monthly number of antibiotic and opioid prescriptions showed the same pattern and decreased during lockdown. The COVID-19 pandemic altered the trends of prescribing antibiotics and opioids by dentists. The full impact of COVID-19 pandemic on the population's oral health in light of changes in prescribing practices by dentists during and after lockdown warrants further investigation.

Using Exogenous Social Media Exposure Measures to Assess the Effects of Smokeless Tobacco-Related Social Media Content on Smokeless Tobacco Sales in the United States.

Prior research on the effects of social media promotion of tobacco products has predominantly relied on survey-based self-report measures of marketing exposure, which potentially introduce endogeneity, recall, and selection biases. New approaches can enhance measurement and help better understand the effects of exposure to tobacco-related messages in a dynamic social media marketing environment. We used geolocation-specific tweet rate as an exogenous indicator of exposure to smokeless tobacco (ST)-related content and employed this measure to examine the influence of social media marketing on ST sales. Autoregressive error models were used to analyze the association between the ST-relevant tweet rate (aggregated by 4-week period from February 12, 2017 to June 26, 2021 and scaled by population density) and logarithmic ST unit sales across time by product type (newer, snus, conventional) in the United States, accounting for autocorrelated errors. Interrupted time series approach was used to control for policy change effects. ST product category-related tweet rates were associated with ST unit sales of newer and conventional products, controlling for price, relevant policy events, and the coronavirus disease 2019 (COVID-19) pandemic. On average, 100-unit increase in the number of newer ST-related tweets was associated with 14% increase in unit sales (RR = 1.14; p = .01); 100-unit increase in conventional ST tweets was associated with ~1% increase in unit sales (p = .04). Average price was negatively associated with the unit sales. Study findings reveal that ST social media tweet rate was related to increased ST consumption and illustrate the utility of exogenous measures in conceptualizing and assessing effects in the complex media environment. Tobacco control initiatives should include efforts to monitor the role of social media in promoting tobacco use. Surveillance of social media platforms is critical to monitor emerging tobacco product-related marketing strategies and promotional content reach. Exogenous measures of potential exposure to social media messages can supplement survey data to study media effects on tobacco consumption.

Differentiable multi-ridge regression for system identification

Regularization aims to shrink model parameters, reducing complexity and overfitting risk. Traditional methods like LASSO and Ridge regression, limited by a single regularization hyperparameter, can restrict bias-variance trade-off adaptability. This paper addresses system identification in a multi-ridge regression framework, where an l2-penalty on the model coefficients is introduced, and a different regularization hyperparameter is assigned to each model parameter. To compute the optimal hyperparameters, a cross-validation-based criterion is optimized through gradient descent. Autoregressive and Output Error models are considered. The former requires formulating a regularized least-squares problem. The identification of the latter class is more challenging and is addressed by adopting regularized instrumental variable methods to ensure a consistent parameter estimation.

Soil water availability strongly drives the differentiation of acquisitive and conservative strategies for coexisting woody species in a Chinese subtropical evergreen forest

AbstractQuestionsTrait–environment relationships hold great promise for elucidating the mechanisms that drive community assembly. We asked to what extent does functional trait coordination underlie the differentiation of acquisitive–conservative strategies along environmental gradients? Also, to what extent does soil water availability determine trait combinations and subsequently shape microhabitat preferences for species with distinct ecological strategies?LocationYunnan Province, southwest China.MethodsPearson correlation and trait network analyses were used to quantify trait associations. A combined RLQ–fourth‐corner analysis and a spatial autoregressive error model were employed to examine trait–environment relationships and explain species distribution along environmental gradients.ResultsLeaf and stem traits were tightly coordinated along the first RLQ axis. Herein, traits representing construction cost exhibited trade‐offs against traits representing resource acquisition rate, underlying an acquisitive–conservative strategy differentiation along the environmental gradients. Significant overall and bivariate trait–environment relationships were found. In addition, the topographic wetness index contributed substantially to the environmental variation, and significantly influenced most traits alone. These results supported the hypothesis that soil water availability was the key environmental factor in selecting trait combinations and driving the differentiation of acquisitive and conservative strategies for coexisting species compared with other environmental factors. Finally, deciduous and a proportion of the evergreen species were acquisitive, whereas the remaining evergreen species were conservative, preferring wetter and drier microhabitats, respectively.ConclusionsOur findings indicate that soil water availability plays a central role in shaping both the trait combinations and microhabitat preferences of species with different ecological strategies rather than different leaf habits. These findings also highlight the fundamental significance of functional traits in facilitating the differentiation between acquisitive and conservative strategies along environmental gradients.

Bayesian geoacoustic inversion in shallow water with vertical coherence of ship-radiated noise

Spatial structure of underwater acoustic field depends strongly on the geoacoustic properties of the seabed. The geoacoustic parameters can be estimated using the spatial coherence dominated by a single broadband source. In this paper, geoacoustic inversion of a shallow water environment is carried out using the vertical coherence of ship-radiated noise at a close range. Data were measured in an experiment in the South China Sea, and the vertical coherence is extracted from a single hydrophone pair in the vertical line array. The closest point of the passing research ship approach is determined first, and then the broadband coherence curves are extracted as the input for the inversion algorithm. Due to the lack of an appropriate parametrization, the trans-dimensional inversion method combined with the auto-regressive error model is adopted in this paper. The inversion results show that the sound speed is the most sensitive, followed by the density, and then the attenuation coefficient is the least sensitive, which is consistent with previous works. The data error hypothesis is verified via a posterior examinations.

Determining the Effect of Foreign Trade On Employment in Turkey with Spatial Panel Data Analysis / Türkiye'de Dış Ticaretin İstihdam Üzerinde Etkisinin Mekânsal Panel Veri Analizi İle Belirlenmesi

In this study, which examines the relationship between foreign trade and employment, which is directly proportional to economic growth, the aim is to investigate the effect of import and export on employment by including neighborhood relations. In the study, while the number of insured 4a, 4b and 4c in Turkey's 81 provinces is considered as employment variable, annual import, export and population values are also considered as independent variables and the period of 2009-2020 is examined. Using the spatial econometrics application, which also includes the effect of neighborhood relations, remarkable results were obtained in the study, which was examined under three headings: spatial Durbin, spatial autoregressive and spatial error model. Models were estimated using the Maximum Likelihood (ML) method. In the study, Moran's I index was calculated to examine the convergence in employment rates between provinces. According to the model results, it was determined that there is a significant inverse relationship between exports and employment. Population variable, on the other hand, was found to have a significant relationship in the same direction with employment. A significant effect of the import variable could not be determined. When the variables of neighboring provinces were examined, it was concluded that the variables of import and employment were significant.

Green finance, environmental pollution and high-quality economic development-a study based on China's provincial panel data.

Under the new normal, green finance is inevitably linked to high-quality economic development in China. Based on 30 province panel data sets from China from 2010 to 2019, this research employs an innovative spatial econometric model to integrate green finance, environmental pollution and high-quality economic development into a unified empirical analysis framework. Regional green finance and high-quality economic development have spatial spillover effects in China, according to spatial auto-regressive and spatial error model tests, with the eastern region leading, the central region running in parallel and the western region chasing, because of a strong moderating effect in the eastern region. Green finance contributes greatly to high-quality economic development, but increased environmental pollution impedes high-quality economic development. Green financing can help to mitigate the detrimental effects of pollution on high-quality economic development. According to the mechanism of action analysis, green finance reduces pollution by modifying the industrial structure and boosting scientific and technological growth. Finally, the green finance threshold test demonstrates a nonlinear impact on economic quality development after passing a specified threshold value and has a strong threshold characteristic. This research has policy implications since it improves understanding of the dynamics of high-quality economic development as well as the benefits, mechanisms and heterogeneity of green finance in reducing pollution and empowering high-quality economic development.

Flood hazards and housing prices: a spatial regression analysis for Hat Yai, Songkhla, Thailand

PurposeGlobal warming exacerbates sea level rise and extreme weather events that cause severe flooding, resulting in lost productivity and property damage. To reduce the impact of flooding, residents are avoiding purchasing homes in high-risk areas. There are numerous studies on the relationship between flood hazards and housing prices in developed countries, but few in developing countries. Therefore, this study aims to investigate the relationship between flood hazards and housing prices in Hat Yai, Songkhla, Thailand.Design/methodology/approachThis study uses spatial-lag, spatial error and spatial autoregressive lag and error (SARAR) models to analyze the effect of flood risk on property prices. The main analysis examines the degree of flood risk and housing rental prices from our survey of 380 residences. To test the robustness of the results, the authors examine a different data set of the same samples by using the official property valuation from the Ministry of Finance and the flood risk estimated by the Southern Natural Disaster Research Center.FindingsThe SARAR model was chosen for this study because of the occurrence of spatial dependence in both dependent variable and the error term. The authors find that flood risk has a negative impact on property prices in Hat Yai, which is consistent with both models.Originality/valueThis study is one of the first to use spatial econometrics to analyze the impact of flood risk on property prices in Thailand. The results of this study are valuable to policymakers for benefit assessment in cost–benefit analysis of flood risk avoidance or reduction strategies and to the insurance market for pricing flood risk insurance.

Does Investment and Energy Infrastructure Influence Convergence in Sumatra Island, Indonesia?

This research aims to prove the existence of a convergence process and analyse the effect of investment and energy infrastructure on the convergence process on Sumatra Island by including the element of space to understand spatial convergence better. The dataset used in panel data consists of 154 regions (district/municipality) from 2010 to 2020. The analytical tools used with a spatial econometric approach consist of Spatial Autoregressive (SAR) and Spatial Error Model (SEM). The results of the convergence test prove that there is convergence in both absolute and conditional convergence, and there is a difference in the speed of convergence for the two equations. Meanwhile, the results of the spatial approach state that there are spatial dependencies so that neighbouring regions influence the region. The estimation results of conditional β-convergence reveal that investment and government spending in infrastructure has a positive and significant effect, in contrast to energy infrastructure, which has a negative and significant relationship, and only human capital is not significant to the convergence process in Sumatra.

The Shift from Energy to Water Limitation in Local Canopy Height from Temperate to Tropical Forests in China

Canopy height greatly affects the biomass stock, carbon dynamics, and maintenance of biodiversity in forests. Previous research reported that the maximum forest canopy height (Hmax) at global and regional scales could be explained by variations in water or energy availability, that is, the water- or energy-related hypothesis. However, fundamental gaps remain in our understanding of how different drivers (i.e., water and energy) contribute to the Hmax at the local scale. In this study, we selected eight dynamic forest plots (20–30 ha) across a latitudinal gradient (from 21.6° N to 48.1° N) in China and measured the canopy structure using airborne light detection and ranging (LiDAR) data. Based on the LiDAR point cloud data, we extracted the maximum tree height (Hmax) in a 20 × 20 m quadrat as a proxy for canopy height, and the topographic wetness index (TWI) and digital terrain model-derived insolation (DTMI) were calculated as proxies for water and energy conditions. We used a linear mixed model and spatial simultaneous autoregressive error model to quantify how TWI and DTMI contributed to variations in Hmax at the local scale. We found that the positive effect of TWI was stronger in subtropical and tropical forests, highlighting that water was the main factor that drives the canopy height pattern in these regions. In contrast, although the effects of DTMI can be both positive and negative, its relative contribution was higher in temperate forest plots than in other forest types, supporting the idea that energy input is more critical for Hmax in temperate forests. Overall, our study revealed the directional change from energy to water limitation from temperate to subtropical and tropical forests. Our findings can offer important insights into forest management, especially under global climate change in the Anthropocene.

Shift in Clinical Profile of Hospitalized Pneumonia in Children in the Non-pharmaceutical Interventions Period During the COVID-19 Pandemic: A Prospective Multicenter Study.

Non-pharmaceutical interventions (NPIs) against coronavirus disease 2019 were implemented in March 2020. These measures were followed by a major impact on viral and non-viral diseases. We aimed to assess the impact of NPI implementation in France on hospitalized community-acquired pneumonia (hCAP) frequency and the clinical and biological characteristics of the remaining cases in children. We performed a quasi-experimental interrupted time-series analysis. Between June 2014 and December 2020, eight pediatric emergency departments throughout France reported prospectively all cases of hCAP in children from age 1 month to 15 years. We estimated the impact on the monthly number of hCAP using segmented linear regression with autoregressive error model. We included 2,972 hCAP cases; 115 occurred during the NPI implementation period. We observed a sharp decrease in the monthly number of hCAP after NPI implementation [−63.0% (95 confidence interval, −86.8 to −39.2%); p < 0.001]. Children with hCAP were significantly older during than before the NPI period (median age, 3.9 vs. 2.3 years; p < 0.0001), and we observed a higher proportion of low inflammatory marker status (43.5 vs. 33.1%; p = 0.02). Furthermore, we observed a trend with a decrease in the proportion of cases with pleural effusion (5.3% during the NPI period vs. 10.9% before the NPI; p = 0.06). NPI implementation during the COVID-19 (coronavirus disease 2019) pandemic led not only to a strong decrease in the number of hCAP cases but also a modification in the clinical profile of children affected, which may reflect a change in pathogens involved.

Political Potentials, Deep-Seated Nativism and the Success of the German AfD

The German populist radical right party “Alternative for Germany” (AfD) was founded amid various economic and political crises. This article argues that the electoral success of this political challenger, however, is rooted in more than the upsurge of populist resentments born out of these crises. Integrating theories about the activation of attitudes with arguments about the effects of exposure to local political contexts, I contend that the electoral success of the AfD reflects the mobilization of deep-seated nativist sentiments. To test these propositions, I draw on a large panel dataset of the AfD’s electoral returns at the municipal level (N = 10,694) which I link to pre-crises data on the marginal success of extreme-right parties. Exploiting variation between municipalities located within the same county (N = 294), I estimate a series of spatial simultaneous autoregressive error models by maximum likelihood estimation. The results show that the success of the AfD is rooted in the local prevalence of nativist sentiments that date prior to the crises that fomented the formation of the challenger party–an effect that becomes stronger in the course of the radicalization of the AfD. I further demonstrate that the populist right AfD is best able to broaden its electoral appeal among local communities with an extreme-right sub-culture, particularly in Eastern Germany. This suggests that even small extreme-right networks can act as a breeding ground for the populist right and help spread xenophobic and nativist sentiments among citizens.

Wages of Workers Spatial Analysis in Indonesia Region 2019

The wage inequality of workers in Indonesia is one of the main problems and concerns that are important to be addressed by the government. The determination of the regional minimum wage by the local government has not been able to solve the problem of inequality. On a larger scale, the wage inequality of workers can affect the stability of the national economy. Research on the spatial analysis of workers' wages is very important to be carried out as a basis for making appropriate policies by the government. In this study, we have succeeded in analyzing the dependence and spatial relationship of a region with the wages of its workers and have identified the factors that affect the wages of workers in a region. The result reveals the spatial dependences are detected among districts, followed by the spatial clusters and spatial outliers through global and local spatial autocorrelation. Applying two spatial autoregressive models, spatial autoregressive lag model (SAL) and spatial autoregressive error model (SEM), SAL confirmed that there are 4 significant independent variables with a level of 10 percent and have a positive relationship, namely education, age, internet, and sex ratio variables. And SEM confirmed that there are significants 5 significant independent variables with a level of 10 percent and have a positive relationship, namely education, age, technology, internet, and sex ratio variables. As the policy implication, since regional inequality in term of wage is still a major issue, it will be a call for better coordination and cooperation within and between regions.

Drone-acquired data reveal the importance of forest canopy structure in predicting tree diversity

The most salient feature of forests is the vertical-filling architecture of its constituent species. However, among the possible determinants of tree community assembly, vertical niche differentiation has been poorly studied. Here we used an Unmanned Aerial Vehicle to measure spatial variation of canopy structure in five subtropical forest plots in China, and evaluated the importance of canopy structure and topography in structuring tree diversity and species distributions. We combined data from canopy attributes with topography and the locations of 533,763 individuals of 614 tree species. Spatial simultaneous autoregressive error models were used to evaluate the relative importance of each variable to species diversity. We found that varaibles describing canopy structure contributed significantly to tree richness patterns in all plots and all forest layers, although the strength and direction of the effects varied among the sites. Among the study species, the abundance distributions of 38–49% of them in four plots were explained by the combination of canopy structure and topographic variables, and 21–33% by canopy structure or topography alone, while the abundances of 48% species in the Heishiding plot were explained by canopy structure alone. Our study shows that canopy structure variations and topography jointly shape species distributions in these forests and our findings highlight the importance of considering canopy structure and related ecological processes for understanding community assembly.

The impact of the COVID-19 pandemic on paediatric health service use within one year after the first pandemic outbreak in New South Wales Australia – a time series analysis

SummaryBackgroundThe first wave of the COVID-19 pandemic hit New South Wales (NSW) Australia in early 2020, followed by a sharp state-wide lockdown from mid-March to mid-May. After the lockdown, there had been a low level of community transmission of COVID-19 over a year. Such pandemic experiences provide unique opportunity to understand the impact of the pandemic on paediatric health service use as countries emerge from the pandemic.MethodsWe examined the difference between the observed and the predicted numbers of inpatient admissions and emergency department (ED) attendances, respectively, related to chronic, acute infectious and injury conditions, for each month during the COVID-19 period (January 2020-February 2021), based on the numbers from 2016 to 2019, using records from two major paediatric hospitals in NSW. All analyses were conducted using autoregressive error models and were stratified by patient age, sex and socioeconomic status.FindingsHealth service use was significantly lower than predicted for admissions and/or ED attendances related to chronic conditions, acute infections, and injury during the lockdown in 2020. Change in health service use varied by chronic conditions, from the largest decrease for respiratory conditions (40-78%) to non-significant change for cancer and mental health disorders. After the lockdown, health service use for most health conditions returned to pre-COVID-19 predicted levels. However, for mental health disorders, increased health service use persisted from June 2020 up to February 2021 by 30-55%, with higher increase among girls aged 12-17 years and those from socioeconomically advantaged areas. There was persistently lower health service use for acute infections and increased health service use for injuries. Differences by socio-demographic factors were noted for mental health disorders and injuries.InterpretationThe immediate return to pre-COVID-19 levels for most chronic conditions after the first lockdown in NSW highlights the healthcare needs for children affected by chronic conditions. Persistently lower health service use for acute infections is likely attributable to the decreased social contact. Sustained and targeted mental health support is essential to address the potentially increased demand for services among children during and beyond the pandemic.FundingFinancial Markets Foundation for Children Chair (RL, NN), NHMRC Investigator Grant (APP1197940) (NN), NHMRC Career Development fellowship (GNT1158954) (SW)

SPATIAL REGRESSION IN THE NUMBER OF COVID-19 CASES IN BANDUNG CITY

ABSTRACTRegression method that pays attention to neighborhood or region is Spatial Regression with Spatial Autoregressive (SAR) and Spatial Error Model (SEM) approaches. This method is used as an approach for cases of the spread of Covid-19 , namely the number of active Covid-19 patients in the city of Bandung with the independent variable in the form of population per hectare. , population age 60 and over, poverty and population not working. The results of the Moran index test show that there is a spatial effect on the spread of positive active Covid-19 patients, and the SAR model is better than the classical linear regression model (OLS; Ordinary Least Square) and SEM with variables/factors having a significant effect, namely population 60 and over, poverty and residents do not work. The AIC value of the SEM model is 262.845 with a coefficient of determination (R-squared) of 0.457302. Keywords: Spatial Regression, OLS, SAR, AIC, Coefficient of Determination.

The Driving Factors of Subtropical Mature Forest Productivity: Stand Structure Matters

Forest productivity (increment of above-ground biomass) is determined by biodiversity but also by stand structure attributes. However, the relative strengths of these drivers in determining productivity remain controversial in subtropical forests. In this study, we analyzed a tree growth data from 500 plots with in a 20 ha mature subtropical forest in eastern China. We used spatial simultaneous autoregressive error models to examine the effects of diversity variables (species richness, evenness, and composition), stand structural attributes (stand density, tree size range and diversity), environmental factors (topography and soil), and initial above-ground biomass (AGB) on productivity. We also applied structural equation models to quantify the relative importance of diversity, stand structure, environmental factors, and initial AGB in determining forest productivity. Our results showed that stand structure together with diversity and initial AGB governed forest productivity. Tree size diversity (DBH Shannon’s diversity index) had the largest positive effect on forest productivity. These results provide new evidence that structural explanatory variables have greater contributions to productivity for mature subtropical forests, strongly supporting the niche complementarity hypothesis. Our work highlights the importance of tree size diversity in promoting high forest productivity, and suggests that regulating and conserving complexity of forest stand structure should be among the most important goals in subtropical forest management.

Application of Spatial Modeling of Biophysical Variables in an Urbanized Area in the Amazon: The Case of the Metropolitan Area of Belém-Pará

Abstract The type of land use and land cover plays a decisive role in land surface temperature (LST). As cities are composed of varied covers, including vegetation, built-up areas, buildings, roads and areas without vegetation, understanding LST patterns in complex urban spaces is becoming increasingly important. The present study investigated the relationship between LST and albedo, NDVI, NDWI, NDBI and NDBaI in the period between 1994 and 2017. Images from Thematic Mapper (TM) and Thermal Infrared Sensor (TIRS) onboard the Landsat 5 and 8 satellites, respectively, were used in the study. The images were processed, resampled (spatial resolution of 120 m) in the environment of the QGIS 3.0 software and, finally, centroids were extracted resulting in a total of 1252 points. A classical regression (CR) model was applied to the variables, followed by spatial autoregressive (SARM) and spatial error (SEM) models, and the results were compared using accuracy indices. The results showed that the highest correlation coefficient was found between albedo and NDBaI (r = 0.88). The relationship between albedo and LST (r = 0.7) was also positive and significant at р &lt; 0.05. The global Moran's I index showed spatial dependence and non-stationarity of the LST (I = 0.44). The SEM presented the best accuracy metrics (AIC = 3307.15 and R2 = 0.65) for the metropolitan region of Belém, explaining considerably more variations in the relationship between explanatory factors and LST when compared to conventional CR models.