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Purpose: Organisational slack has been widely considered in strategic management, but there is a gap in understanding the process of accumulation and application of slack resources. From a dynamic perspective and over an extended period of time, this paper analyses the management of slack resources and evaluates whether the different behaviours, in relation to the accumulation and consumption of slack resources, have any effect on performance. Design/methodology/approach: The resource-based view and the dynamic extension of this theory, i.e. resource management and resource orchestration, were analysed in order to evaluate how slack resources can be managed and generate value. Assuming a configurational approach, the analysis was structured into two stages to answer the proposed hypothesis. The first stage studied whether there were different patterns of management of slack resources over time using the DistatisR package. The second stage evaluated which behaviours had the greatest impact in terms of profitability by using a dynamic panel data regression. Findings: Three different types of slack resource management were found in companies: efficient, effective and erratic. Different types do not have the same impact on performance. Originality/value: The dynamic management of slack resources has scarcely been considered, even during periods of crisis and economic expansion. This research advances the understanding of how firms transform slack resources into performance from a dynamic perspective. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This study aims to examine the effects of green financing through pollution control bonds (PCBs) on environmental performance. Design/methodology/approach: This study is based on a panel of 189 US energy utility firms observed over the period, 2011–2021 ; this study applies Generalized Method of Moments regressions. Findings: This study found that PCBs positively affect environmental performance (aggregate measure, greenhouse emissions, waste landfill, waste incineration and waste recycling). These findings remain robust when this study considers alternative measures of PCBs and environmental performance, the quantile regression method and some firms' attributes such as financial performance and firm age. Practical implications: The results indicate that US energy utility firms have to adopt more PCBs. This study helps researchers, practitioners, shareholders, bondholders, equity analysts and local authorities such as the California Pollution Control Financing Authority, municipalities and investors understand PCBs issuance, usefulness and relevance. Originality/value: To the best of the authors' knowledge, this study is the first to explore the effectiveness of PCBs in reducing pollution. [ABSTRACT FROM AUTHOR]

Background: It has been claimed that the advent of modern antidepressants has reduced the suicide rate. Aims: To examine the correlation between the suicide rate and the prescription of antidepressants. Method: A dynamic regression was employed to analyze a 73-month-long, monthly time series between 2010 and 2016 in Hungary. The independent variable was the Defined Daily Dose value for the number of antidepressant (AD) prescriptions filled each month. Results: The models failed to show a significant association between the prescription of antidepressants and age- and sex-specific monthly suicide rates. Conclusions: The prescription of antidepressants in Hungary has had no impact on suicide rates. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the effect of innovation on environmental, social and governance (ESG) performance and, consequently, its influence on the economic and financial performance of companies. Design/methodology/approach: A quantitative and descriptive research was carried out based on secondary data from the Refinitiv Eikon® database, using the panel data regression technique, considering the constructs: innovation, ESG performance and economic and financial performance. Findings: The results showed that companies that tend to invest more financial resources in R&D are more likely to have higher ESG performance. In addition, companies that have higher ESG performance tend to have higher economic and financial performance. Practical implications: Managers may consider investing more resources in R&D to achieve superior ESG performance. They should be aware that ESG is a strategic tool for creating financial and nonfinancial value for the organization. More than the traditional preparation of a financial report, stakeholders demand another type of information: ESG information. Originality/value: The results confirm the basis of Stakeholder Theory, showing that the companies that meet the needs of all stakeholders tend to have greater economic and financial performance. ESG practices can include keeping employees motivated to work, improved corporate image in the eyes of customers, more satisfied suppliers and community and environment aligned with management. Therefore, these ESG initiatives are instrumental in protecting organizational objectives as well as increasing shareholder value. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the implications of natural gas rents and institutions as co-drivers of economic growth, focusing on the Gas Exporting Countries Forum (GECF) with panel data between 2001 and 2021. Design/methodology/approach: This research paper uses a specialised two stage estimator, the panel instrumental variable technique (panel IV), which takes care of the potential endogeneity issues in the model. Findings: The findings show that natural gas rent significantly impacts the economic growth of the GECF. On average, natural gas rent increases the sample's growth rate by about 2.634% percentage points in the short run. The result indicates that the qualities of institutions (political and economic) have a significant positive long-term effect on the economies of the GECF. In addition, the study's energy price volatility positively correlates with the countries' growth. Research limitations/implications: There might be a need to investigate the effects of natural gas rents and institutions as co-growth drivers in each country within the GECF. The likelihood exists that the impact of natural gas rents and institutions on economic growth at the country's level may differ from the outcome of such an experiment on the group level. Because of space and time limitations, this study could not carry out the specific country's investigation of natural gas rents and institutions as a co-growth driver. That limitation may constitute further study to advance this study to a new height. Practical implications: With good institutions, natural gas rent is likely to be an alternative growth driver for some economies that rely on fossil fuels like oil as a growth driver. By extension, the GECF has the potential to rival Organisation of Petroleum Exporting Countries (OPEC) in the global energy market, particularly in achieving Sustainable Development Goal number seven. In essence, evidence in this study suggests that natural gas rent has long-term positive effects on the growth of the GECF, conditioned on good institutions. Moreover, the drive of global energy consumption towards sustainable energy usage is an economic blessing for the GECF. By extension, the demand for natural gas would continue to rise, creating opportunities to improve natural gas rents. By implication, the GECF would continue to benefit from the pursuit of sustainability as the world shifts towards energy consumption with less CO2. Originality/value: Firstly, this study models the qualities of institutions for the GECF. Secondly, to the best of the author's knowledge, this study is the first attempt to examine natural gas rents and the qualities of institutions as co-determinants of economic growth among the GECF (a potential cartel). [ABSTRACT FROM AUTHOR]

Abstract Backgroung Antimicrobial resistance (AMR) is on the rise worldwide. Tools such as dynamic regression (DR) models can correlate antimicrobial consumption (AMC) with AMR and predict future trends to help implement antimicrobial stewardship programs (ASPs). Main body We carried out a systematic review of the literature up to 2023/05/31, searching in PubMed, ScienceDirect and Web of Science. We screened 641 articles and finally included 28 studies using a DR model to study the correlation between AMC and AMR at a hospital scale, published in English or French. Country, bacterial species, type of sampling, antimicrobials, study duration and correlations between AMC and AMR were collected. The use of β-lactams was correlated with cephalosporin resistance, especially in Pseudomonas aeruginosa and Enterobacterales. Carbapenem consumption was correlated with carbapenem resistance, particularly in Pseudomonas aeruginosa, Klebsiella pneumoniae and Acinetobacter baumannii. Fluoroquinolone use was correlated with fluoroquinolone resistance in Gram-negative bacilli and methicillin resistance in Staphylococcus aureus. Multivariate DR models highlited that AMC explained from 19 to 96% of AMR variation, with a lag time between AMC and AMR variation of 2 to 4 months. Few studies have investigated the predictive capacity of DR models, which appear to be limited. Conclusion Despite their statistical robustness, DR models are not widely used. They confirmed the important role of fluoroquinolones, cephalosporins and carbapenems in the emergence of AMR. However, further studies are needed to assess their predictive capacity and usefulness for ASPs.

Backgroung: Antimicrobial resistance (AMR) is on the rise worldwide. Tools such as dynamic regression (DR) models can correlate antimicrobial consumption (AMC) with AMR and predict future trends to help implement antimicrobial stewardship programs (ASPs). Main body: We carried out a systematic review of the literature up to 2023/05/31, searching in PubMed, ScienceDirect and Web of Science. We screened 641 articles and finally included 28 studies using a DR model to study the correlation between AMC and AMR at a hospital scale, published in English or French. Country, bacterial species, type of sampling, antimicrobials, study duration and correlations between AMC and AMR were collected. The use of β-lactams was correlated with cephalosporin resistance, especially in Pseudomonas aeruginosa and Enterobacterales. Carbapenem consumption was correlated with carbapenem resistance, particularly in Pseudomonas aeruginosa, Klebsiella pneumoniae and Acinetobacter baumannii. Fluoroquinolone use was correlated with fluoroquinolone resistance in Gram-negative bacilli and methicillin resistance in Staphylococcus aureus. Multivariate DR models highlited that AMC explained from 19 to 96% of AMR variation, with a lag time between AMC and AMR variation of 2 to 4 months. Few studies have investigated the predictive capacity of DR models, which appear to be limited. Conclusion: Despite their statistical robustness, DR models are not widely used. They confirmed the important role of fluoroquinolones, cephalosporins and carbapenems in the emergence of AMR. However, further studies are needed to assess their predictive capacity and usefulness for ASPs. [ABSTRACT FROM AUTHOR]

This study examine the significant contribution of Nigeria Stock Market towards the Gross Domestic Product. The main objective of this work is to assess the level of Stock Market stability in Nigeria by applying Dynamic Regression to modeling of Gross Domestic Product (GDP) using the Quarterly Gross Domestic Product at 1990 constant basic prices of Nigeria from the first quarter of 1985 to the third quarter of 2013. Quarterly all Share Index of the Nigerian Stock Exchange from the first quarter of 1985 to the fourth quarter of 2013 and finally Quarterly Market Capitalization of the Nigerian Stock Exchange for the same period. It was observed that the relationship is statistically significant, which allows the stock market to have an impact on the Nigerian Economy. It was concluded that Government and Economic planner should take more advantage of statistical tools in studying the relationship between the GDP movement and the Stock Exchange. [ABSTRACT FROM AUTHOR]

Diagnostic tests usually need to operate at a high sensitivity or specificity level in practice. Accordingly, specificity at the controlled sensitivity, or vice versa, is a clinically sensible performance metric for evaluating continuous biomarkers. Meanwhile, the performance of a biomarker may vary across sub‐populations as defined by covariates, and covariate‐specific evaluation can be informative. In this article, we develop a novel modeling and estimation method for covariate‐specific specificity at a controlled sensitivity level. Unlike existing methods which typically adopt elaborate models of covariate effects over the entire biomarker distribution, our approach models covariate effects locally at a specific sensitivity level of interest. We also extend our proposed model to handle the whole continuum of sensitivities via dynamic regression and derive covariate‐specific ROC curves. We provide the variance estimation through bootstrapping. The asymptotic properties are established. We conduct extensive simulation studies to evaluate the performance of our proposed methods in comparison with existing methods, and further illustrate the applications in two clinical studies for aggressive prostate cancer. [ABSTRACT FROM AUTHOR]

Car-following behavior modeling is critical for understanding traffic flow dynamics and developing high-fidelity microscopic simulation models. Most existing impulse-response car-following models prioritize computational efficiency and interpretability by using a parsimonious nonlinear function based on immediate preceding state observations. However, this approach disregards historical information, limiting its ability to explain real-world driving data. Consequently, serially correlated residuals are commonly observed when calibrating these models with actual trajectory data, hindering their ability to capture complex and stochastic phenomena. To address this limitation, we propose a dynamic regression framework incorporating time series models, such as autoregressive processes, to capture error dynamics. This statistically rigorous calibration outperforms the simple assumption of independent errors and enables more accurate simulation and prediction by leveraging higher-order historical information. We validate the effectiveness of our framework using HighD and OpenACC data, demonstrating improved probabilistic simulations. In summary, our framework preserves the parsimonious nature of traditional car-following models while offering enhanced probabilistic simulations. The code of this work is available at https://github.com/Chengyuan-Zhang/IDM_Bayesian_Calibration. • Novel calibration for car-following models using dynamic regression framework. • Framework maintains a parsimonious nature with better probabilistic simulations. • Efficient probabilistic simulation with reliable uncertainty quantification. • Captures stochastic driving behavior, replicating real-world traffic. [ABSTRACT FROM AUTHOR]

Accurate and reliable reservoir inflow forecast is instrumental to the efficient operation of the hydroelectric power systems. It has been discovered that natural and anthropogenic aerosols have a great influence on meteorological variables such as temperature, snow water equivalent, and precipitation, which in turn impact the reservoir inflow. Therefore, it is imperative for us to quantify the impact of aerosols on reservoir inflow and to incorporate the aerosol models into future reservoir inflow forecasting models. In this paper, a comprehensive framework was developed to quantify the impact of aerosols on reservoir inflow by integrating the Weather Research and Forecasting model with Chemistry (WRF-Chem) and a dynamic regression model. The statistical dynamic regression model produces forecasts for reservoir inflow based on the meteorological output variables from the WRF-Chem model. The case study was performed on the Florence Lake and Lake Thomas Alva Edison of the Big Creek Hydroelectric Project in the San Joaquin Region. The simulation results show that the presence of aerosols results in a significant reduction of annual reservoir inflow by 4-14%. In the summer, aerosols reduce precipitation, snow water equivalent, and snowmelt that leads to a reduction in inflow by 11-26%. In the spring, aerosols increase temperature and snowmelt which leads to an increase in inflow by 0.6-2%. Aerosols significantly reduce the amount of inflow in the summer when the marginal value of water is extremely high and slightly increase the inflow in the spring when the run-off risk is high. In summary, the presence of aerosols is detrimental to the optimal utilization of hydroelectric power systems.

Randomized machine learning focuses on problems with considerable uncertainty in data and models. Machine learning algorithms are formulated in terms of a functional entropy-linear programming problem. We adapt these algorithms to forecasting problems on an example of the evolution of thermokarst lakes area in permafrost zones. Thermokarst lakes generate methane, a greenhouse gas affecting climate change. We propose randomized machine learning procedures using dynamic regression models with random parameters and retrospective data (climatic parameters and remote sensing of the Earth's surface). The randomized machine learning algorithm developed below estimates the probability density functions of model parameters and measurement noises. Randomized forecasting is implemented as algorithms transforming the optimal distributions into the corresponding random sequences (sampling algorithms). The randomized forecasting procedures and technologies are trained, tested, and then applied to forecast the evolution of thermokarst lakes area in Western Siberia. [ABSTRACT FROM AUTHOR]

Recurrent event data with a terminal event commonly arise in many longitudinal follow‐up studies. This article proposes a class of dynamic semiparametric transformation models for the marginal mean functions of the recurrent events with a terminal event, where some covariate effects may be time‐varying. An estimation procedure is developed for the model parameters, and the asymptotic properties of the resulting estimators are established. In addition, relevant significance tests are suggested for examining whether or not covariate effects vary with time, and a model checking procedure is presented for assessing the adequacy of the proposed models. The finite sample performance of the proposed estimators is examined through simulation studies, and an application to a medical cost study of chronic heart failure patients is provided. [ABSTRACT FROM AUTHOR]

Background: Non-pharmaceutical interventions (NPIs) aim to reduce the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections mostly by limiting contacts between people where virus transmission can occur. However, NPIs limit social interactions and have negative impacts on economic, physical, mental and social well-being. It is, therefore, important to assess the impact of NPIs on reducing the number of coronavirus disease 2019 (COVID-19) cases and hospitalizations to justify their use. Methods: Dynamic regression models accounting for autocorrelation in time series data were used with data from six Canadian provinces (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Québec) to assess 1) the effect of NPIs (measured using a stringency index) on SARS-CoV-2 transmission (measured by the effective reproduction number), and 2) the effect of the number of hospitalized COVID-19 patients on the stringency index. Results: Increasing stringency index was associated with a statistically significant decrease in the transmission of SARS-CoV-2 in Alberta, Saskatchewan, Manitoba, Ontario and Québec. The effect of stringency on transmission was time-lagged in all of these provinces except for Ontario. In all provinces except for Saskatchewan, increasing hospitalization rates were associated with a statistically significant increase in the stringency index. The effect of hospitalization on stringency was time-lagged. Conclusion: These results suggest that NPIs have been effective in Canadian provinces, and that their implementation has been, in part, a response to increasing hospitalization rates of COVID-19 patients. [ABSTRACT FROM AUTHOR]

Abrupt changes in the Lake Urmia water level have been addressed in many studies, and yet the link between the water level decline and hydro‐meteorological variables in the basin is a major topic for debate between researchers. In this study, a set of data‐driven techniques is used to investigate the components of the water budget in Lake Urmia. Then, the rate of monthly depth differences (DD), precipitation (P), evaporation (E), potential groundwater head (G), and streamflow (Q) time series between 1974 and 2014 are used in the analysis. Several scenarios and strategies are developed by considering the major changes in the year‐2000, which is believed to be the initiation of the hydrological encroachment in the basin. Simple water budget (WB), dynamic regression (DR), and symbolic regression (SR) techniques are used to simulate the DD with consideration to P, E, G, and Q. Alternatively, the effect of the year 1997 as the potential base‐line for the initiation of significant meteorological trends in the basin is investigated. Conducted analysis showed that the DR models of an autoregressive moving average together with multiple exogenous inputs provide an approximate R2: 0.7 as the best alternative among the selected models. It is shown that the Q and G depict abrupt changes compared to the P and E, while either the year 1997 (climate effect) or the year 2000 (encroachment effect) is considered as the baseline in the study. Plain Language Summary: We have investigated the main scenarios regarding the Lake Urmia water level decline. For this, a conceptual water budget approach together with sophisticated data mining techniques and mathematical models are used. Then based on the best fit model results and prior details obtained in data analysis we have questioned the credibility of different hydrological scenarios. Key Points: Lake water budgetMathematical modelHydrological data analysis [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to investigate the synergy between sectoral output, energy use and CO2 emission with other factors for a panel of South Asian economies including Afghanistan, Bangladesh, Bhutan, India, Pakistan, Maldives, Nepal and Sri Lanka. Design/methodology/approach: The analysis is done using annual panel data from 1980–2019 using dynamic ordinary least squares (DOLS), fully modified OLS (FMOLS) and Toda-Yamamoto techniques. Findings: Empirical findings reveal the existence of a statistically significant long-run cointegrating relationship between energy use, sectoral output such as agricultural, industry and service gross domestic product (GDP), globalization, urbanization and CO2 emission. DOLS and FMOLS result posits that in the case of the South Asian region agriculture GDP does not contribute to increasing CO2 emission while service and industrial GDP is responsible for increasing CO2 emission along with urban population, energy use and to some extent globalization. More remarkably, the contribution of the service GDP is greater than the other two sectoral outputs in increasing CO2 emission with a feedback hypothesis. Practical implications: As CO2 emission is a global phenomenon with a cross-boundary effect, these empirical findings might contribute to formulating implementable energy and environmental policies to sustain growth, as well as to protect the environment in the regional context. Originality/value: The study contributes to the literature by providing an empirical investigation of South Asia incorporating the contribution of sectoral output to understand the potential contribution of each sector on energy and emission. This is the first study on the South Asian context from the perspective of sectoral output, energy and emission. [ABSTRACT FROM AUTHOR]

In the last decade, tourists gradually became more connected, gathering information about their destinations on the Internet. This suggests that the readily available search data is increasingly more relevant for up to date descriptive and predictive analysis regarding tourism data. This benefits the tourism sector, which is a very important sector for the economy of many countries, including Portugal. In this work, the relation between guests' data in tourism accommodation in Portugal and the search data of the main destinations in the country is analyzed for the last few years. The search data used is from Google Trends, considering different lag values. Short horizon tourist demand forecasts were produced considering exponential smoothing (ETS), autoregressive integrated moving average (ARIMA) and dynamic regression models, through a cross-validation experimental setup. It was evaluated if the inclusion of search data improved the forecasts. The study was performed considering the eight main guests' countries of origin, which are: Portugal, Great Britain, Ireland, Spain, France, Germany, Brazil, and Italy. The results indicate correlation between moderate and very strong for all countries. Furthermore, it was possible to observe that the correlation was higher for almost all the countries when the search data considered was lagged between 0 and 3 months. Regarding the comparative analysis between the forecasting models, the ARIMA models presented the best results for most of the countries. [ABSTRACT FROM AUTHOR]

Soot blowers are employed in a predefined sequence and schedule to clean the ash particles on heat exchanger surface. However, considering the detrimental effects of frequent soot blow operation a criteria based system has to be adopted that involves soot blow activation based on a predefined cleanliness level. This insists the need for an online monitoring model to predict the Cleanliness Factor (CF). This paper analyzes the deposition of ash in power plant reheater with the objective of developing accurate prediction model for CF using Autoregressive Integrated Moving Average with eXplanatory variables (ARIMAX) model. In practice the covariates that should be included in the model is not known a priori and often with more number of candidate variables. The findings in this work reveal that the ARIMAX model including flue gas input temperature, 1 lag value of flue gas output temperatures, air flow rate, coal flow rate and attemperator flow rate in time series analysis of CF dataset produces a more robust predictive model. The model performance is quantified by two indicators namely and Root Mean Square Error (RMSE) and Akakie Information Criteria (AIC). To ensure the adequacy of the model residual diagnostics was performed which revealed that the Auto Correlation Function (ACF) plot of the residuals are uncorrelated and there is no considerable departure from white noise as the test statistic clearly shows that the p-values exceeds the 5% significance level for all lag orders. The recommendation suggested in this study can be applied to criteria based soot-blow operation where the soot blowers are operated when a predefined CF is reached. [ABSTRACT FROM AUTHOR]

In this paper, we present a combination of three forecast models, ARIMA, exponential smoothing and dynamic regression, in order to predict the West Texas Intermediate (WTI) crude oil spot price and the Brent North Sea (Brent) crude oil spot price. Using samples from the period between January 1994 and June 2012 (in-sample), we identify the parameters and estimate the models. The validated models are combined to perform a forecast out-of-sample between July 2012 and June 2013. The results demonstrate that among the three models tested in-sample with Brent Prices, based on the MAPE measurement error, the ARIMA (2,1,8) model produced the best result, and the dynamic regression model was the best in-sample model for the WTI price. In the validation phase, the dynamic regression models did not prove to be valid, and therefore the combinations are performed only with the ARIMA and exponential smoothing models. For both proxies of oil prices, the combination of forecasts using ARIMA and exponential smoothing (out-of-sample) performed better than individual ARIMA and exponential models and also better than our benchmark models (naive forecast and Neural Network model). Based on the results, it can be inferred that using to a combination of forecasts to predict WTI and Brent spot prices is promising. We also point out that the selected model is easily replicable in spreadsheets and forecasting software and is based only on the past or lagged WTI or Brent values for future predictions. [ABSTRACT FROM PUBLISHER]

Airborne allergenic pollen impact the health of a great part of the global population. Under climate change conditions, the abundance of airborne pollen has been rising dramatically and so is the effect on sensitized individuals. The first line of allergy management is allergen avoidance, which, to date, is by rule achieved via forecasting of daily pollen concentrations. The aim of this study was to elaborate on 3-hourly predictive models, one of the very few to the best of our knowledge, attempting to forecast pollen concentration based on near-real-time automatic pollen measurements. The study was conducted in Augsburg, Germany, during four years (2016–2019) focusing on Betula and Poaceae pollen, the most abundant and allergenic in temperate climates. ARIMA and dynamic regression models were employed, as well as machine learning techniques, viz. artificial neural networks and neural network autoregression models. Air temperature, relative humidity, precipitation, air pressure, sunshine duration, diffuse radiation, and wind speed were additionally considered for the development of the models. It was found that air temperature and precipitation were the most significant variables for the prediction of airborne pollen concentrations. At such fine temporal resolution, our forecasting models performed well showing their ability to explain most of the variability of pollen concentrations for both taxa. However, predictive power of Betula forecasting model was higher achieving R2 up to 0.62, whereas Poaceae up to 0.55. Neural autoregression was superior in forecasting Betula pollen concentrations, whereas, for Poaceae, seasonal ARIMA performed best. The good performance of seasonal ARIMA in describing variability of pollen concentrations of both examined taxa suggests an important role of plants' phenology in observed pollen abundance. The present study provides novel insight on per-hour forecasts to be used in real-time mobile apps by pollen allergic patients. Despite the huge need for real-time, short-term predictions for everyday clinical practice, extreme weather events, like in the year 2019 in our case, still comprise an obstacle toward highly performing forecasts at such fine timescales, highlighting that there is still a way to go to this direction. [ABSTRACT FROM AUTHOR]

This article raises the question of whether the political scandals that take place in Lithuania have an effect on public trust in Lithuania’s state institutions and, if so, what kind of an effect can be perceived. The following institutions of the Republic of Lithuania were selected for analysis: the Government, the Parliament, the Institution of the President, the Special Investigation Service, and the State Security Department. The analysis covers the 2004–2016 years (inclusive) period. The links between political scandals and trust in state institutions are analyzed based on an institutional approach to changes in governmental trust, as well as a non-functional approach to political scandals and the results of empirical studies analyzing political scandals and their relation to governmental trust. In order to determine links between political scandals and trust in state institutions, a dynamic linear regression with time series data was performed. The analysis suggests that political scandals that took place in Lithuania during the period of 2004–2016 had a substantial impact on changes in public trust in state institutions. In the case of all institutions, a negative short-term impact of political scandals on trust in these institutions has been identified. The results of the analysis also reveal that the extent of the negative impact of political scandals may depend on the level of intensity of the escalation of political scandals in the media and the type of political scandal.

Purpose: This study aims to focus on assessing how improving openness influences carbon dioxide (CO2) emissions in sub-Saharan Africa (SSA). Design/methodology/approach: This study focusses on 49 countries in SSA for the period 2000–2018 divided into: 44 countries in SSA for the period 2000–2012; and 49 countries for the period 2006–2018. Openness is measured in terms of trade and foreign direct investment (FDI) inflows. The empirical evidence is based on the generalised method of moments. Findings: The following main findings are established. First, enhancing trade openness has a net positive impact on CO2 emissions, while increasing FDI has a net negative impact. Second, the relationship between CO2 emissions and trade is a Kuznets shape, while the nexus between CO2 emissions and FDI inflows is a U-shape. Third, a minimum trade openness (imports plus exports) threshold of 100 (% of gross domestic product (GDP)) and 200 (% of GDP) is beneficial in promoting a green economy for the first and second samples, respectively. Fourth, FDI is beneficial for the green economy below critical masses of 28.571 of net FDI inflows (% of GDP) and 33.333 of net FDI inflows (% of GDP) for first and second samples, respectively. It follows from findings that while FDI can be effectively managed to reduce CO2 emissions, this may not be the case with trade openness because the corresponding thresholds for trade openness are closer to the maximum limit. Originality/value: This study complements the extant literature by providing critical masses of trade and FDI that are relevant in promoting the green economy in SSA. [ABSTRACT FROM AUTHOR]

Summary: This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia. [ABSTRACT FROM AUTHOR]

Copyright of Politologija is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In Brazil, the government has historically given low attention to the planning of telecommunication infrastructure, such as the prediction of the Internet bandwidth in the short and medium term, since this process can be slow and costly. Notably, smart city applications are impaired by this policy, because they depend on cost-benefit technology to support the Internet of Things. This paper presents a method for forecasting the Internet demand based on public data obtained from the International Telecommunication Union, the World Bank, and government agencies, using Brazil as a case study. The information inputs are associated with the population growth, and with social and technological development. The prediction process uses statistic concepts and models to infer the relationship between input variables and the data bandwidth rate. The proposed methodology is not restricted to the prediction of the Internet demand and may also be used to estimate other concerns for developing countries, such as oil, energy, and water consumption. The method is compared with other time series analysis models. The results reveal that factors related to innovation and technology significantly impact the annual projection of the Internet demand.

This article examines the relationship between observed claim frequencies in the automobile insurance line and the evolution of selected economic magnitudes. From a variety of economic variables, we aim to identify the main factors affecting claim frequencies, while controlling for other legislative and demographic factors. Through a dynamic regression model, the analysis is conductedfor three different categories of vehicles and for a variety of coverages. A comprehensive dataset from the main Spanish insurance companies is used to calibrate the model. The evidence might assist companies to improve ratemaking. [ABSTRACT FROM AUTHOR]

A new concept of machine learning based on the computer simulation of entropy-optimal randomized models is proposed. The procedures of randomized machine learning (RML) with "hard" and "soft" randomization are considered; the former imply the exact reproduction of empirical balances while the latter their rough reproduction with an accepted approximation criterion. RML algorithms are formulated as functional entropy-linear programming problems. Applications of RML procedures to text classification and the randomized forecasting of migratory interaction of regional systems are presented. [ABSTRACT FROM AUTHOR]

Abstract Coupling microbial dynamics with the complete dynamics of the packaging gases is still a challenge. In this work the microbial growth kinetic parameters for Pseudomonas and Lactic Acid Bacteria (LAB) in MAP are identified based on accurate estimation of diffusivity of gases and parameter scaled sensitivity approaches. The microbial dynamics are also compared with those estimated based on partial pressure measurement. Scaled sensitivity coefficient analysis using dissolved gases as variable inputs, shows that in most cases the only coefficients large enough for estimation were those for CO 2max-diss , and for μ max. The current data showed that dissolved gases led significant differences on the microbial parameter of CO 2max values when compared with the headspace gases. On the other hand, the (so-called) dissolved specific growth rate follows a clear trend down for both microorganisms in relation to the increase of the initial headspace CO 2. Finally, current results indicate a possible correlation between CO 2max - diss , CO 2max - headspace , and μ max as functions of CO 2init. [ABSTRACT FROM AUTHOR]

We propose a new forecasting procedure that includes randomized hierarchical dynamic regression models with random parameters, measurement noises and random input. We developed the technology of entropy-randomized machine learning, which includes the estimation of characteristics of a dynamic regression model and its testing by generating ensembles of predicted trajectories through the sampling of the entropy-optimal probability density functions of the model parameters and measurement noises. The density functions are determined at the learning stage by solving the constrained maximization problem of an information entropy functional subject to the empirical balances with real data. The proposed procedure is applied to the randomized forecasting of the daily electrical load in a regional power system. We construct a two-layer dynamic model of the daily electrical load. One of the layers describes the dependence of electrical load on ambient temperature while the other simulates the stochastic quasi-fluctuating temperature dynamics.