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One of the main challenges economists must face while studying economic sanctions is to simulate them in their models faithfully. It seems extremely hard to achieve without a dedicated variable that precisely imitates coercive economic measures. Thence, this short paper aims to offer a framework for the construction of economic sanction indexes. Researchers can use our multipurpose index modelling to simulate economic punishment in their econometric models. If our work is framed within the Ukrainian crisis case and European economic sanctions against Russia, the method we provide can be used in any other case study. It is a great alternative to the standard use of dummy variables that witness sanctions' implementation and withdrawal. Additionally, our proposal allows calibrating the weight attributed to each sanction depending on its type, the sanction sender's ability to apply economic pressure, and the effect of time on sanctions' effectiveness. The first part of this paper details the methodology and the mathematical formalisation of our framework. It is used to build a sanction index that simulates sanctions against Russia during the Ukrainian crisis. The second part is an empirical study that compares our index to another one from the literature. Results reveal that our new sanction index witnesses the behaviour of sanctions more precisely, which leads to a stronger explanatory power. Moreover, it enhances the statistical significance of impulse-response functions generated by our SVAR models. Consequently, the framework for sanction index construction that is proposed in this paper successfully produced a reliable index that simulates the European Union's sanction against Russia in the Ukrainian crisis case. [ABSTRACT FROM AUTHOR]

• Weather effect on intercity travel demand is analyzed with additional consideration of weather difference between OD points. • Differences in rainfall and snowfall between OD points have significant impacts on intercity travel demand. • Snow and heavy rain have the lagging (two days) and advance (one day) effect on freeway origin-destination volume. • Intercity travelers with less relevant experience are more sensitive to adverse weather. • Findings are helpful in providing intercity travelers with more accurate information and guidance services. Existing literature has confirmed that severe weather events have a negative impact on travel behavior. Considering the features of long distances and long durations, intercity travel may be affected by the weather at the origin (O) point and at the destination (D) point. This paper aims to reveal the relationship between weather and OD volume of intercity travel by additional considerations of the weather difference between OD points and the lagging effect of various severe weather conditions. Travel data from toll collection system of freeway network in Shandong Province of China is extracted to investigate the weather effect on intercity travel demand. Three types of real-time weather variables (continuous variables of basic weather, dummy variables of severe weather, and weather difference variables between OD points) and two types of non-real-time weather variables (lagging variables and advance variables of severe weather) are combined to systematically analyze the weather effect. The results show that, temperature value and wind speed have no significant impact on intercity travel. Hot day, cold day, foggy day, strong breeze, heavy rain, rainfall, snowfall and visibility have significant impacts on freeway OD volume. Among these weather events, fog, heavy rain and snow usually have the most severe impact. Except for the weather at the O point, the differences in rainfall and snowfall between OD points have significant impacts on intercity travel demand, while the differences in temperature, wind speed, visibility between OD points do not. In addition, snow and heavy rain have the lagging and advance effects on freeway OD volume. The advance effect of snow and heavy rain lasts for one day and the lagging effect lasts for two days. Intercity travel demand is very sensitive to adverse weather on weekend days and during the afternoon. Comparative analysis of multiple OD pairs indicates that intercity travelers with less relevant experience are more sensitive to adverse weather. Findings from the paper will provide transport institutions with a practical guide for systematically investigating the weather effect on intercity travel demand. It can be applied in the intelligent transportation system to provide long-distance travelers with more accurate weather information and traffic guidance services. [ABSTRACT FROM AUTHOR]

• Impact of HSR on regional disparity examined. • Multiple disparity metrics for GRP, HSR coverage, travel time, frequency. • Inauguration of HSR has a positive correlation with provincial equity. • Positive effect diminishes with the spread of HSR coverage. • HSR accessibility has more significant effect on economic equity than frequency. This paper investigates the impacts of high-speed rail (HSR) development on regional equity in China during 2007–2017. The equity in terms of economic output and HSR service is characterized at national, divisional (Eastern, Central, and Western), and provincial levels. The Gini indices associated with prefectures' gross regional product (GRP) per capita, HSR connectivity and accessibility are measured to assess disparity from different aspects. Instead of measuring the effect of HSR entry by a dummy variable representing the presence of HSR service only, this paper estimates the effects of HSR by various terms regarding the HSR presence, network coverage, service quality, and equity of HSR development. In particular, the HSR network coverage is captured by the number of prefectures connected to HSR. Service quality is characterized by the HSR connectivity (frequency of HSR services) and accessibility (average travel time and economic potential). The frequency and accessibility related metrics are further decomposed into two sub-variables representing the intra- and inter-provincial metrics to differentiate the service availability and travel time between prefectures in a same or different province(s). The influence between neighboring or adjoining provinces on each other's economic equity is identified by spatial autocorrelation effect. Our main findings include: 1) National equity is gradually improved in terms of both GRP and HSR developments in China between 2007 and 2017; the provinces or divisions with larger GRP tend to be less equitable; 2) The inauguration of HSR has positive correlation with provincial equity; however, the positive effect diminishes with the spread of HSR coverage; 3) HSR accessibility (average travel time and economic potential) has more significant effect on provincial economic equity than frequency; 4) Effects of intra-provincial accessibility and connectivity (HSR service within the province) dominate inter-provincial metrics (services to other provinces); 5) The prosperous and balanced HSR development in neighboring provinces helps promote provincial economic equity of one another. [ABSTRACT FROM AUTHOR]

This is the second part of a two-part paper on operations on fuzzy sets that may not share the same domain of definition. Part I described several ways of representing variable-domain fuzzy sets and introduced a few basic variable-domain fuzzy set operations. The present (largely self-contained) Part II further develops the theory of variable-domain fuzzy sets, based primarily on the representation of variable domains by a dummy membership degree that indicates the out-of-domain assignment error. We first describe several families of algebraic operations on the extended set of membership degrees; these are later employed in the investigation of basic notions of variable-domain fuzzy set theory. The fuzzy set operations studied in this paper include variable-domain variants of unions and intersections, kernels and supports, heights and plinths, equalities and inclusions, Cartesian products, and fuzzy relational compositions. Besides the initial examination of these variable-domain notions, our aim is to demonstrate the viability of variable-domain fuzzy set theory and highlight the similarity as well as differences between the fixed- and variable-domain treatments of fuzzy sets. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between competition and innovation at the firm level. Recent papers (Aghion et al., 2005; Askenazy et al., 2013; Rafique Hashmi, 2013) advocate a non-linear relationship, conciliating the Schumpeterian and Arrowian views. In this paper, the effect of competition on innovation is studied at the firm level using a large dataset for France coming from the annual surveys on R&D, covering the period 2000–2013, where a dummy variable is available for product or process innovation. The econometric results do not confirm the inverted-U shape relationship between the Lerner index and innovation, found by Aghion et al. (2005). This may be due to the use of firm-level data rather than industry-level data. However, for most firms, there is a negative effect of competition on innovation, meaning that more competition in the industry or a small market share has a negative effect on the propensity to innovate, either in products or processes, and the effect seems to be slightly stronger for product than for process innovation. [ABSTRACT FROM AUTHOR]

Our study investigates the effects of foreign ownership on firm performance with respect to profitability, productivity, export intensity, and skills acquisition in Sub-Saharan African (SSA) countries. This is particularly important given that in the last three decades, the stock of FDI has increased by over 20,000% in SSA countries. To achieve the objectives of this paper, we employed firm- and country-level data from the World Bank Enterprise Surveys and the World Bank Development Indicators, respectively. Results of the least squares dummy variable (LSDV) and propensity score matching (PSM) estimations showed that foreign ownership was positively associated with higher levels of financial profitability, productivity, exports, and skilled labour acquisition. Policy implications were deduced from the findings. [ABSTRACT FROM AUTHOR]

This paper analyses the overall trend of key macroeconomic variables of BRICS nations impacted by the US financial crises (2008–2009) through structural break analysis for period 2000–2013. Dummy variable model using exogenous break-date examines intercept and trend shift simultaneously in a single linear equation. The analysis statistically establishes that different variable of the countries endured varying level (severity) of the crisis impacted. Results found both immediate and lagged manifestation of the crisis impact on variables. The study's exclusivity lies in exposing the exact period of crisis impact on each variable and also in capturing their exact trend movements. The paper contributes in isolating sensitive variables (sensitive/resilient to exogenous shocks) for further research on crises. [ABSTRACT FROM AUTHOR]

• The study analyzes the effects of weight-based pricing on municipal waste. • It accounts for the dynamic relationship between variables. • The remarkable decrease is observed only just after the introduction of pricing. • Price elasticities of weight-based pricing are smaller than volume-based one at first. This paper analyzes the effects of weight-based pricing on municipal waste generation in Flanders (Belgium) by applying the bias-corrected least squares dummy variables estimation model to account for the dynamic relationship. The study also considers yearly weight-based pricing dummy variables to clarify the annual effects after its introducing and price elasticities of weight-based pricing systems from the both viewpoints of short and long terms. The results by the dynamic panel estimations showed that the continuous participation of weight-based pricing decreases the amount of residual waste significantly by approximately 10.4%. However, the remarkable decrease (approximately 21.4%) was observed only in the first year after the introduction and the reduction effect gradually decreases following its introduction and it disappears in five years. The study also showed that price elasticities of weight-based pricing were smaller than that of volume-based pricing for five years after its introduction and consequently the difference between them disappeared in five years. In addition, the study indicated that the estimation results by the non-dynamic fixed effects model overestimated the long-term effects in weight-based pricing and price effects while underestimating the short-term effect by its introduction. The study suggested that we should consider dynamic effects and remove the bias from the least squares dummy variables estimators when we examine the effects of weight-based pricing. [ABSTRACT FROM AUTHOR]

Forecasting bioenergy feedstock commodity volatility has received significant attention due to its importance in biofuel production and household consumption. Several extreme events, including the COVID-19 pandemic, have sparked interest in studying the role of structural breaks on volatility modeling and prediction in these markets. This study extensively examines the prediction performance of econometric models at multiple horizons using a rolling-window approach, with and without accommodating structural changes. We exploit the ICSS algorithm to determine the in-sample estimation windows to accommodate structural breaks. We extend the procedure beyond GARCH-class models. Also, the detected break information defines the regime dummies. The study innovatively evaluates the prediction performance of specific GARCH-class models by incorporating binary variables for sudden shifts in unconditional variance. Our findings reveal that accounting for the endogenously detected structural breaks through the dummy variables leads to considerable forecast accuracy gains. • This paper focuses on modeling and forecasting the volatility of bioenergy energy crop markets. • We thoroughly examine different econometric models with and without accommodating structural breaks at multiple horizons. • Some models rely on the adjusted ICSS procedure to determine the size of the estimation windows and define regime dummies. • Accounting for the structural breaks results in considerable accuracy gains in point and direction forecasts. [ABSTRACT FROM AUTHOR]

Highlights • Outside the crisis period financial development promotes economic growth, while during the crisis it has an adverse effect on economic activity. • During the recent sub-prime crises the capital adequacy of banks promoted the stability the financial system. • The degree of international trade openness in the economy of a country was the primary factor that led growth during both crisis periods. • Further investigation on the unconventional financial development measures that lead to economic growth after the crisis is required. Abstract This paper aims to examine the relationship between financial development and economic growth on the face of the recent financial crisis, using a panel dataset of 26 European Union countries over the period 1990–2016. The empirical approach uses multiplicative dummies to compare two distinct sub-periods before/after the crisis. The results show that before crisis, financial development promoted economic growth, while after the crisis it hindered economic activity. Also, the findings suggest that during the years 2008 and 2009 the capital adequacy of banks protected depositors and promoted the stability of the financial system. [ABSTRACT FROM AUTHOR]

Abstract: This paper tests the Feldstein–Horioka “puzzle” for the two richest countries of the world: Japan and the USA. For this purpose it employs three different cointegration tests that are applied to the Feldstein–Horioka long-run investment–saving equation in conjunction with the unit root test considering two structural shifts. A novel aspect of the paper is that it determines the number of breaks solely from a scrutiny of the data and that in constructing the dummy variables for the breaks it uses the endogenously determined break dates. It shows that allowing for structural shifts eliminates the “puzzle” both for Japan and the USA. [Copyright &y& Elsevier]

• The paper proposes a method to cluster categorical features in Generalized Linear Models. • The proposed approach uses a numerical method guided by the learning performance. • The underlying structure of the categories and their relationship is identified using proximity graphs. • Complexity is reduced and accuracy results are competitive against benchmark one-hot encoding of categorical features. We propose a method to reduce the complexity of Generalized Linear Models in the presence of categorical predictors. The traditional one-hot encoding, where each category is represented by a dummy variable, can be wasteful, difficult to interpret, and prone to overfitting, especially when dealing with high-cardinality categorical predictors. This paper addresses these challenges by finding a reduced representation of the categorical predictors by clustering their categories. This is done through a numerical method which aims to preserve (or even, improve) accuracy, while reducing the number of coefficients to be estimated for the categorical predictors. Thanks to its design, we are able to derive a proximity measure between categories of a categorical predictor that can be easily visualized. We illustrate the performance of our approach in real-world classification and count-data datasets where we see that clustering the categorical predictors reduces complexity substantially without harming accuracy. [ABSTRACT FROM AUTHOR]

This paper studies the development of renewable energy in China by examining the driving force of environment quality, regulation and employment on renewable energy generation. We adopt renewable energy as a metric for environment quality, and test the relationship between renewable energy and income using Environment Kuznets Curve (EKC) theories. The impact of employment on renewable energy is tested, and dummy variables are used to indicate when the regulation was in effect. The results show that there exists a quadratic relationship between renewable energy and income. But the results fail to provide that the renewable energy generation is a job creator when the lagged unemployment rate is included as an explaining variable. We consider the employment population, and the finding shows that the employment can promote the development of renewable energy. The regulation has significantly positive impacts on renewable energy. The interaction of income and employment show that along with the income increases, the impacts of employment on renewable energy decrease. Our findings are helpful for government to figure out the determinants for rising the renewable energy generation, and take efficient measures to promote its development. [ABSTRACT FROM AUTHOR]

Fertilizer overuse is a common phenomenon in global agroforestry production, and this overuse causes ecological destruction. The ability to accurately estimate the nutrient content of plant leaves in real-time would be a wonderful solution to reduce the degree of environmental damage. In recent years, remote sensing technology has been widely used in the diagnosis of crop nutrition in many countries. Most studies focus on optimal band selection or create new vegetation indices, but these studies have ignored the random impact of natural environmental factors on the estimated results. This paper proposed an estimation model of total nitrogen content (TNC) in sandalwood leaves that takes sampling season and site conditions as the dummy variable and random effect, respectively. Three forestry farms with different locations and site conditions were selected as study areas to enhance the universality of this model. Multispectral images of leaves were obtained using a low-cost five-band camera (RedEdge3, MicaSense, USA), and the experimental results indicate the following: (1) the growth of the tree height, crown width and stem effectively increased under the medium gradient level (N2), whereas a high gradient level (N3) significantly promoted all aspects except tree height; (2) the mean and variance of some image texture features of the G, RE and NIR band were significantly correlated with TNC at the 0.05 and 0.01 levels, and the texture mean value index (TMVI) proposed in this paper can improve the correlation with TNC; and (3) the results obtained using the nonlinear mixed-effects model with dummy variables improved the fitting degree and estimation accuracy compared with results of SVR and BPNN. This study demonstrates the advantages of using the nonlinear mixed-effects model with dummy variables to obtain a more reliable estimation model for the nutritional diagnosis of rare tree species. [ABSTRACT FROM AUTHOR]

Based on China's provincial panel data from 2011 to 2019, this paper examines the complex impact of new energy industry agglomeration on green innovation efficiency from the perspective of spatial mismatch of research and development (R&D) resources. The super slack-based measured directional distance function (super-SBM-DDF) is applied to measure the green innovation efficiency, while the least square dummy variable model (LSDV) is implemented to evaluate the mismatch of R&D resources in the new energy industry. Based on the generalized method of moment (GMM), the moderating effect model, and the dynamic threshold model are employed to investigate the threshold effect of new energy industry agglomeration on green innovation efficiency and the moderating effect of spatial mismatch of R&D resources. We come to the following findings. First, the agglomeration of China's new energy industry exhibits a "west-east-central" gradient distribution. In the meantime, there is a widespread insufficient mismatch of R&D resources in the majority of regions. Second, the agglomeration of the new energy industry can significantly boost the efficiency of green innovation. Moderate agglomeration can have a marginally enhancing effect, whereas excessive agglomeration will obscure the green innovation effect of new energy industry agglomeration. Third, the relationship between the new energy industry agglomeration and green innovation efficiency is negatively moderated by the mismatch of R&D resources, while the moderating effect is amplified by the mismatch of innovation financial resources. Fourth, there are two inflection points in the positive impact of new energy industry agglomeration on green innovation efficiency when the innovation resource mismatch is assigned as the threshold variable. [Display omitted] • The relationship among industry agglomeration, R&D resources mismatch and green innovation efficiency is investigated. • The mismatch of R&D resources is calculated by LSDV. The green innovation efficiency is measured by super-SBM-DDF. • The new energy industry presents a gradient distribution of "west-east-central" and there is a mismatch of R&D resources. • R&D resources mismatch hinders the positive effect of new energy industry agglomeration on green innovation efficiency. • Dynamic threshold panel model reveals the nonlinear relationship between agglomeration and green innovation efficiency. [ABSTRACT FROM AUTHOR]

• Deep learning models, LSTM, 1D-CNN outperformed other models w.r.t SMAPE. • RF, LSTM & 1D-CNN + LSTM are statistically similar in non-chaos-observed-ATMs. • In chaos-observed-ATMs, RF and LSTM was statistically significant. • GRU displayed less variability in predictions than other techniques w.r.t. SMAPE. • Including exogenous dummy variable significantly improved the predictions. This paper proposes to model chaos in the automated teller machine (ATM) cash withdrawal time series of a large Indian commercial bank and forecast the withdrawals using deep learning (DL) and hybrid DL methods. It also considers the influence of "day-of-the-week" on the results. We first modelled the chaos present in the withdrawal time series by reconstructing the state space of each series using the optimal lag and embedding dimension. This process converts the original univariate time series into a multi variate time series. The "day-of-the-week" dummy variable is converted into seven variables using one-hot encoding and augmented to the multivariate or univariate time series depending on whether chaos was present or absent. For forecasting the future cash withdrawals, we employed (i) statistical technique namely autoregressive integrated moving average (ARIMA), (ii) machine learning techniques such as random forest (RF), support vector regression (SVR), multi-layer perceptron (MLP), group method of data handling (GMDH), and general regression neural network (GRNN), and (iii) DL techniques such as long short term memory (LSTM) neural network, Gated Recurrent Unit (GRU) and 1-dimensional convolutional neural network (1D-CNN). We also explored hybrid DL techniques such as 1D-CNN + LSTM and 1D-CNN + GRU. We observed improvements in the forecasts for all techniques when "day-of-the-week" variable was included. It is observed that chaos was present in 28 ATMs, whereas in the remaining 22 ATMs chaos was absent. In both the cases, LSTM yielded the best Symmetric Mean Absolute Percentage Error (SMAPE) on the test data. However, LSTM showed statistically different performance than the 1D-CNN + LSTM in chaos category but equal performance with 1D-CNN in non– category yet statistically significant than 1D-CNN. [ABSTRACT FROM AUTHOR]

Pollution concern has been established as an important predictor of economic value ascription related to solid waste service improvement, such as recycling. However, the variable is not directly observable and as such, its influence is captured by existing applied economics papers via “yes” or “no” discrete-nominal measure. Such measure assumes the variable has one dimension. This leads to loss of information, considering its multi-dimensional structure in theory. It thus implies that, if a respondent in an economic valuation study indicates a support for an improvement signified by a “yes” response to valuation question, a uni-dimensional dummy variable cannot tell what dimension of concern the respondent subscribes to. Despite this importance of understanding the variable's dimension, it has rarely been explored. This study investigates such dimensions and our findings support the existence of both the theoretically espoused dual and tripartite factor models. This implies a possible misspecification in existing solid waste related studies that have most often captured the variables via uni-dimensional nominal-discrete measure. [ABSTRACT FROM AUTHOR]

Abstract: A critical issue facing a number of colleges and universities is how to allocate first year places to incoming students. The decision to admit students is often based on a number of factors, but a key statistic is a student''s high school grades. This paper reports on a case study of the subsequent performance at the University of Winnipeg of high school students from 84 Manitoba high schools. By tracking the university performance of students admitted for the years 1997–2002, we are able to estimate the likelihood of success of subsequent students based on their characteristics as well as their high school grades. In doing so, we use a number of alternative estimators including a Least Squares Dummy Variable Model and a Hierarchical Linear Model. The methodology should be of interest to admissions officers at other universities as an input into estimating the subsequent performance of first year students. [Copyright &y& Elsevier]

Abstract: For reasons of food safety, packaging and food contact materials must be submitted to migration tests. Testing of silicone moulds is often very laborious, since three replicate tests are required to decide about their compliancy. This paper presents a general modelling framework to predict the sample''s compliance or non-compliance using results of the first two migration tests. It compares the outcomes of models with multiple continuous predictors with a class of models involving latent and dummy variables. The model''s prediction ability was tested using cross and external validations, i.e. model revalidation each time a new measurement set became available. At the overall migration limit of 10mgdm−², the relative uncertainty on a prediction was estimated to be ∼10%. Taking the default values for α and β equal to 0.05, the maximum value that can be predicted for sample compliance was therefore 7mgdm−2. Beyond this limit the risk for false compliant results increases significantly, and a third migration test should be performed. The result of this latter test defines the sample''s compliance or non-compliance. Propositions for compliancy control inspired by the current dioxin control strategy are discussed. [Copyright &y& Elsevier]

Abstract: Balance-sheet data offer a potentially large number of candidate predictors of corporate financial failure. In this paper we provide a novel predictor selection procedure based on non-parametric regression and classification tree method (CART) and test its performance within a standard logit model. We show that a simple logit model with dummy variables created in accordance with the nodes of estimated classification tree outperforms both standard logit model with step-wise-selected financial ratios, and CART itself. On a population of Slovenian companies our method achieves remarkable rates of precision in out-of-sample bankruptcy prediction. Our selection method thus represents an efficient way of introducing non-linear effects of predictor variables on the default probability in standard single-index models like logit. These findings are robust to choice-based sampling of estimation samples. [Copyright &y& Elsevier]

Abstract: A novel classification method using -norm based regression is proposed in this paper. The -norm based loss function is robust to outliers or large variations distributed in the given data, and the -norm regularization term selects correlated samples across the whole training set with grouped sparsity. A probabilistic interpretation under the multiple task learning framework presents theoretical foundation for the optimal solution. Complexity analysis of our proposed classification algorithm is also presented. Several benchmark data sets including facial images and gene expression data are used for evaluating the effectiveness of the new proposed algorithm, and the results show competitive performance particularly better than those using dummy matrix as the response variables. This result is very useful since it is important for selecting appropriate response variables in classification oriented regression models. [Copyright &y& Elsevier]

Abstract: This paper focuses on low frequency airline service patronage using the data from a survey conducted at Imam Khomeini International Airport in Tehran, Iran. It uses Dominance-based Rough Set Approach (DRSA) for modeling passengers’ choices. We compare the method with discrete choice models using a prediction potential measure. Unlike discrete choice methods, it can use qualitative and categorized data without having to transform them into dummy variables. It is found that the prediction accuracy of the proposed technique is better than that of the discrete choice model for the case under consideration. [Copyright &y& Elsevier]

The analysis of behavior of portal visitors is one of the most important parts of web portal optimization. The results of the analysis are important for the further correction and improvement of web part organization. The aim of the paper is modeling of probabilities‘ accesses to the categories of web parts of portal. We deal with the access probabilities to the individual categories of faculty portal content depending on the day’s hour and the week’s day. The probabilities are estimated using multinomial logit model for employees and students separately. In logit models, in case of students and employees, the week’s days present statistically significant signs, representing dummy variables (MON, TUE,…) in the model. On the other hand, day’s hours representing with variables HOUR_DAY and their square HOUR_DAY_Q, are shown as statistically significant signs only in the case of students. These results correspond with the computing probabilities wherein the probabilities of access to web parts of the portal are more stable in the case of employees than of students during the day. The analysis provided us several interesting and surprising results. For instance, from the analysis, results follow that the part study is the most visited part by students in the evening and night hours. The analysis results confirmed general trends, for example the part announcements is the most visited part in morning’s hours, at the beginning of the week especially. All of the analysis results will help us to further optimize our web portal. This is especially point in level of portal adaptivity on the basis user and access hour on portal. [ABSTRACT FROM AUTHOR]

Abstract: Given the importance of trucking to the economic well being of a country and the safety concerns posed by the trucks, a study of large-truck crashes is critical. This paper contributes by undertaking an extensive analysis of the empirical factors affecting injury severity of large-truck crashes. Data from a recent, nationally representative sample of large-truck crashes are examined to determine the factors affecting the overall injury severity of these crashes. The explanatory factors include the characteristics of the crash, vehicle(s), and the driver(s). The injury severity was modeled using two measures. Several similarities and some differences were observed across the two models which underscore the need for improved accuracy in the assessment of injury severity of crashes. The estimated models capture the marginal effects of a variety of explanatory factors simultaneously. In particular, the models indicate the impacts of several driver behavior variables on the severity of the crashes, after controlling for a variety of other factors. For example, driver distraction (truck drivers), alcohol use (car drivers), and emotional factors (car drivers) are found to be associated with higher severity crashes. A further interesting finding is the strong statistical significance of several dummy variables that indicate missing data – these reflect how the nature of the crash itself could affect the completeness of the data. Future efforts should seek to collect such data more comprehensively so that the true effects of these aspects on the crash severity can be determined. [ABSTRACT FROM AUTHOR]

Abstract: Sensory professionals mostly used trained or expert panels for diagnostic purposes and only use consumers for hedonic assessments. In market research, consumers are not only used for hedonic assessments, but also for product diagnostic purposes (as is the case with Just About Right procedure). In the Ideal Profile method, consumers are also used for both tasks. They rate the perceived and ideal intensities and the acceptance of a series of products. The two sets of information (product description and hedonic data) are then used for product improvement. In this paper, the results of two methods of analysis – PLS on dummy variables and Fishbone method – will be compared. The objectives of this study were (1) to compare PLS and Fishbone method to determine their similarity in predicting the impact of the attributes on overall liking, and (2) to determine if the methods would return similar or contrasting conclusions. These methodologies have been applied to a study concerning 12 commercially-available women’s perfumes. Though the differences in model derivations caused some small dissimilarities, similar trends were found between products across methods for those perfumes far from the ideal. There was agreement regarding which attributes are too strong or too weak, and the order of importance of these attributes for liking. The two methods showed greater dissimilarity for perfumes that were already near the consumer’s ideal. [ABSTRACT FROM AUTHOR]

Abstract: Many empirical studies using high-frequency intraday data from a variety of markets indicate that PGARCH models give superior return volatility forecasts than those produced from standard GARCH models. This paper investigates into modelling approaches of four versions of PGARCH models of high-frequency data of Bursa Malaysia, in particular where the intraday volatility of double U-shaped pattern. It is examined through half-hourly dummy variables, quarterly-hourly dummy variables, Fourier Functional Form (FFF) based variables and spline-based variables. The non-periodic GARCH models, i.e., GARCH, EGARCH and TARCH are used for comparison of performance of best fit. The analysis show that among the four versions of PGARCH models, the half-dummy and the spline-based versions perform the best. EGARCH produced consistently superior results to other GARCH specifications. [Copyright &y& Elsevier]

Abstract: In this paper we build on the analysis of La Porta et al. [La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. W. (1998). Law and finance. Journal of Political Economy, 106(6), 1113–1155], to investigate the importance of legal families in explaining the dissimilar levels of environmental quality indicators among countries with different legal systems. The main intuition behind our analysis is that the nations in which the rights of shareholders are more protected promote real and financial investment; this increases the speed at which the per capita income corresponding to the declining branch of the Environmental Kutznets Curve (EKC) is achieved. In econometrics different regressions analyses were performed, using as dependent variables three different kinds of pollutants (CO2, fine suspended particulates and waste), and including as an explanation some financial variables never before considered in this kind of study. [Copyright &y& Elsevier]

The harmonic content of supply voltage results in additional power losses and hence increases the load power consumption. The role of the power quality equipments on the power consumption without using an accurate model cannot be determined, too. In this paper, an improved model for aggregated loads proposed, which estimates the effects of voltage harmonics on the power consumption. The distinguished aspect of the proposed model is its parameters identification method which is based on the practical techniques, such as employing a capacitor bank or varying dummy loads in steps. The proposed model has been verified by the comparison of measured and simulated results. [Copyright &y& Elsevier]

Abstract: It has been noticed that in real life situations, one finds it difficult to balance between jobs and machines, therefore most of the time we face unbalanced assignment problems. The methods suggested in the literature is based on the assumption to assign some of the jobs to dummy or fictitious machines, those jobs are executed on dummy machines which later may be ignored. The present paper suggests a modified method for solving the unbalanced assignment problems. The method is capable of assigning all the jobs to machine optimally. The method is presented in an algorithmic form and implemented on the several sets of input data to test the performance and effectiveness of the algorithm. The developed algorithm is coded into C++. A comparison is also made with the existing approach and it is recorded that our algorithm gives better results. [Copyright &y& Elsevier]

Window-opening behavior profoundly affects indoor air quality, thermal comfort, and the energy consumption of HVAC systems in office buildings. This paper presents the results of a field study on manual window control conducted in five typical individual offices in the building of a research institute in the city of Xi'an, located in the cold region of China. The average duration of window-open and window-closed states was 5.3 h/day and 6.7 h/day during working hours, respectively, and thus the window-open state accounted for 44% of the total duration during the transition season. Unlike previous studies, the results show that once a window was opened when occupants first come to the office, it would not be left like that for long, until the occupants left the office at the end of day. In addition, window opening behavior is more strongly correlated with indoor PM 2.5 concentration and indoor temperature. A new algorithm has been developed that applies dummy variables based on a binary logistic to provide a more accurate description of the relationship between the window state and influencing factors at different levels. The proposed model increased the prediction accuracy of the window being open by 5.9% compared to the continuous variable model. This study focuses on understanding the characteristics of window operation and establishing a more accurate model that offers suitable data and theoretical support for applications to simulate energy consumption in office building. • We study characteristics of the window-opening behavior in typical office building. • Window-opening state would not remain continuously when the office was occupied. • Environmental factors are analyzed. The indoor PM 2.5 concentration is the strongest correlation with the window state. • A new logistic model adopting dummy variables is developed to predict window state. • The new model increases the prediction accuracy by 5.9% over the traditional model. [ABSTRACT FROM AUTHOR]

In recent years, as the level of fine particulate matter (PM 2.5) concentration has become more closely monitored in Thailand and its harmful effects on health have been widely recognized by the public, the Thai government has debated various measures to improve air quality. In this paper, the Land Use Regression (LUR) technique was used to model the relationship between the daily PM 2.5 concentration and various predictor variables using data from the entire year of 2019. The results confirmed strong seasonal effects on PM 2.5 and substantial effects of time-variant predictors, including open biomass burning and meteorological conditions. However, time-invariant variables, including traffic, transportation, and land use characteristics were generally weaker predictors in the LUR models. The results of the model based on data for the entire year showed better statistical fit and robustness than the seasonal models. The relatively low adjusted R2 of the models developed in this study compared with previous LUR studies suggests that more detailed data, especially the traffic volume on roads nearby monitoring sites, might be necessary to improve the model's performance. Finally, the large buffer size of the open biomass burning predictor implied that the measures to reduce PM 2.5 by limiting open biomass burning would require international cooperation as some fires within the buffer area occurred in neighboring countries outside the borders of Thailand. Image 1 • Land use regression models of PM 2.5 in Bangkok were developed using data from 2019. • The annual model performed better than the separate seasonal models. • Meteorological factors and open biomass burning were critical predictors of PM 2.5. • Negative coefficient of holiday dummy variable suggests the effect of traffic-related factors. [ABSTRACT FROM AUTHOR]

To investigate the impacts of regulatory announcements on China's pilot carbon markets, as well as test the semi-strong form of market efficiency, a multivariate regression model with GARCH effects combined with multiple bilaterally modified dummy variables is established which allows us to investigate the potential overlapping influence from two timely adjacent events. The Shanghai, Guangdong and Hubei pilot carbon markets are taken as case studies. Based on the ADF test and the runs test, the results show that the China's pilot carbon markets have achieved the weak form of efficiency but not the semi-strong form of efficiency. With the regression model the results show that the release of the allowance allocation plan will affect the carbon price trend, and the allowance auction can only play an auxiliary role as its impact on the carbon price is not decisive. In addition, ubiquitous ex-ante impact proves that the information leakage exists in each pilot carbon market. The model established in this paper is suitable for analyzing the impacts of overlapping events in different markets, which can effectively assist decision makers to distinguish the effects caused by overlapping events. [ABSTRACT FROM AUTHOR]

Many studies have evaluated the social-ecological impacts of China's Sloping Land Conversion Program (SLCP). And reviews of the literature have appeared with most of them summarizing the reported findings. The primary goal of this paper is to assess the strengths and weaknesses of the socioeconomic studies in terms of the quality of data used, the appropriateness of approaches, and the adequacy of variable selection, based on a collection of 33 peer-reviewed articles. It is found that about half of them use cross-sectional or repeated cross-sectional data of household surveys, which cannot fully capture the spatiotemporal variations in farmers' labor and land allocations. Moreover, a dummy variable is inadequate to distinguish the impacts of participation with different amounts of land enrolled. Aggregate covariates are rarely included to reflect the external change and regional heterogeneity. In addition to the often-used difference in differences and propensity score matching models, other evaluation methods like regression discontinuity and synthetic control can be adopted. While endogeneity is a common concern with program participation, few studies have confronted it directly. We hope that our review will contribute to setting up the counterfactuals and controlling for confounding factors more adequately, in future studies in and outside of China. [ABSTRACT FROM AUTHOR]

Applying the data envelopment approach, this paper computes efficiency change (EFFCH) in terms of its components: pure technical and scale efficiency changes. The least squares dummy variable corrected (LSDVC) method was employed to examine the determinants of efficiency change of bioenergy industry of the EU28 countries covering the period 1990–2013. Results show that the bioenergy industry recorded an average annual rate of progress in efficiency change of 3.8% during the period studied. Interestingly, the region recorded a technical progress with both pure technical efficiency change and scale efficiency change increasing over the study period, however, scale efficiency was the major contributor to efficiency with annual increase of 4.5%. We also found variations in regional performance with EU developing region experiencing a slightly higher increase in efficiency at 3.86% while this figure is at 3.76% for the EU developed countries. Empirical finding show that economic growth, gross fixed capital formation, inflation and size of biomass input affected EFFCH of bioenergy industry of the EU28 region significantly during the period studied. Policy implications of these results are that the bioenergy industry should relentlessly pursue technical progress and aim to improve both technical and scale efficiencies simultaneously through optimal allocation of resources. • Efficiency progress is mostly attributed to scale efficiency change. • Efficiency change of bioenergy industry is highly responsive to investment. • Efficiency is an important determinant of NREAPs 2020 achievement. [ABSTRACT FROM AUTHOR]