ARTICLE Download: 16| View: 41| Comments: 0 | doi:10.20944/preprints202001.0162.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: cardiovascular disease (CVD); Disability-Adjusted Life Years (DALYs); cost of admission; risk factors
Online: 16 January 2020 (09:05:32 CET)
Cardiovascular disease (CVD) is considered to be one of the leading health issues in Thailand. CVD not only contributes to an increase in the number of hospital admissions year on year but also impacts on the rising health care expenditure for the treatment and long-term care of CVD patients. Therefore, this study is aimed at examining the impacts of risk reduction strategies on the number of CVD hospital admissions, Disability-Adjusted Life Years (DALYs) and the costs of hospitalisation. To estimate such impacts a CVD cost-offset model wasapplied using a Microsoft Excel spreadsheet. The number of the mid-year population was classified by age, gender and the CVD risk factor profiles from the recent Thai National Health Examination Survey (NHES) IV. This survey was chosen as the baseline population. The CVD risk factor profiles included age, gender, systolic blood pressure, total cholesterol, and smoking status. The Asia-Pacific Collaborative Cohort Study (APCCS) equation was applied to predict the probability of developing CVD over the next eight-year period. Estimates on the following were obtained from the model: (1.) the CVD events both fatal and non-fatal; (2.) the difference between the projected number of deaths and the actual number of deaths in that population; (3.) the number of patients who are expected to live with CVD; (4.) the DALYs from the estimated number of fatal and non-fatal events; (5.) the cost of hospital admissions. Four CVD risk strategy scenarios were investigated as follows: (1.) the do nothing scenario; (2.) the optimistic scenario; (3.) achieve the UN millennium development goal; and (4.) the worst-case scenario. The findings showed that over the next eight years there are likely to be 3,297,428 recorded cases of CVD; 5,870,049 cases of DALYs; and, approximately ฿57,000 million, ($1.9 billion), is projected as the total cost of hospital admissions. However, if the current health policy can reduce the levels of risk factors as defined in the optimistic scenario or such policy meets the specifications of the UN millennium development goal,there would be a significant reduction in the number of hospital admissions. These are estimated to be a reduction of 522,179 male and 515,416 female cases. With these results it is expected that health care costs would save approximately ฿9,000 million, ($298.3 million), for CVD and 900,000 million of DALYs over the next eight years. However, if there is an upward trend in the risk factors as predicted in the worst-case scenario, then there will be an increase of 428,220 CVD cases; consequently, DALYs cases may rise by 766,029 while the hospitalisation costs may increase by approximately ฿7,000 million, ($232.1 million). Based on our findings, reducing the levels of CVD risk factors in the population will drastically reduce: (1.) the number of CVD cases; (2.) DALYs cases; and (3.) health care costs. Therefore it is recommended that the health policy should enhance the primary prevention programs which would be targeted at reducing the CVD risk factors in the population.
Wed, 4 December 2019
ARTICLE Download: 22| View: 18| Comments: 0
Subject: Social Sciences, Econometrics & Statistics Keywords: environmental protection enterprise; input-output efficiency; market driven; policy driven
Online: 4 December 2019 (04:52:50 CET)
The environmental protection industry provides important support for achieving sustainable economic development. In China, because the actual operation of environmental protection enterprises is driven by multiple factors, the input-output efficiency displays relatively complex distribution characteristics and evolutionary trends. In view of the differences in the definitions of efficiency and the limitations of efficiency factors in existing research, we use no balanced panel data from the environmental protection listed companies in China from 2014-2018 as the research sample, and an analysis is performed based on the stochastic frontier analysis (SFA) framework. A comprehensive investigation of the various factors that influence efficiency yielded the following empirical conclusions. (1) Efficiency improvements in Chinese environmental protection enterprises display significant “market-driven” and “policy-driven” characteristics, and there is negative feedback related to “funding”. (2) The environmental protection industry in China has strong “blue ocean” market characteristics, and the enterprise efficiency needs to be more dependent on decreased scale expansion and operations. Management improvements, not product innovation, require increased vigilance to decrease expansion. Therefore, to improve the efficiencies of Chinese environmental protection enterprises, focus should be placed on market changes and the creation of a differentiated policy support system. Additionally, it is necessary to monitor the blind expansion of microenvironmental enterprises and the risk of leveraging.
Mon, 25 November 2019
COMMUNICATION Download: 54| View: 82| Comments: 0 | doi:10.20944/preprints201911.0302.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Environmental Kuznets Curve; polynomial specification; elasticity formula; quadratic function; percentage change
Online: 25 November 2019 (02:57:12 CET)
The polynomial functional specification is widely used in environmental and ecological economics. When different powers of the same variable enter to the specification the elasticity of explained variable with respect to that variable should be obtained using the elasticity formula. Since, in this case the elasticity itself is a function, one need to calculate it at a certain point, to have a general idea about the response of explained variable to the change in explanatory variable. The mainly used point is mean of the variables entering to the elasticity formula. Sometimes, the minimum and maximum points also used for comparison purposes. One should careful in interpreting the response of explanatory variable to the change in the variable entering to the specification with its different powers. This study revisits some methodological points regarding the calculation and interpretation of responses in the above-mentioned cases.
Sun, 10 November 2019
ARTICLE Download: 69| View: 82| Comments: 0 | doi:10.20944/preprints201911.0119.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: second-hand cars; p-values; confidence interval; non-parametric bootstrapping; correlation
Online: 10 November 2019 (16:46:47 CET)
In developed countries, especially the big-sized ones like Australia and the USA, a car is almost an inevitable necessity to carry out daily activities. Due to this, used cars have become a great alternative to brand new cars because of their cost effectiveness. In this work, estimation of prices of used cars based on numerous factors is studied statistically. Data is based on prices of used cars sold across Australia. Statistical methods like correlation and permutation tests using linear regression model, exact tests and non-parametric bootstrapping is implemented to study the relationship of price with mileage and year of manufacture of the car using p-values and null hypothesis. Predictions are also made on the price by calculating a 95% confidence interval (CI) of median prices in small portions of the dataset. The study presents potential ideas for understanding correlation between variables and parameters in business studies.
Mon, 28 October 2019
ARTICLE Download: 51| View: 84| Comments: 0 | doi:10.20944/preprints201910.0320.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Nepal; Vietnam; Bangladesh; gridded population sampling; GridSample; OpenStreetMap; GeoODK; cross-sectional design; urban; household survey
Online: 28 October 2019 (11:48:02 CET)
Background: The methods used in low- and middle-income countries (LMICs) household surveys have not changed in four decades; however, LMIC societies have changed substantially. This mismatch may result in unintentional exclusion of vulnerable and mobile urban populations. We compare three survey method innovations with standard survey methods in Kathmandu, Dhaka, and Hanoi, and summarize feasibility of our innovative methods in terms of time, cost, skill requirements, and experiences. Methods: We used descriptive statistics and regression techniques to compare respondent characteristics in samples drawn with innovative versus standard survey designs and household definitions, adjusting for sample probability weights and clustering. Feasibility of innovative methods was evaluated using a thematic framework analysis of focus group discussions with survey field staff, and via survey planner budgets. Results: We found that a common household definition excluded single adult (46.9%) and migrant headed households (6.7%), as well as non-married (8.5%), unemployed (10.5%), disabled (9.3%), and studying (14.3%) adults. Further, standard two-stage sampling resulted in fewer single adult and non-family households than an innovative one-stage design; however, two-stage sampling resulted in more tent and shack dwellers. Our survey innovations provided good value for money and field staff experiences were neutral or positive. Staff recommended streamlining field tools and pairing technical and survey content experts during fieldwork. Conclusions: This evidence of unintentional exclusion of vulnerable and mobile urban populations in LMIC household surveys is deeply concerning, and underscores the need to modernize survey methods and practices.
Fri, 4 October 2019
ARTICLE Download: 60| View: 142| Comments: 0 | doi:10.20944/preprints201910.0049.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Adaptation; Indigenous knowledge; CD production; paddy; hilly Nepal
Online: 4 October 2019 (11:44:11 CEST)
Climate change is a buzzword in the world. Scientist has approved it as global warming with its projection of undesired and unpredicted frequent extreme events and their vulnerabilities not only at present but also at future. There is an assumption of occurrence of adaptive capacity and behavior of farmers in agriculture production activity at some extent to neutralize climate change vulnerabilities of flood and landslides on paddy production. This paper empirically examines the effects of climate change in paddy production and farmer’s adaptive behaviors to neutralize such climatic shocks and events in paddy production by employing CD production function based econometric model. The study employed primary data collected through 642 household surveys. The study finds that climatic shocks and events have huge loss (60%) in paddy production and revenue income in such plot where farmers have not indigenous knowledge and practices. But both small and larger farmers who have adaptive capacity and behavior with their indigenous knowledge have less loss in paddy production and revenue income, although they have heterogeneity in their socio economic characteristics (income, asset holding, literacy, experience, land holding and age). The farmers who have used adaptive behavior have indigenous knowledge and experiences including bamboo wall construction to control flood and landslides and seed change to resist climatic shocks and events. In hilly region, the farmers have not sufficient alternative measures, except both adaptive measures because of their poverty, illiteracy and remote locations. The study finds their higher effective level to minimize vulnerabilities to paddy production and revenue per farm plot, although these adaptive behaviors are cost effective and local entity. Comparatively, bamboo wall construction is more effective measure in the paddy production than others are (seed switch) to minimize the flooding materials from the flood and the landslides. Thus, low cost indigenous adaption behavior of farmers is effective measure to climate change and climate change induced disasters and events vulnerability in paddy production.
Mon, 9 September 2019
ARTICLE Download: 144| View: 338| Comments: 0 | doi:10.20944/preprints201909.0102.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: ARIMA Methodology; Out-of-Sample Forecast; Tourist Arrivals; Sierra Leone
Online: 9 September 2019 (12:11:03 CEST)
This study have uniquely mad use of Box-Jenkins ARIMA models to address the core of the threes objectives set out in view of the focus to add meaningful value to knowledge exploration. The outcome of the research have testify the achievements of this through successful nine months out-of-sample forecasts produced from the program codes, with indicating best model choices from the empirical estimation. In addition, the results also provide description of risks produced from the uncertainty Fan Chart, which clearly outlined possible downside and upside risks to tourist visitations in the country. In the conclusion, it was suggested that downside risks to the low level tourist arrival can be managed through collaboration between authorities concerned with the management of tourist arrivals in the country.
Mon, 27 May 2019
ARTICLE Download: 93| View: 162| Comments: 0 | doi:10.20944/preprints201905.0311.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Mallows criterion; Model averaging; Model selection; Shrinkage; Tuning parameter choice.
Online: 27 May 2019 (10:28:22 CEST)
Model selection and model averaging have been the popular approaches in handling modelling uncertainties. Fan and Li(2006) laid out a uniﬁed frame work for variable selection via penalized likelihood. The tuning parameter selection is vital in the optimization problem for the penalized estimators in achieving consistent selection and optimal estimation. Since the OLSpost-LASSO estimator by Belloni and Chernozhukov (2013), few studies have focused on the ﬁnite sample performances of the class of OLS post-penalty estimators with the tuning parameter choice determined by diﬀerent tuning parameter selection approaches. We aim to supplement the existing model selection literature by studying such a class of OLS post-selection estimators. Inspired by the Shrinkage Averaging Estimator (SAE) by Schomaker(2012) and the Mallows Model Averaging (MMA) criterion by Hansen (2007), we further propose a Shrinkage Mallows Model Averaging (SMMA) estimator for averaging high dimensional sparse models. Based on the Monte Carlo design by Wang et al. (2009) which features an expanding sparse parameter space as the sample size increases, our Monte Carlo design further considers the eﬀect of the eﬀective sample size and the degree of model sparsity on the ﬁnite sample performances of model selection and model averaging estimators. From our data examples, we ﬁnd that the OLS post-SCAD(BIC) estimator in ﬁnite sample outperforms most of the current penalized least squares estimators as long as the number of parameters does not exceed the sample size. In addition, the SMMA performs better given sparser models. This supports the use of the SMMA estimator when averaging high dimensional sparse models.
Wed, 15 May 2019
ARTICLE Download: 74| View: 197| Comments: 0 | doi:10.20944/preprints201905.0190.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: power envelope; Neyman-Pearson tests; Skewness & Kurtosis
Online: 15 May 2019 (10:50:52 CEST)
In social & health sciences, many statistical procedures and estimation techniques rely on the underlying distributional assumption of normality of the data. Non-normality may lead to incorrect statistical inferences. This study evaluates the performance of selected normality tests on the stringency framework for the skewed alternative space. Stringency concept allows us to rank the tests uniquely. Bonett & Seier test (Tw) turns out to be the best statistics for slightly skewed alternatives and the Anderson-Darling (AD), Chen-Shapiro (CS), Shapiro-Wilk (W) and Bispo, Marques, & Pestana, (BCMR) statistics are the best choices for moderately skewed alternative distributions. Maximum loss of Jarque-Bera (JB) and its robust form (RJB), in terms of deviations from the power envelope, is greater than 50% even for large sample sizes which makes them less attractive in testing the hypothesis of normality against the moderately skewed alternatives. On balance, all selected normality tests except Tw and COIN performed exceptionally well against the highly skewed alternative space.
Fri, 10 May 2019
ARTICLE Download: 78| View: 45| Comments: 0 | doi:10.20944/preprints201905.0127.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Clean Energy Production; Nuclear Awareness; Nuclear Electrical Power; Nuclear Optimism; Nuclear Waste; Sustainable Development
Online: 10 May 2019 (14:31:53 CEST)
Relying on the United Arab Emirates (UAE) extract from the cross-national data sample on the environmental affection and cognition of adolescent students, and seemingly unrelated bivariate weighted ordered probit regression modeling, this study adopts a national perspective to investigate the determinants of adolescent students' awareness and expectations about nuclear power technology and nuclear waste in the UAE. Identification of model parameters is achieved through maximum simulated likelihood estimation. The findings show that each level increase in UAE youth's interest in ecosystem services and sustainability raises their awareness of nuclear electrical power and nuclear waste by 13.5%, while reducing by 2.4% their level of optimism towards the technology. Furthermore, we find significant heterogeneity in youth awareness and expectations about nuclear power technology across the seven Emirates. Accounting for all other factors (including interest in ecosystem services), UAE youth awareness about nuclear electrical power technology appears to not significantly influence their expectations about the evolution of this technology for the next 20 years. Given that the UAE first nuclear power plant ``Barakah'' is scheduled to start operations end of 2019 beginning 2020, and the typical long life-span of nuclear wastes, our results provide important insights for developing sustainable nuclear energy policies and establishing a long-term nuclear energy program in the UAE.
ARTICLE Download: 150| View: 215| Comments: 0 | doi:10.20944/preprints201905.0122.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: dichotomous response; predictive model; tree boosting; GLM; machine learning
Online: 10 May 2019 (11:28:11 CEST)
XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims vs. no claims can be used to identify the determinants of traffic accidents. We compare the relative performances of logistic regression and XGBoost approaches for predicting the existence of accident claims using telematics data. The dataset contains information from an insurance company about individuals’ driving patterns – including total annual distance driven and percentage of total distance driven in urban areas. Our findings show that logistic regression is a suitable model given its interpretability and good predictive capacity. XGBoost requires numerous model-tuning procedures to match the predictive performance of the logistic regression model and greater effort as regards interpretation.
Thu, 4 April 2019
ARTICLE Download: 118| View: 218| Comments: 0 | doi:10.20944/preprints201904.0058.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: load forecast; short term; probabilistic; Gaussian processes
Online: 4 April 2019 (16:01:54 CEST)
We provide a comprehensive framework for forecasting five minute load using Gaussian processes with a positive definite kernel specifically designed for load forecasts. Gaussian processes are probabilistic, enabling us to draw samples from a posterior distribution and provide rigorous uncertainty estimates to complement the point forecast, an important benefit for forecast consumers. As part of the modeling process, we discuss various methods for dimension reduction and explore their use in effectively incorporating weather data to the load forecast. We provide guidance for every step of the modeling process, from model construction through optimization and model combination. We provide results on data from the PJMISO for various periods in 2018. The process is transparent, mathematically motivated, and reproducible. The resulting model provides a probability density of five-minute forecasts for 24 hours.
Wed, 27 February 2019
ARTICLE Download: 120| View: 218| Comments: 0 | doi:10.20944/preprints201902.0259.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Green pesticides; Agricultural subsidies; Product certification; Laboratory experiment
Online: 27 February 2019 (15:08:30 CET)
This paper studies the impact of agricultural subsidies and product certification on the use rate of green pesticides based on experimental economics. We found that agricultural subsidies effectively increased the utilization rate of green pesticides. If the agricultural subsidies raised from 20% to 100%, the green pesticides’ using rate increased by 438.51%. We also found that product certification increased the utilization rate of green pesticides by 376.16%%.The increase of agricultural subsidies is more effective than the product certification. Under a higher proportion of agricultural subsidies, farmers’ behavior will maintain “status bias”. Therefore, there are three suggestions proposed. Firstly, because of high price of green pesticides and lower production, the subsidies for agricultural materials should raise greatly to effectively improve the utilization rate of green pesticides. It is recommended that green pesticide provided free of charge in some wealthy areas. Secondly, both subsidies and product certification can improve the use rate of green pesticides. However, since the effect of agricultural subsidies is better than product certification, and farmers may have status bias. Therefore, it is recommended to give priority to the substantial increase on the proportion of agricultural subsidies, and then to product certification.
Wed, 30 January 2019
ARTICLE Download: 93| View: 380| Comments: 0 | doi:10.20944/preprints201901.0304.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: climate change; econometric analysis; insurance; resilience; risk; tropical cyclones
Online: 30 January 2019 (07:10:54 CET)
Having sustained, over the course of more than two decades, record-breaking natural catastrophe losses, American insurers and reinsurers are justifiably questioning the potential linkage between anthropogenic climate change and extreme weather. Here, we explore issues pertaining to this linkage, looking at both the likely short-term implications for the insurance industry, as well as potential longer-term impacts on financial performance and corporate resilience. We begin our discussion with an overview of the implications that climate change is likely to have on the industry, especially as it relates to how catastrophic risks are construed, assessed, and managed. We then present the rudiments of an econometric analysis that explores the financial resilience of the property/casualty (P/C) industry in the face of both natural and man-made catastrophes. In this analysis, we explore the profitability consequences of several illustrative scenarios involving large-scale losses from extreme weather—specifically, a sequence of storms like those striking the U.S. in 2004—and a scenario that explores the prospect of a Katrina-scale storm in combination with a mass terror attack on the scale of 9/11. At systemic levels of aggregation, our analysis suggests a high degree of macro-resilience for the insurance industry. Moreover, we find that insurer resilience is higher for larger impacts, considering both the speed of recovery, as well as the inverse of the area under the unaffected system profile. We conclude with a summary of our findings and a closing commentary that explores the potential implications of these results for P/C insurers moving forward.
Fri, 16 November 2018
ARTICLE Download: 175| View: 87| Comments: 0 | doi:10.20944/preprints201811.0387.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: climate change; soybean yields; technology; temperature; CO2
Online: 16 November 2018 (07:45:34 CET)
Soybean yields are often indicated as an interesting case of climate change mitigation due to the beneficial effects of CO2 fertilization. In this paper we econometrically study this effect using a time series model of yields in a multivariate framework for a main producer and exporter of this commodity, Argentina. We have to deal with the upward behavior of soybean yields trying to identify which variables are the long-run determinants responsible of its observed trend. With this aim we adopt a partial system approach to estimate subsets of long-run relationships due to climate, technological and economic factors. Using an automatic selection algorithm we evaluate encompassing of the different obtained equilibrium correction models. We found that only technological innovations due to new crop practices and the use of modified seeds explain soybean yield in the long run. Regarding short run determinants we found positive effects associated with the use of standard fertilizers and also from changes in atmospheric CO2 concentration which would suggest a mitigation effect from global warming. However, we also found negative climate effects from periods of droughts associated with La Niña episodes, high temperatures and extreme rainfall events during the growing season of the plant.
Wed, 14 November 2018
ARTICLE Download: 71| View: 57| Comments: 0 | doi:10.20944/preprints201811.0329.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: gross domestic product; Leontief dynamic model; investments in production capital; Kalman filter
Online: 14 November 2018 (09:52:29 CET)
This paper based on systems - theoretic approach to the definition of a country's GDP as not directly observable characteristic of system state. Leontief dynamic model is generalized to take into account the stimulating effect of consumption on GDP growth. In consumption, apart from final consumption, terms are considered: balance of foreign trade, fictitious investments and hidden costs. The Kalman filter uses Rosstat's gross output (for system output) and final consumption (for system control) data from 1995 to 2015. It is concluded that if in the years 2014, 2015 it was possible to increase consumption by 5% by, say, price cuts or some increase in money supply, then GDP would be greater by about 2.5%. GDP real values in recent years are most likely greater than official values. Fictitious investments and hidden costs are found in the amount of up to third the value of final consumption. The accuracy of one-year forecasts of true GDP by the methodology of this article is approximately 1.5%.
Tue, 13 November 2018
ARTICLE Download: 66| View: 77| Comments: 0 | doi:10.20944/preprints201811.0319.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Environmental regulation, Industrial structure upgrade, Economic fluctuation, Dynamic panel threshold
Online: 13 November 2018 (15:11:13 CET)
This paper utilizes dynamic panel threshold technology to conduct a nonlinear test on the direct effect between environmental regulation and economic fluctuations and the effect of industrial structure upgrading by taking 35 industrial sectors in China from 2003 to 2016 . The research has found that there is an inverted "U" relationship between environmental regulation and economic fluctuations, with the enhancement of environmental regulations, the economic fluctuation increases first and then decreases. The cross-terms of environmental regulation and industrial structure rationalization or industrial structure upgrading are significantly negative, which indicates that the enhancement of environmental regulation is conducive to promoting industrial structure upgrading and reducing the economic fluctuations. While the rationalization factors of industrial structure and advanced industrial structure are significantly negative, indicating that both forms of industrial structure upgrading are conducive to reducing the economic fluctuations. Environmental regulation has technical innovation thresholds for industrial structure upgrading and economic fluctuations, but there are no human capital or FDI thresholds. In the rationalization model of industrial structure, there is a nonlinear "U" relationship between environmental regulation and economic fluctuations when the proportion of scientific research expenditure is more than 1.35%. With the enhancement of environmental regulation, the economic fluctuation reduces first and then increases, and the corresponding inflection point value is 2.398% of the environmental regulation level. At the same time, the environmental regulation can indirectly reduce economic fluctuations by pushing down the industrial structure upgrades. In the advanced model of industrial structure, there is a “U” relationship between environmental regulation and economic fluctuation when the proportion of scientific research expenditure is greater than 1.26%. With continuous enhancement of environmental regulation, the economic fluctuation reduces first and then increases. The corresponding inflection point value is 1.78% of the environmental regulation level, and environmental regulation can indirectly reduce economic fluctuations by promoting the industrial structure at the same time.
Mon, 5 November 2018
ARTICLE Download: 83| View: 91| Comments: 0 | doi:10.20944/preprints201811.0125.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Retailers’ Optimal Pricing Strategy; Expected Utility Theory (EUT); Regret Theory; Regret Reference Point; Price-dependent Demand
Online: 5 November 2018 (15:46:20 CET)
Based on the Expected Utility Theory and Regret Theory, the Extended Regret Theory (ERT) is proposed in this paper to study the optimal pricing strategy of retailers in e-commerce environment. Taking the diversity of sales channels and the uncertainty of consumers in e-commerce environment into consideration, author of the paper designs an extended regret utility function which comprehensively considers both pessimistic and optimistic attitudes of decision makers in retailing industry to describe their regret-avoidance behavior. According to the sensitivity analysis, it is found that the optimal retail price decreases as the consumer price sensitivity coefficient increases, yet does not show variation with changes of the consumers pessimism degree. Moreover, the optimal retail price(s) obtained under EUT, ERT and combination of EUT and ERT represent the same.
Thu, 27 September 2018
REVIEW Download: 125| View: 82| Comments: 0 | doi:10.20944/preprints201809.0523.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: credit rating transitions; mixtured estimating equations; multiplicative intensity model; structural break
Online: 27 September 2018 (03:03:41 CEST)
Various sudden shifts in financial market conditions over the past decades have demonstrated the significant impact of market structural breaks on firms' credit behavior. To characterize such effect quantitatively, we develop a continuous-time modulated Markov model for firms' credit rating transitions with the possibility of market structural breaks. The model takes a semi-parametric multiplicative regression form, in which the effects of firms' observable covariates and macroeconomic variables are represented parametrically and nonparametrically, respectively, and the frailty effects of unobserved firm-specific and market-wide variables are incorporated via the integration form of the model assumption. We further develop a mixtured-estimating-equation approach to make inference on the effect of market variations, baseline intensities of all firms' credit rating transitions, and rating transition intensities for each individual firm. We then use the developed model and inference procedure to analyze the monthly credit rating of U.S. firms from January 1986 to December 2012, and study the effect of market structural breaks on firms' credit rating transitions.
Mon, 23 July 2018
ARTICLE Download: 349| View: 151| Comments: 0 | doi:10.20944/preprints201807.0412.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: P.C. regression; AIC criterion; logit function; pearson's Chi-square use
Online: 23 July 2018 (10:58:36 CEST)
In this paper, we use the Principal Components Logistic Regression as a technique to reduce the variables being used in Credit Scoring Modeling. Specifically, we construct two models in which greek enterprises are classified, through their credit behavior and we evaluate them, relying on real data. In general, we propose a general way to use PC Regression, in case that we have high correlations and categorical variables in the sample.
Wed, 18 July 2018
ARTICLE Download: 193| View: 180| Comments: 0 | doi:10.20944/preprints201807.0318.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: difference kernel estimator; integrated difference kernel estimator; M-estimation; Monte Carlo; nonparametric threshold regression
Online: 18 July 2018 (08:24:47 CEST)
This paper compares the finite sample performance of three non-parametric threshold estimators via Monte Carlo method. Our results show that the finite sample performance of the three estimators is not robust to the relative position of the threshold level along the distribution of threshold variable, especially when a structural change occurs at the tail part of the distribution.
Mon, 9 July 2018
ARTICLE Download: 237| View: 208| Comments: 0 | doi:10.20944/preprints201807.0132.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Cointegrated VAR methodology, Linking theory and evidence, Empirically based macroeconomics.
Online: 9 July 2018 (11:34:19 CEST)
This survey paper discusses the Cointegrated VAR methodology and how it has evolved over the last 30 years. The first section is a description of major steps in the econometric development of the CVAR model that facilitated serious real world applications. The next three sections are primarily methodological and discuss (i) difficulties and puzzles when confronting theory with the data, (ii) the formulation of a viable link between theory and the data, a so called theory-consistent CVAR scenario, and (iii) how all this was inspired by Trygve Haavelmo and his Nobel prize winning monograph "The Probability Approach to Economics". The next two sections discuss early applications of the Cointegrated VAR model to monetary transmission mechanisms, international transmission mechanisms and wage, price and unemployment dynamics. They report puzzling evidence, discuss the need for new theory, and propose a method for combining partial CVAR analyses into a larger macroeconomic model. The following sections propose a new, empirically-based, approach to macroeconomics in which imperfect knowledge based expectations replace so called rational expectations and in which the financial sector plays a key role for understanding the long persistent movements in the data. The last section argues that the CVAR can act as a "design of experiment for passive observations" and illustrates with several applications including unemployment dynamics under crises periods and aid effectiveness in South Saharan African countries.
Fri, 8 June 2018
ARTICLE Download: 222| View: 320| Comments: 0 | doi:10.20944/preprints201806.0144.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Contingent valuation method, economic value of water, farmer-managed irrigation system
Online: 8 June 2018 (16:24:42 CEST)
Declining water supply is the main cause of rising water fee for agricultural use. Moreover, in non-technical irrigation, poor irrigation infrastructure exacerbates water scarcity. Thus, the purpose of this study is to identify farmer willingness to pay for non-technical irrigation and its determinants. Structured questionnaire was used to collect data from 100 farmers. Contingent valuation method was employed to elicit farmer WTP and multiple linear regression was used to find its determinants. The result shows that farmer average WTP is Rp 3,055,168 /ha/year. It accounts for 20 percent of total farmer revenue and almost 20 times fee for technical irrigation. Economic and technical variables are the significant determinants of WTP while social variables seem insignificant to WTP. This result indicates high economic value of water, and to improve irrigation management we recommend establishing irrigation infrastructure gradually by mobilizing farmer resources (capital and management) and strengthening WUA.
Mon, 4 June 2018
ARTICLE Download: 285| View: 581| Comments: 0 | doi:10.20944/preprints201806.0044.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: air pollution; environmental awareness; environmental education; green gas emission; sustainable development; water shortage
Online: 4 June 2018 (13:01:55 CEST)
This study inscribes itself in the global discussion on the nurturing of pro-environmental behaviors among young people for a sustainable future. Here we focus on students' interest in Ecosystem Services and Sustainability to explain their awareness and optimism about the environmental issues of air pollution, water shortage and green gas emission in 50 countries around the world. To this end, we use the cross-sectional survey data of the OECD's Program for International Student Assessment (PISA) 2015, along with seemingly unrelated bi-variate weighted ordered Probit modeling with country specific effects. The results show that in addition to factors such as age, gender, immigration status, and economic, social and cultural status, interest in the biosphere is a significant determinant of students' environmental awareness and optimism. In fact, a one level increase in students' interest in ecosystem services and sustainability raises on average their awareness level by 15.3% for the issue of air pollution, 15.7% for the issue of water shortage, and 24.6% for the issue of green gas emission. Although students' interest in the biosphere seems to not have a significant effect on their expectations about the issue of green gas emission, it does however raise their level of optimism by 0.8% for the issue of air pollution, and 0.2% for the issue of water shortage. Furthermore, every one level increase in students' environmental awareness leads to 17.3% more optimism about the issue of air pollution, 15.8% more optimism about the issue of water shortage, and 17.4% more optimism about the issue of green gas emission. Therefore, relying on the Theory of Planned Behavior (TPB), our results imply that governments and policy makers can successfully leverage young people interests in the biosphere to effectively achieve their goals for sustainability.
Fri, 1 June 2018
ARTICLE Download: 225| View: 244| Comments: 0 | doi:10.20944/preprints201806.0013.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: sustainability; trust; distress; transport services; road freight transport; modal shift potential; shift paradigm; modelling; prediction; General Discriminant Analysis
Online: 1 June 2018 (10:44:39 CEST)
Confidence in intermodal transport has not yet been defined. There are many different approaches to the concept of trust. However, the authors embedded them in the light of the challenges of sustainability, linking with the shift paradigm. The objective of the article is to indicate the directions and criteria for the implementation of the shift paradigm, inscribed in the idea of sustainable transport. The auxiliary objective is to predict which countries in a given year will have the TRUST status, i.e. implement the shift paradigm, and which will not implement it (DISTRESS). The article uses taxonometric techniques and built a model using General Discriminant Analysis. On their basis, the utility function was approximated, including the directions of implementation of the shift paradigm depending on the scale of the environmental load of transport. In the course of the research, an original and innovative econometric model was constructed, pointing to three variables, which had the greatest impact on trust. Thanks to the cognitive value of the model, it is possible to identify individuals who deserve the trust, i.e. it will implement the shift paradigm, with 93% probability. In the future, it is worth expanding the research by models for each country.
Wed, 16 May 2018
ARTICLE Download: 297| View: 299| Comments: 0 | doi:10.20944/preprints201805.0230.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: industrial agglomeration; FDI; green total factor productivity; spillover effect
Online: 16 May 2018 (10:54:56 CEST)
This paper studies the influence mechanism of industrial agglomeration and foreign direct investment (FDI) on green total factor productivity (GTFP). We use the SBM Directional Distance Function to measure the GTFP of Chongqing's manufacturing industry from 1999 to 2015. The results show that the level of GTFP in Chongqing's manufacturing industry is relatively low, which is contrary to the current green development mode. By clarifying the conduction path of industrial agglomeration and FDI on GTFP, we use the panel Tobit model to study the effect of industrial agglomeration and FDI on GTFP. The main findings are: the higher the level of industrial agglomeration, the more beneficial it is to increase GTFP. FDI has an inhibitory effect on GTFP. The spillover effect of FDI on GTFP is not significant. At the same time, FDI counteracts the role of industrial agglomeration in promoting GTFP. The findings in a present study indicate that, according to Chongqing's experience, the "pollution haven" is established. Therefore, relying solely on foreign technology to promote the development of the manufacturing industry has many drawbacks clearly. Only by improving the ability of independent innovation is the reliable way to enhance GTFP effectively.
Wed, 2 May 2018
ARTICLE Download: 245| View: 207| Comments: 0 | doi:10.20944/preprints201805.0017.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Bayesian approach; conjugate prior; cartel; leniency program; policy simulation
Online: 2 May 2018 (08:37:32 CEST)
Cartels cause tremendous damage to the market economy and disadvantage consumers by causing higher prices and lower quality; moreover, they are difficult to detect. We need to prevent them by scientific analysis, which includes the determination of an indicator to explain antitrust enforcement. Particularly, the probability of cartel penalization is a useful indicator for the evaluation of the competition enforcement. This study is to estimate the probability of cartel penalization by using a Bayesian approach. In the empirical study, the probability of cartel penalization is estimated by Bayesian approach from cartel data of Department of Justice in United States from 1970 to 2009. The probability of cartel penalization is seen to be sensitive to change of competition law and the results shows the usefulness of higher interpretation than other research. The result of the policy simulation shows how effective the leniency program is. From this estimation, antitrust enforcement is evaluated, and thereby, can be improved.
Thu, 12 April 2018
ARTICLE Download: 256| View: 288| Comments: 0 | doi:10.20944/preprints201804.0159.v1
Online: 12 April 2018 (06:11:34 CEST)
This paper examines the regional changes of corn production and the relationship between ethanol production and corn production. The underlying hypothesis is that the rapid growth in ethanol production causes regional expansion of corn production outside the traditional regions. This paper introduces the information approach developed by entropy theory to describe these regional changes. The results support the hypothesis that ethanol production leads to expansion of corn production outside traditional corn producing regions.
Mon, 26 March 2018
ARTICLE Download: 266| View: 224| Comments: 0 | doi:10.20944/preprints201803.0210.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: causality-in-variance; cross-correlation function; housing and stock markets
Online: 26 March 2018 (08:48:02 CEST)
This paper employs the two-step procedure developed by Cheung and Ng (1996) to analyze the causality-in-mean and causality-in-variance between the housing and stock markets of the UK. The empirical findings make two key contributions. First, although previous studies have indicated a one-way causal relation from the housing market to the stock market in the UK, this paper discovered a two-way causal relation between them. Second, a causality-in-variance as well as a causality-in-mean was detected from the housing market to the stock market.
Mon, 19 February 2018
ARTICLE Download: 312| View: 323| Comments: 0 | doi:10.20944/preprints201802.0121.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Formal Education, Labor Market Participation, Literacy, Poverty, Sustainable Development, Semipametric Trivariate Probit
Online: 19 February 2018 (16:00:35 CET)
This research contributes to the overall debate on education for sustainable development (ESD) by shed- ding lights on the contributing role of formal education to the contemporaneous dynamics of literacy, labor market participation and poverty reduction in Africa, with a focus on Burkina Faso. The study uses a semi-parametric recursive trivariate probit modeling approach, and data from the 2014 National Survey on Household Living Conditions in Burkina Faso. The results show that the embraced systemic approach in this analysis is statistically signicant as shown by the 95% condence intervals on the three correlation coeffcients in the model. Furthermore, education does improve literacy skills, however improved literacy skills in itself does not guaranty active labor market participation in Burkina Faso. Active labor market participation seem to be affected by labor market rates of return, and individual reservation wage (or income). When labor market rate of return is short of high literacy skilled individuals' reservation wage, then the natural response is a choice of inactivity in the labor market, by the later group. Simultaneously however, it is found that active labor market participation leads to poverty reduction; therefore, in addition to new industrial policies for structural transformation of the economy, policy makers in Burkina Faso should consider education and minimum wage reforms to give highly literate household members the incentive to be active in the labor market.
Tue, 13 February 2018
ARTICLE Download: 386| View: 647| Comments: 0 | doi:10.20944/preprints201802.0093.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: consumer behavior; cooking fuel; environmental consciousness; health consciousness; semi-parametric estimation; trivariate probit; water and sanitation; wealth
Online: 13 February 2018 (08:53:03 CET)
Relying on Random Utility Theory (RUT) as the guiding mechanism for the Data Generating Process (DGP), this paper uses households consumption choices on cooking fuel, drinking water, and sanitation from the 2014 United States Agency for International Development's (USAID) Demographic and Health Survey (DHS) data on Burkina Faso, to characterize and investigate the inter-linkages between health consciousness and environmental consciousness, and their relationship with wealth in a low income country context. We achieve this by specifying sequentially three econometric modeling frameworks: the first one being independent binary probit (IBP) models to describe each choice process, followed by a fully parametric trivariate probit (FPTP) model to account for choice dependency, and finally by a semi-parametric trivariate probit (SPTP) model to further relax the linearity assumption. Based on the Akaike Information criteria (AIC) and the estimated Trivariate model correlation coefficients, the SPTP framework is found to be the best specification for describing the observed consumption behaviors. The results show that increased wealth level raises households health and environmental consciousness, while leaving the relative preference ordering over the elements in the household consumption basket unchanged.
Wed, 10 January 2018
ARTICLE Download: 655| View: 416| Comments: 0 | doi:10.20944/preprints201801.0090.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: clustering; curve fitting; nonparametric regression; smoothing data; polynomial approximation
Online: 10 January 2018 (09:48:23 CET)
Nonlinear nonparametric statistics (NNS) algorithm offers new tools for curve fitting. A relationship between k-means clustering and NNS regression points is explored with graphics showing a perfect fit in the limit. The goal of this paper is to demonstrate NNS as a form of unsupervised learning, and supply a proof of its limit condition. The procedural similarity NNS shares with vector quantization is also documented, along with identical outputs for NNS and a k nearest neighbours classification algorithm under a specific NNS setting. Fisher's iris data and artificial data are used. Even though a perfect fit should obviously be reserved for instances of high signal to noise ratios, NNS permits greater flexibility by offering a large spectrum of possible fits from linear to perfect.
Wed, 29 November 2017
ARTICLE Download: 472| View: 394| Comments: 0 | doi:10.20944/preprints201711.0191.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Pesticides, Vegetable, Nepal, Determinant, Multivariate Probit
Online: 29 November 2017 (13:27:57 CET)
Currently, the pesticides are the global core concern because it is a boon to farmers against increasing disease-pest and simultaneously, pesticide residue is the major anxiety regarding human health. For that reason, identification and determination of factors affecting the application of pesticides are essential. To identify and evaluate determinants of pesticides application in Nepal, a household survey of 300 households was carried-out and an empirical analysis was done using multivariate probit model. Moreover, powder and liquid forms of pesticides were considered for summer and winter season in vegetable farming, which was assigned as outcome variables. Likewise, socio-economic, demographic, farm-level and perception data were considered as explanatory variables. Use of chemical fertilizers, age and gender of head of household, household size and access to weather information were found the most influencing factors. Moreover, forms of pesticides and growing seasons were found complementary to each other. Therefore, devising the policy options accordingly should balance needs of farmers and health of consumers.
Wed, 15 November 2017
ARTICLE Download: 346| View: 312| Comments: 0 | doi:10.20944/preprints201711.0098.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Paris 2015 Agreement; CO2 emissions; VAR models; Granger causality; impulse response functions; forecast error variance decomposition; software: R; MTS; RATS
Online: 15 November 2017 (18:34:09 CET)
In this paper a dynamic relationship between the CO2 emissions in Finland, Norway and Sweden is presented. With the help of a VAR(2) model, and using the Granger terminology, it is shown that the emissions in Finland are affecting those in Norway and Sweden. Other aspects of this dynamic relationship are presented as well.
Mon, 16 October 2017
ARTICLE Download: 1470| View: 431| Comments: 0 | doi:10.20944/preprints201710.0107.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Bus; Customer satisfaction; Intercity terminal; SERVQUAL Model
Online: 16 October 2017 (12:58:16 CEST)
The aim of this study was to evaluate the nature of relationship between service quality and customers’ satisfaction thorough SERVQUAL model. This study can be considered as an applied research, from purpose point of view and descriptive-survey, with regards to the nature and method. Passengers of Kaveh and Sofeh terminal in Isfahan have been considered as research population. Sample size included 200 passengers witch was determined by Cochran formula. Spss 19 was used to analyze collected data. Results show that there is a significant positive relationship between service quality and customers’ satisfaction. It is also proved that in terms of the importance of satisfactions’ dimensions, assurance is the most important aspect and then reliability, empathy, equipment appearance and responsiveness in sequence are the most important dimensions.
Mon, 25 September 2017
ARTICLE Download: 419| View: 361| Comments: 0 | doi:10.20944/preprints201709.0115.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Bivariate Kumaraswamy distribution; copula based construction; Kendall'stau; dependence structures; application in insurance risk modeling
Online: 25 September 2017 (06:55:52 CEST)
A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of (Farlie-Gumbel-Morgenstern) FGM bivariate copula for constructing several dierent bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It is established that construction of bivariate distributions by this method allows for greater flexibility in the values of Spearman's correlation coefficient rho, and Kendall's tau . For illustrative purposes, one representative data set is utilized to exhibit the applicability of these proposed bivariate copula models.
Mon, 11 September 2017
ARTICLE Download: 427| View: 394| Comments: 0 | doi:10.20944/preprints201709.0035.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: exchange traded funds; financial and energy sectors; co-volatility spillovers; spot and futures prices; generated regressors; Diagonal BEKK
Online: 11 September 2017 (04:35:24 CEST)
It is well known that that there is an intrinsic link between the financial and energy sectors, which can be analyzed through their spillover effects, which are measures of how the shocks to returns in different assets affect each other’s subsequent volatility in both spot and futures markets. Financial derivatives, which are not only highly representative of the underlying indices, but can also be traded on both the spot and futures markets, include Exchange Traded Funds (ETFs), a tradable spot index whose aim is to replicate the return of an underlying benchmark index. When ETF futures are not available to examine spillover effects, “generated regressors” are useful for constructing both Financial ETF futures and Energy ETF futures. The purpose of the paper is to investigate the co-volatility spillovers within and across the US energy and financial sectors in both spot and futures markets, by using “generated regressors” and a multivariate conditional volatility model, namely Diagonal BEKK. The daily data used are from 1998/12/23 to 2016/4/22. The data set is analyzed in its entirety, and are also subdivided into three distinct subsets. The empirical results show there is a significant relationship between the Financial ETF and Energy ETF in the spot and futures markets. Therefore, financial and energy ETFs are suitable for constructing a financial portfolio from an optimal risk management perspective, and also for dynamic hedging purposes.
Fri, 30 June 2017
ARTICLE Download: 917| View: 677| Comments: 0 | doi:10.20944/preprints201706.0129.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: decomposition; LMDI; decoupling; heavy industry
Online: 30 June 2017 (09:08:44 CEST)
China is facing huge pressure on CO2 emissions reduction. The heavy industry accounts for over 60% of China’s total energy consumption, and thus lead to a large number of energy-related carbon emissions. This paper adopts the Log Mean Divisia Index (LMDI) method based on the extended Kaya identity to explore the influencing factors of CO2 emissions from China’s heavy industry; we calculate the trend of decoupling by presenting a theoretical framework for decoupling. The results show that labor productivity, energy intensity, and industry scale are the main factors affecting CO2 emissions in the heavy industry. The improvement of labor productivity is the main cause of the increase in CO2 emissions, while the decline in energy intensity leads to CO2 emissions reduction, and the industry scale has different effects in different periods. Results from the decoupling analysis show that efforts made on carbon emission reduction, to a certain extent, achieved the desired outcome but still need to be strengthened.
Tue, 16 May 2017
ARTICLE Download: 585| View: 546| Comments: 0 | doi:10.20944/preprints201705.0119.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: subunit distribution; structural analysis; statistical hypothesis; parameter estimation; sequential test
Online: 16 May 2017 (07:41:47 CEST)
Studies on the structure of economic systems are, most frequently, carried out by the methods of informational statistics. These methods, often accompanied by a wide range of indicators (Shannon entropy, Balassa coefficient, Herfindahl specialty index, Gini coefficient, Theil index etc.) around which a wide literature has been created over time, have a major disadvantage. Such weakness is related to the imposition of the system condition, therefore the need to know all the components of the system (as absolute values or as weights). This restriction is difficult to accomplish in some situations, and in others, this knowledge may be irrelevant, especially when there is an interest in structural changes only in some of the components of the economic system (either we refer to the typology of economic activities - NACE or of territorial units – NUTS). This article presents a procedure for characterizing the structure of a system and for comparing its evolution over time, in the case of incomplete information, thus eliminating the restriction existent in the classical methods. The proposed methodological alternative uses a parametric distribution, with subunit values for the variable. The application refers to Gross Domestic Product values for five of the 28 European Union countries, with annual values of over 1,000 billion Euros (Germany, Spain, France, Italy and United Kingdom) for the years 2003 and 2015. A form of the Wald sequential test is applied to measure changes in the structure of this group of countries, between the years compared. The results of this application validate the proposed method.
Mon, 8 May 2017
ARTICLE Download: 792| View: 689| Comments: 0 | doi:10.20944/preprints201701.0065.v2
Subject: Social Sciences, Econometrics & Statistics Keywords: lee-carter; cairns-blake-dowd; mortality models; backtesting
Online: 8 May 2017 (07:38:50 CEST)
The work proposes a backtesting analysis in comparison between the Lee-Carter and the Cairns-Blake-Dowd mortality models, employing Italian data. The mortality data come from the Italian National Statistics Institute (ISTAT) database and span the period 1975-2014, over which we computed back-projections evaluating the performances of the models in comparisons with real data. We propose three different backtest approaches, evaluating the goodness of short-run forecast versus medium-length ones. We find that both models were not able to capture the improving shock on the mortality observed for the male population on the analyzed period. Moreover, the results suggest that CBD forecast are reliable prevalently for ages above 75, and that LC forecast are basically more accurate for this data.
Tue, 21 March 2017
ARTICLE Download: 777| View: 748| Comments: 0 | doi:10.20944/preprints201703.0169.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: China; sustainability development; carbon emissions; carbon flow; sectoral analysis
Online: 21 March 2017 (04:28:01 CET)
Reducing carbon emissions is a major ways to achieving green development and sustainability for China’s future. This paper elaborates the detailed feature of China's carbon flow for 2013 with the carbon flow chart and gives changing characteristics of China's CO2 flow from the viewpoint of sector and energy during 2000 and 2013. The results show that (1) during 2000 to 2013, China's CO2 emissions with the approximately growth portion of 9% annually, while the CO2 intensity of China diminishes at different rates. (2) The CO2 emissions from secondary industry are prominent from the perspective of four main sectors accounting for 83.5%. The manufacturing play an important part in the secondary industry with 45%. In which the "smelting and pressing of metal" takes up a large percentage as about 50% in manufacturing. (3) The CO2 emissions produced by coal consumption is keep dominant in energy-related emissions with a contribution of 65%, while it will decrease in the future. (4) From the aspect of sector, the CO2 emissions mainly come from the "electricity and heating" sector and the "smelting and pressing of metals" sub-sector. While it is essential and urgent to propose concrete recommendations for CO2 emissions mitigation. Firstly, the progression of creative technology is inevitable and undeniable. Secondly, the government should make different CO2 emissions reduction policies among different sectors. For example, the process emission plays an important role in "non-metallic mineral" while in "smelting and manufacturing of metals" it is energy. Thirdly, the country can change the energy structure and promote renewable energy for powering by wind or other low-carbon energy. Besides it, the coke oven gas can be a feasible substitution. Finally, policy maker should be aware of the emissions from residents have been growing in a fast rate. It is effective to involve the public in the activity of energy conservation and carbon emissions reduction such as reducing the times of personal transportation.
Thu, 16 March 2017
ARTICLE Download: 611| View: 688| Comments: 0 | doi:10.20944/preprints201703.0117.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: goodness-of-fit; time series; copulas; GARCH models
Online: 16 March 2017 (09:38:24 CET)
In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural changes and specification tests of the distribution of innovations are discussed. It is also shown that if the stochastic volatility matrices are diagonal, which is the case if the univariate time series are estimated separately instead of being jointly estimated, then the empirical copula process behaves as if the innovations were observed; a remarkable property. As a by-product, one also obtains the asymptotic behavior of rank-based measures of dependence applied to residuals of these time series models.
Mon, 13 March 2017
ARTICLE Download: 561| View: 579| Comments: 0 | doi:10.20944/preprints201703.0065.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: generalized estimating equations; overdispersion; poisson; spatio-temporal; Leishmaniasis
Online: 13 March 2017 (09:30:11 CET)
This paper is motivated by spatio-temporal pattern in the occurrence of Leishmaniasis in Afghanistan and the relatively high number of zero counts. We hold the view that correlations that arise from spatial and temporal sources are inherently distinct. Our method decouples these two sources of correlations, there are at least two advantages in taking this approach. First, it circumvents the need to inverting a large correlation matrix, which is a commonly encountered problem in spatio-temporal analyses. Second, it simplifies the modelling of complex relationships such as anisotropy, which would have been extremely difficult or impossible if spatio-temporal correlations were simultaneously considered. We identify three challenges in the modelling of a spatio-temporal process: (1) accommodation of covariances that arise from spatial and temporal sources; (2) choosing the correct covariance structure and (3) extending to situations where a covariance is not the natural measure of association. Moreover, because the data covers a period that overlaps with the US invasion of Afghanistan, the high number of zero counts may be the result of no disease incidence or lapse of data collection. To resolve this issue, a model truncated at zero built on a foundation of the generalized estimating equations was proposed.
Fri, 17 February 2017
ARTICLE Download: 692| View: 887| Comments: 0 | doi:10.20944/preprints201702.0064.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: St. Petersburg Paradox; entrance fees; Bayesian analysis
Online: 17 February 2017 (07:18:58 CET)
In his best-selling book An Introduction to Probability Theory and its Applications, W. Feller established a way of ending the St. Petersburg Paradox by the introduction of an entrance fee, and provided it for the case in which the game is played with a fair coin. A natural generalization of his method is to establish the entrance fee for the case in which the probability of head is θ (0 < θ < 1/2). The deduction of those fees is the main result of Section 2. We then propose a Bayesian approach to the problem. When the probability of head is θ (1/2 < θ < 1) the expected gain of the St. Petersburg Game is finite, therefore there is no paradox. However, if one takes θ as a random variable assuming values in (1/2,1) the paradox may hold, what is counter-intuitive. On Section 3 we determine a necessary and sufficient condition for the absence of paradox on the Bayesian approach and on Section 4 we establish the entrance fee for the case in which θ is uniformly distributed in (1/2,1), for in this case there is paradox.
Fri, 13 January 2017
ARTICLE Download: 1164| View: 795| Comments: 0 | doi:10.20944/preprints201701.0065.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: lee-carter; cairns-blake-dowd; mortality models; backtesting
Online: 13 January 2017 (01:56:49 CET)
The work proposes a backtesting analysis in comparison between the Lee-Carter and the Cairns-Blake-Dowd mortality models, employing Italian data. The mortality data come from the Italian National Statistics Institute (ISTAT) database and span the period 1975-2014, over which we computed back-projections evaluating the performances of the models in comparisons with real data. We propose three different backtest approaches, evaluating the goodness of short-run forecast versus long-run ones. We find that both models were not able to capture the improving shock on the mortality observed for the male population on the analyzed period. Moreover, the results suggest that CBD forecast are reliable prevalently for ages above 75, and that LC forecast are basically more accurate for this data.
Thu, 29 December 2016
ARTICLE Download: 895| View: 746| Comments: 0 | doi:10.20944/preprints201612.0137.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: cross-sectional heteroskedasticity; model specification strategy; Sargan-Hansen (incremental) tests; variants of t-tests; weighting matrices; Windmeijer-correction
Online: 29 December 2016 (07:32:36 CET)
Studies employing Arellano-Bond and Blundell-Bond GMM estimation for single linear dynamic panel data models are growing exponentially in number. However, for researchers it is hard to make a reasoned choice between many different possible implementations of these estimators and associated tests. By simulation the effects are examined of many options regarding: (i) reducing, extending or modifying the set of instruments; (ii) specifying the weighting matrix in relation to the type of heteroskedasticity; (iii) using (robustified) 1-step or (corrected) 2-step variance estimators; (iv) employing 1-step or 2-step residuals in Sargan-Hansen overall or incremental overidentification restrictions tests. This is all done for models in which some regressors may be either strictly exogenous, predetermined or endogenous. Surprisingly, particular asymptotically optimal and relatively robust weighting matrices are found to be superior in finite samples to ostensibly more appropriate versions. Most of the variants of tests for overidentification restrictions show serious deficiencies. A recently developed modification of GMM is found to have great potential when the cross-sectional heteroskedasticity is pronounced and the time-series dimension of the sample not too small. Finally all techniques are employed to actual data and lead to some profound insights.
Thu, 15 December 2016
ARTICLE Download: 826| View: 769| Comments: 0 | doi:10.20944/preprints201612.0081.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: aluminum resources; sustainable supply; supply structure; guarantee degree
Online: 15 December 2016 (10:13:06 CET)
Aluminum is a strategic mineral resource, and China’s aluminum production and consumption is fairly large. However, its supply guarantee is uncertain because of a high dependency on external raw materials. This uncertainty may expand, so finding a way to reduce the uncertainty of aluminum resource supply is especially important. This paper applies the SFA method to analyze the aluminum flows in mainland China from 1996 to 2014, and establishes a supply structure model to measure its supply guarantee degree. The results claim that: (1) China’s aluminum production can satisfy demand and even create a surplus; (2) Domestic self-productive primary and secondary aluminum increased at an annual rate of 12% and 24%; (3) The proportion of self-productive secondary aluminum in the supply structure increased from 7.7% in 1996 to 12.8% in 2014, while that of primary aluminum decreased from 79.6% to 42.8%; (4) The total supply guarantee degree decreased from 87.3% to 55.6% in this period. These results provide a feasible way to solve this plight: the proportion of secondary aluminum in the supply structure should be enhanced, and an efficient aluminum resource recycling system needs to be established as soon as possible to ensure its sustainable supply.
Fri, 14 October 2016
ARTICLE Download: 1050| View: 1059| Comments: 0 | doi:10.20944/preprints201610.0051.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: residential electricity consumption; income; piecewise linear model; China; robust tests
Online: 14 October 2016 (09:53:13 CEST)
There are many uncertainties and risks in residential electricity consumption during the economic development. Knowledge of the relationship between residential electricity consumption and its key determinant—income—are important to the sustainable development of electric power industry. Using panel data from 30 provinces for the 1995-2012 period, this study investigates how residential electricity consumption changes as incomes increase in China. Previous studies typically used linear or quadratic double-logarithmic models imposing ex ante restrictions on the indistinct relationship between residential electricity consumption and income. Contrary to those models, we employed a reduced piecewise linear model that is self-adaptive and highly flexible and circumvents the problem of “prior restrictions.” Robust tests of different segment specifications and regression methods are performed to ensure the conservatism of the research. The results provide strong evidence that the income elasticity was approximately one, and it remained stable throughout the estimation period. The income threshold at which residential electricity consumption automatically remains stable or slows has not been reached. To ensure the sustainable development of the electric power industry, introducing higher energy efficiency standards for electrical appliances and improving income levels are vital. And government should emphasize electricity conservation in industrial sector rather than in residential sector.
Mon, 1 August 2016
ARTICLE Download: 982| View: 901| Comments: 0 | doi:10.20944/preprints201608.0001.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: discrete-time hazard models; labour market transitions; duration of unemployment spells; immigration
Online: 1 August 2016 (09:47:20 CEST)
This paper studies the duration patterns of unemployment spells for immigrants and the determinants of unemployment’s completion into one of a number of studied labour market states in Finland. We estimate a duration model for unemployment with competing risks of its terminating into employment, labour market training or economic inactivity. Taking into account the wide period of observation and opportunities to analyse processes of labour market integration during various periods of economic development in Finland, in combination with the individualistic character of the labour careers of immigrants, this research is beneficial owing to the many various findings concerning labour market integration of immigrants. The approach undertaken in this research has a dualistic “descriptive-dynamic” character under which integration is understood as a never-ending process, which is conditioned by a time period of long-term existence and a context of solitary action. We find that transitions out of unemployment spells have a cyclical character; after every new “cycle” in unemployment, the probability of terminating unemployment decreases further. We also find that ascriptive factors make sense in the process of job-placement of immigrants from unemployment. Therefore, the gender, education and age of immigrants, as well as the effect of the period in which first unemployment occurred, potentially predict transitions out of unemployment and further labour market integration of immigrants.
Mon, 18 July 2016
ARTICLE Download: 1052| View: 1026| Comments: 0 | doi:10.20944/preprints201607.0047.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: SAINT model; SiZer; local linear fitting; mortality data
Online: 18 July 2016 (10:35:40 CEST)
The main contribution of this paper is to develop a graphical tool to evaluate the suitability of a candidate parametric model to fit a data set. The practical motivation is to check the adequacy of the so called SAINT model proposed in Jarner and Kryger (2011). This model has been implemented in practice by an important European pension fund concerned with setting annuity reserves for all current or former employees of Denmark. So, one particular relevant question is whether this mortality model is actually fitting old-age. Our graphical test can be described as follows. First we transform the data by means of the parametric model which is being evaluated. Should the model be correct, the transformed data will be in accordance with an Exponential distribution with rate 1. Then we construct a family of local linear hazard estimates based on the data on the transformed scale. Finally we use the statistical tool SiZer to assess the goodness-of-fit of the Exponential distribution to the data on the transformed scale. If the model is correct the SiZer map should not reveal any significant feature in the family of kernel estimates. Our method allow us to establish a diagnostic regarding the validity of the SAINT model when describing mortality patterns in four different countries.
Thu, 7 July 2016
ARTICLE Download: 1166| View: 1252| Comments: 0 | doi:10.20944/preprints201607.0007.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Bayesian networks; directed acyclic graphs; employee loyalty; employment arrangements; flexi-time; job satisfaction; teleworking; workplace employment relations survey
Online: 7 July 2016 (12:12:14 CEST)
This study explores the relationship between job satisfaction, employee loyalty and two types of flexible employment arrangements; teleworking and flexi-time. The analysis relies on data derived by the Workplace Employee Relations Survey (WERS) in 2004 and 2011. A propensity score matching and least squares regressions are applied. Furthermore, Bayesian Networks (BN) and Directed Acyclic Graphs (DAGs) are employed in order to confirm the causality between employment types explored and the outcomes of interest. Finally, an instrumental variables (IV) approach based on the BN framework is proposed and applied in this study. The results support that there is a positive causal effect from these employment arrangements on job satisfaction and employee loyalty.
Mon, 4 July 2016
ARTICLE Download: 1698| View: 1449| Comments: 0 | doi:10.20944/preprints201607.0004.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Bayesian modeling, long memory/anti-persistence; continuous time modeling; MCMC
Online: 4 July 2016 (09:57:31 CEST)
Using recent developments in econometrics and computational statistics we consider the estimation of the instantaneous rate of asset return process when the underlying Data Generating Mechanism (DGM) is an Ornstein-Uhlenbeck process, driven by fractional noise, and sampled at fixed intervals of length h. To address the problem we adopt throughout the paper an exact discretization approach. This enable us to exploit the fact that a flow sampling scheme arises naturally when observing the DGM. For, while the instantaneous rate of return process is unobservable at points in time, its time integral over successive observations is observable since it equals the increment of log-prices. Exact discretization delivers an ARIMA(1,1,1) model for log-prices with a fractional driving noise. Building on the resulting exact discretization formulae and covariance function, a new Markov Chain Monte Carlo (MCMC) scheme is proposed and we examine the properties of both the time and frequency domain likelihoods / posteriors through Monte Carlo. For the exact discrete model we adopt a general sampling interval of length h. This allow us to determine the optimal choice of h independent of the sample size. An empirical application using high frequency stock price data is presented showing the relevance of aggregation over time issues in modelling asset prices.