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The purpose of this paper is to propose a semiparametric estimator for the tail index of Pareto-type random truncated data that improves the existing ones in terms of mean square error. Moreover, we establish its consistency and asymptotic normality.

To construct a root mean squared error (RMSE)-reduced estimator of the tail index, the authors used the semiparametric estimator of the underlying distribution function given by Wang (1989). This allows us to define the corresponding tail process and provide a weak approximation to this one. By means of a functional representation of the given estimator of the tail index and by using this weak approximation, the authors establish the asymptotic normality of the aforementioned RMSE-reduced estimator.

In basis on a semiparametric estimator of the underlying distribution function, the authors proposed a new estimation method to the tail index of Pareto-type distributions for randomly right-truncated data. Compared with the existing ones, this estimator behaves well both in terms of bias and RMSE. A useful weak approximation of the corresponding tail empirical process allowed us to establish both the consistency and asymptotic normality of the proposed estimator.

A new tail semiparametric (empirical) process for truncated data is introduced, a new estimator for the tail index of Pareto-type truncated data is introduced and asymptotic normality of the proposed estimator is established.

The study aims at estimating the shadow economy (SE) using the method of demand for currency in Palestine for the period 2008–2018 by studying the relationship between a group of variables that affect the ratio of money traded outside the banking system to the money supply in the broad sense.

The study has adopted analytical and descriptive research methods to estimate SE in Palestinian territories. The data has been obtained from the inflation reports issued by the Palestinian Monetary Authority for ten years, from 2008 to 2018. A standard model was constructed using EViews version 8 for statistical data processing after converting the annual data to quarterly data.

The authors demonstrated that the size of the SE in Palestinian territories has varied over time, and the annual average of its size during the study period reached about $1764.893 (in millions). This amount constitutes about 15.5% of the gross domestic product. The study provides recommendations for reducing the size of the SE in Palestinian territories.

The current study shows that shadow economics could significantly matter for economic policy design by policymakers.

This study deals directly with Tanzi’s “estimation of shadow economy in Palestinian territories” concept and its impact on economic policies.

The purpose of this paper is to estimate the impact of credit rationing on the amount of capital allocated to inputs used by rice farmers in the Mekong River Delta (MRD).

Based on the literature review, the authors propose nine hypotheses on the determinants of access of rice farmers to credit and four hypotheses on the impact of credit rationing on the amount of capital allocated to inputs used by rice farmers in the MRD. Data were collected from 1,168 farmer households randomly selected out of 10 provinces (city) in the MRD.

Step 1 of propensity score matching (PSM) with probit regression shows that land value, income, education, gender of household head and geographical distance to the nearest credit institution affect the degree of credit rationing facing rice farmers. Step 2 of PSM estimator identifies that the amount of capital allocated to inputs such as fertilizer and hired labour increases when credit rationing decreases while that allocated to seed and pesticide is not influenced by credit rationing because rice farmers use these inputs adamantly regardless of effectiveness.

This paper sheds light on the impact of credit rationing on the amount of capital allocated to inputs used by rice farmers, which is largely different from the main focus of the extant literature just on the determinants of credit rationing facing farmers in general and rice farmers in particular.

The purpose of this paper is to estimate the mean life expectancy (LE) and the expected years of life lost (EYLL) for ischemic stroke and intracerebral hemorrhage.

This retrospective cohort study included 5,210 patients with a diagnosis of first ischemic stroke or intracerebral hemorrhage between 2005 and 2013 from Ramathibodi Hospital, Bangkok, Thailand. The survival of each case was followed until December 31, 2016. A semiparametric extrapolation method was applied to estimate the lifetime survival function relative to an age and sex-matched reference population.

Of 5,210 patients, 74.2 percent experienced ischemic stroke. About 54.3 percent were men. Mean age at diagnosis was 64.3 years. The mean LE was 12.5 years for ischemic stroke and 12.0 years for intracerebral hemorrhage. The EYLL among patients with intracerebral hemorrhage was significantly higher than among those with ischemic stroke (10.1 vs 5.7). Women were expected to lose more LE than men for both types of stroke (p-value<0.05), while younger aged patients were expected to lose more years of life than older ones.

This study fulfilled an identified need to estimate LE and EYLL among patients with ischemic stroke and intracerebral hemorrhage.

While there exist many surveys on the use stochastic frontier analysis (SFA), many important issues and techniques in SFA were not well elaborated in the previous surveys, namely, regular models, copula modeling, nonparametric estimation by Grenander’s method of sieves, empirical likelihood and causality issues in SFA using regression discontinuity design (RDD) (sharp and fuzzy RDD). The purpose of this paper is to encourage more research in these directions.

A literature survey.

While there are many useful applications of SFA to econometrics, there are also many important open problems.

This is the first survey of SFA in econometrics that emphasizes important issues and techniques such as copulas.

Previous research has studied interactive value formation (IVF) using resource- or practice-based approaches but has neglected the role of emotions. This article aims to show how emotions are correlated in problematic social media interactions and explore their role in IVF.

By combining a text mining algorithm, nonparametric Spearman's rho and thematic qualitative analysis in an explanatory sequential mixed-method design, the authors (1) categorize customers' comments as positive, neutral or negative; (2) pinpoint peaks of negative comments; (3) classify problematic interactions as detrimental, contradictory or conflictual; (4) identify customers' main positive (joy, trust and surprise) and negative emotions (anger, dissatisfaction, disgust, fear and sadness) and (5) correlate these emotions.

Despite several problematic social interactions, the same pattern of emotions appears but with different intensities. Additionally, value co-creation, value no-creation and value co-destruction co-occur in a context of problematic social interactions (peak of negative comments).

This study provides new insights into the effect of customers' emotions during IVF by studying the links between positive and negative emotions and their effects on different sorts of problematic social interactions.

The purpose this paper is to review some of the statistical methods used in the field of social sciences.

A review of some of the statistical methodologies used in areas like survey methodology, official statistics, sociology, psychology, political science, criminology, public policy, marketing research, demography, education and economics.

Several areas are presented such as parametric modeling, nonparametric modeling and multivariate methods. Focus is also given to time series modeling, analysis of categorical data and sampling issues and other useful techniques for the analysis of data in the social sciences. Indicative references are given for all the above methods along with some insights for the application of these techniques.

This paper reviews some statistical methods that are used in social sciences and the authors draw the attention of researchers on less popular methods. The purpose is not to give technical details and also not to refer to all the existing techniques or to all the possible areas of statistics. The focus is mainly on the applied aspect of the techniques and the authors give insights about techniques that can be used to answer problems in the abovementioned areas of research.

As climate change impacts residential life, people typically use heating or cooling appliances to deal with varying outside temperatures, bringing extra electricity demand and living costs. Water is more cost-effective than electricity and could provide the same body utility, which may be an alternative choice to smooth electricity consumption fluctuation and provide living cost incentives. Therefore, this study aims to identify the substitute effect of water on the relationship between climate change and residential electricity consumption.

This study identifies the substitute effect of water and potential heterogeneity using panel data from 295 cities in China over the period 2004–2019. The quantile regression and the partially linear functional coefficient model in this study could reduce the risks of model misspecification and enable detailed identification of the substitution mechanism, which is in line with reality and precisely determines the heterogeneity at different consumption levels.

The results indicate that residential water consumption can weaken the impact of cooling demand on residential electricity consumption, especially in low-income regions. Moreover, residents exhibited adaptive asymmetric behaviors. As the electricity consumption level increased, the substitute effects gradually get strong. The substitute effects gradually strengthened when residential water consumption per capita exceeds 16.44 tons as the meeting of the basic life guarantee.

This study identifies the substitution role of water and heterogeneous behaviors in the residential sector in China. These findings augment the existing literature and could aid policymakers, investors and residents regarding climate issues, risk management and budget management.

The purpose of this paper is to incorporate risk in technical efficiency of ASEAN banks in a panel data framework for the period 2000 to 2015.

The directional distance function and semi-parametric framework are employed to estimate efficiency scores for two scenarios, one with only good outputs and the other with a combination of good and bad outputs.

The findings show there is no evidence of technological progress for banks in ASEAN and concerns about the outperformance of Vietnam’s banks. In addition, performance of Vietnam’s banks tends to be distorted by low level of loan loss reserves.

To reflect the true performance and shorten the period of removing bad assets, the State Bank of Vietnam can request banks in Vietnam to book more loan loss reserves.

By examining such a new approach, this study makes an early attempt to incorporate credit risk into the banking efficiency in ASEAN region.

This paper aims to apply regression-tree analysis to capture the nonlinear effects of corruption on economic growth. Using data of 103 countries for the period 1996–2017, the authors endogenously detect two distinct areas in corruption quality in which the members share the same model of economic growth.

The authors apply regression tree analysis to capture the nonlinearity of the influences. This methodology allows us to split endogenously the whole sample of countries and characterize the different ways through which corruption impacts economic growth in each group of countries.

The traditional determinants of economic growth have different impacts on countries depending on their level of corruption, which, in turn, confirms the parameter heterogeneity of the Solow model found in other strands of the literature.

The authors apply a new approach to a worldwide sample obtaining novel results.