Limited Dependent Variable Research Articles

Research on Environmental Performance Measurement and Influencing Factors of Key Cities in China Based on Super-Efficiency SBM-Tobit Model

In recent years, China has experienced significant economic growth and some degree of environmental pollution control. However, achieving a perfect balance between the environment and economic development remains a challenge. In order to seek solutions to this issue and promote the sustainable development of cities, this paper starts from the urban level, which is relatively lacking in existing research. Based on the panel data of urban indicators from 2013 to 2021, it quantifies the environmental performance of key cities using the slack-based measure (SBM) model of super-efficiency based on a non-expected output. Furthermore, it utilizes the Tobit panel regression model suitable for limited dependent variables to analyze the impact of driving factors on the environmental performance of key cities, and it further explores the reasons for the loss of urban environmental performance from the dual perspectives of inputs and outputs. The research findings indicate the following. (1) The average environmental performance of 30 key cities has shown an increasing trend but has not yet reached a valid state. The cities’ environmental performance rises in the range of [0.444, 0.821], indicating that there is room for improvement in urban environmental management. (2) Cities in the northeastern region of China have lagged behind the eastern, central, and western regions in terms of environmental performance over this nine-year period, and the redundancy of undesirable outputs is partly responsible for this decline. (3) The large proportion of the secondary industry, the number of vehicles on the road, and the population density have a significantly negative impact on urban environmental performance, while the per capita regional GDP and urban maintenance and construction funds make a positive difference. These research findings provide a scientific basis and valuable insights into urban environment performance enhancement and can serve as a reference for areas in need of balanced development between the urban environment and economic growth.

Simultaneously Incomplete and Incoherent (SII) Dynamic LDV Models: With an Application to Financing Constraints and Firms’ Decision to Innovate

We develop novel methods for establishing coherency and completeness conditions in Static and Dynamic Limited Dependent Variables (LDV) Models. We characterize the two distinct problems as “empty-region” incoherency and “overlap-region” incoherency or incompleteness and show that the two properties can co-exist. We focus on the class of models that can be Simultaneously Incomplete and Incoherent (SII). We propose estimation strategies based on Conditional Maximum Likelihood Estimation (CMLE) for simultaneous dynamic LDV models without imposing recursivity. Point identification is achieved through sign-restrictions on parameters or other prior assumptions that complete the underlying data process. Using as modelling framework the Panel Bivariate Probit model with State Dependence, we analyse the impact of financing constraints on innovation: ceteris paribus, a firm facing binding finance constraints is substantially less likely to undertake innovation, while the probability that a firm encounters a binding finance constraint more than doubles if the firm is innovative. In addition, a strong role for state dependence in dynamic versions of our models is established.

From a strategic management perspective, comparison of graduate/undergraduate education performances of research universities and efficiency determinants: The case of Turkey

This study determined undergraduate and graduate education efficiency scores using the 2020 data from 20 state research universities in Turkey. The study used input-oriented data envelopment analysis to compare undergraduate and graduate education efficiencies. In the study, the efficiency of the research universities in the prioritized field(s) was compared with the efficiency in the field(s) in which they operate intensively. It also includes suggestions on increasing their effectiveness in the prioritized field(s). In addition, the Tobit regression model, which is a regression model for limited dependent variables, was used to determine the determinants of efficiency scores. The findings show the undergraduate and graduate education performances of research universities comparatively. In addition, based on the results obtained from the Tobit regression model, suggestions were made to increase graduate performance. Five factors (the number of graduate students per faculty member, the number of undergraduate students per academic staff, the number of graduates/undergraduates in the number of students and graduations, and the number of faculty members per program) have a significant effect on graduate performance. Therefore, it is important in terms of strategic management that research universities should be restructured by considering these factors or that they should be considered in plans. The study offers an alternative perspective to performance management in both education and management.

Measurement Systems

Economic models often depend on quantities that are unobservable, either for privacy reasons or because they are difficult to measure. Examples of such variables include human capital (or ability), personal income, unobserved heterogeneity (such as consumer “types”), et cetera. This situation has historically been handled either by simply using observable imperfect proxies for each of the unobservables, or by assuming that such unobservables satisfy convenient conditional mean or independence assumptions that enable their elimination from the estimation problem. However, thanks to tremendous increases in both the amount of data available and computing power, it has become possible to take full advantage of recent formal methods to infer the statistical properties of unobservable variables from multiple imperfect measurements of them. The general framework used is the concept of measurement systems in which a vector of observed variables is expressed as a (possibly nonlinear or nonparametric) function of a vector of all unobserved variables (including unobserved error terms or “disturbances” that may have nonadditively separable affects). The framework emphasizes important connections with related fields, such as nonlinear panel data, limited dependent variables, game theoretic models, dynamic models, and set identification. This review reports the progress made toward the central question of whether there exist plausible assumptions under which one can identify the joint distribution of the unobservables from the knowledge of the joint distribution of the observables. It also overviews empirical efforts aimed at exploiting such identification results to deliver novel findings that formally account for the unavoidable presence of unobservables. (JEL C30, C55, C57, D12, E21, E23, J24)

Primerjava različnih regresijskih modelov za napovedovanje debelinskega priraščanja jelke

We present seven alternative statistical models for modelling tree diameter increment with data from permanent sampling plots. In addition to the polynomial regression model, we present a regression model with added random noise, a mixed linear model, regression with natural splines, and three models with limited dependent variables: truncated regression, tobit regression and grouped data regression. The models may be used when dealing with truncated or censored variables, biased estimation of the increment due to censoring and rounding down, or when having multilevel data. The parametrization of the models was done using 21,013 fir trees on 4,405 plots in the period 1990–2014 in uneven-aged Dinaric fir-beech forests. All models showed a similar effect of tree diameter, stand basal area, basal area of larger trees, diameter structure diversity, altitude and slope. There were only minor differences in the regression coefficients and fit measures. The highest increment predictions were given by the tobit model. The mixed model fit the data best and, compared to the other models, predicted a slower decrease in the growth of large-diameter trees after growth culmination.

Comparison Between Nelson-Olson Method and Two-Stage Limited Dependent Variables (2SLDV ) Method for the Estimation of a Simultaneous Equations System (Tobit Model)

This study relates to the estimation of a simultaneous equations system for the Tobit model where the dependent variables ( ) are limited, and this will affect the method to choose the good estimator. So, we will use new estimations methods different from the classical methods, which if used in such a case, will produce biased and inconsistent estimators which is (Nelson-Olson) method and Two- Stage limited dependent variables(2SLDV) method to get of estimators that hold characteristics the good estimator .
 That is , parameters will be estimated for the limited variables and find the variance-covariance matrix for extracted estimators by the aforementioned two methods and then compare between the results of the two methods and find any better method by estimation and then finding the estimation efficiency, and this is what the study aims to .
 A simultaneous equations system will be imposed for the limited model defined by two equations containing two endogenous variables one of complete observations and the other censored at zero. 
 The two methods were used to analyze the relationship between income and family expenditure on durable consumer goods , where the results showed that the performance of (Nelson-Olson) method is better than performing the Two-Stage limited dependent variables (2SLDV) method in obtain the lower values and all comparison measures as well as the results showed that income and expenditure one affects the other and the 
 and the price affects the income and expenditure

How much should a pedestrian be fined for intentionally blocking a fully automated vehicle? A random parameters beta hurdle model with heterogeneity in the variance of the beta distribution

Intentionally blocking the path of fully automated vehicles is an important dimension of pedestrians’ receptivity towards these vehicles. The monetary value of this behaviour can be obtained by asking pedestrians about their perception of the “fine” for blocking the path of a fully automated vehicle. Econometric modelling of the reported fine can shed more light on factors influencing pedestrians’ receptivity towards fully automated vehicles. However, development of such an econometric model is not straightforward due to the unique characteristics of the dependent variable: it has two fundamentally different states; it is right-truncated; and it may be fat-tailed. Despite fairly extensive methodological advancements in econometric modelling of pedestrian behaviour, there is no model that can adequately explain these characteristics. While a beta distribution in a hurdle setting has the potential to address the above complexities, its applicability in dealing with limited dependent variables in transport applications has remained, by and large, unexplored.This study aims to fill this gap by developing a new beta hurdle regression model that systematically considers the dual-state of a right-truncated dependent variable representing the fine associated with intentionally blocking a fully automated vehicle. The hypothesized model is empirically tested using data obtained from a survey administered in Queensland, Australia, and the results are compared with truncated lognormal, and truncated lognormal hurdle regression models. Results indicate that the hurdle models are superior to the non-hurdle model. The beta variant of the hurdle model provides a better statistical fit for the data that are near their right limit. In addition, parametrizing the variance of the beta distribution captures the additional heterogeneity in the data. Age, gender, education level, violations, attitudes, behaviours that appease social interactions, and perceived ease or difficulty of interacting with fully automated vehicles influence the likelihood and/or the propensity of the fine and thus are associated with the perceived monetary value of intentionally blocking the path of a fully automated vehicle.

Mothers\u2019 daily perceived stress influences their children\u2019s mental health during SARS-CoV-2-pandemic\u2014an online survey

BackgroundThe current situation caused by the SARS-CoV-2-pandemic is associated with serious losses for everyone and has been affecting social life, politics, the economy and the media worldwide. Preventive isolation and social distancing strategies have confronted families with a large number of different challenges. The current epidemic and quarantine restrictions have a verifiable influence on the emotional and social development of children and adolescents. During this ongoing situation children of parents, who already were mentally stressed, seem particularly at risk.ObjectiveWe aimed to assess the role of maternal daily perceived stress on children’s mental health during the SARS-CoV-2-pandemic.MethodsAn online “SARS-CoV-2-pandemic survey” was developed to assess children’s mental health since the beginning of the SARS-CoV-2 pandemic. To describe maternal perceived everyday stress, data from a longitudinal survey was utilized. Our survey includes elements and versions of the Childhood Trauma Questionnaire, the Strengths and Difficulties Questionnaire and the Perceived Stress Scale. We furthermore collected socio-demographic data. Due to our limited dependent variables we applied Tobit models for estimation.ResultsWe found a positive and significant effect of the maternal perceived everyday stress on children’s emotional problems during the pandemic. Furthermore, results provide empirical evidence for an increase of the children's hyperactivity level dependent on the mother’s perceived stress before the SARS-COV-2 crisis. We could not find significant effects for the relationship between mother’s perceived everyday stress and the children's behavioral problems.ConclusionsAnalyses illustrate the effects on children's mental distress during everyday life in the SARS-CoV-2 pandemic. Future research needs to identify influencing factors with regard to political, economic and social restrictions, in order to prevent children’s mental health problems.

Movimentos de Emigração de Mulheres Rurais em Itapejara d’Oeste/PR: enfrentando relações de poder patriarcais

Este artigo analisa as relações de poder, saber e ser que, desde o colonialismo, constituíram hierarquias em torno das classificações de gênero. Nossa cultura rotula como não civilizada e não humana aquelas(es) que não se enquadram no modelo hegemônico, esse rótulo é usado para entender o papel da mulher no meio rural. Nas áreas rurais, as mulheres modernas buscam romper com as relações de poder estabelecidas, procuram maior independência e acesso à liderança pública. Diante dessas questões, vale a pena perguntar: quais são os fatores que empoderam as mulheres rurais? Observa-se que a escolaridade e as alternativas de renda não agrícola possibilitam quebrar sua invisibilidade nos estabelecimentos rurais. Para a construção da análise, são apresentados os dados estatísticos de uma amostra de 95 casos da população rural de Itapejara d'Oeste/PR, cujo levantamento inicial foi realizado em 2005 e posteriormente atualizado em 2010 e 2015, nesta análise, utilizou-se de ferramentas econométricas, com base no modelo de variável limitada dependente (MVLD). Os resultados indicaram a ocorrência de uma maior emigração de mulheres relacionadas à maior escolaridade, bem como uma redução dessa emigração quando as mulheres passaram a ter maior acesso a rendimentos não agrícolas no meio rural. Ambas as formas de acesso ao espaço público deram às mulheres rurais maior independência e provavelmente ajudaram a sair de sua invisibilidade.

The Duration of Unemployment in Sub-Saharan Africa: Study Using Microeconomic Data from the Republic of Congo

The Duration of Unemployment in Sub-Saharan Africa: Study Using Microeconomic Data from the Republic of Congo Ndombi Avouba Fabrice-gilles, Kouika Bouanza Jean Roméo Félix, Ganga Zandzou Ulrich Jeanin Aymar Abstract This article aims to analyze the factors explaining the duration of unemployment in Congo. To do so, we opted for a logit model with a limited dependent variable. Using data from a survey conducted by the Laboratoire de recherches et d'études économiques et sociales du Congo (LARES) in partnership with the International Development Research Centre (IDRC) in 2018), we show that the duration of unemployment in Congo is explained in part by job search intensity.This finding has implications for economic policy. Moreover, this result can be further developed in the future by conducting an analysis of the gender determinants of unemployment duration. Full Text: PDF DOI: 10.15640/jeds.v9n1a3

Estimación Logit y Probit del impacto en el tamaño relativo del sector agropecuario ante las variaciones del ingreso per cápita y el índice de libertad económica

In this work, it was proposed to answer the question: What is the relative size of the agricultural sector in a country in the face of improvements in economic freedom and the growth of the per-capita income of its inhabitants? Therefore, it was sought to estimate, probabilistically, the impact on the percentage of composition of agriculture in GDP (PAGP) in the face of marginal increases in the Index of Economic Freedom (IEF) and the Gross Domestic Product Per Capita (GDPbp); the methodology used was the estimation of econometric models for a limited dependent variable of the dichotomous form, specifically, Logit and Probit Models. For this estimation, PAGP was defined as a dependent variable, taking the value of one (1) countries with economies with PAGP greater than or equal to the median PAGP of all countries and zero (0) otherwise, IEF and GDPbp; were specified as independent variables. All data used are cross-sectional and correspond to the year 2018. The results obtained indicate that the unit variations of IEF and GDPbp generate a high probability that a country's PAGP is below the median PAGP of all countries, that is, as economic freedom and average income in a country increases, its agricultural sector has a high probability of losing relative weight in the economic composition of the country.

위험회피성향에 따른 부동산 자산 선호 분석*

For years, numerous attempts have been made to empirically prove the economic theory that safe assets are preferred when the tendency of risk aversion becomes strong. However, those attempts have been limited to financial products. In Korea, there are few precedent studies about the relationship between risk aversion and real estate assets even though more than 75% of household assets consist of real estate. Therefore, this study analyzes the relationship between risk aversion and real estate assets, based on data from the National Survey of Tax and Benefit 2017 and 2018. The findings show that people who have a higher tendency of risk aversion have less real estate assets. In addition, the empirical results are little different in the study that limited dependent variables to real estate assets other than a residential house. Considering findings from precedent studies and this study together, it is shown that consumers in real world, as well as those in economic theories, regard deposits and insurance products as safe assets but classify stocks and real estate properties as relatively risky assets. However, this study has limitations as the long-term relationship between risk aversion and real estate assets could not be analyzed due to limited data.

Macroeconomic imbalance procedure (MIP) scoreboard indicators and their predictive strength of ?multidimensional crises?

Research background: The evaluation of the predictive strength of MIP indicators in relation to crises is extremely important for the process of coordinating the economic policies of the EU countries. MIP is one of the pillars of the economic crisis prevention procedure. Predictive power of individual indicators has not been tested before their introduction.
 Purpose of the article: Evaluation of the predictive strength of fourteen MIP indicators in relation to multidimensional crises in the EU countries.
 Methods: We used ordered probit model to test the ability of MIP indicators to correctly predict episodes of ?multidimensional crises? (as defined by the authors) in the period between 2008 and 2017 in all EU Member States.
 Findings & Value added: We defined ?multidimensional crises?, combining several negative phenomena into one limited dependent variable. This work is also novel in its application of probit regression to test the predictive strength of MIP indicators with an ordered probit model. We identified five MIP variables which were statistically significant in predicting ?multidimensional crises? for all EU countries: net international investment position, nominal unit labour cost index, house price index, private sector credit flow and general government gross debt. Other variables turned out to be less important or not effective in crises prediction.

Taylor quasi-likelihood for limited generalized linear models

ABSTRACT It is a major research topic of limited generalized linear models, namely, generalized linear models with limited dependent variables. The models are developed in many research fields. However, quasi-likelihood estimation of the models is an unresolved issue, due to including limited dependent variables. We propose a novel quasi-likelihood, called Taylor quasi-likelihood, to handle with the unified estimation problem of the limited models. It is based on Taylor expansion of distribution function or likelihood function. We also extend the likelihood to a generalized version and an adaptive version and propose a distributed procedure to obtain the likelihood estimator. In low-dimensional setting, we give selection criteria for the proposed method and make arguments for the consistency and asymptotic normality of the estimator. In high-dimensional setting, we discuss feature selection and oracle properties of the proposed method. Simulation results confirm the advantages of the proposed method.

Modified likelihood ratio tests for unit gamma regressions

Regression analyses are commonly performed with doubly limited continuous dependent variables; for instance, when modeling the behavior of rates, proportions and income concentration indices. Several models are available in the literature for use with such variables, one of them being the unit gamma regression model. In all such models, parameter estimation is typically performed using the maximum likelihood method and testing inferences on the model's parameters are usually based on the likelihood ratio test. Such a test can, however, deliver quite imprecise inferences when the sample size is small. In this paper, we propose two modified likelihood ratio test statistics for use with the unit gamma regressions that deliver much more accurate inferences when the number of data points in small. Numerical (i.e. simulation) evidence is presented for both fixed dispersion and varying dispersion models, and also for tests that involve nonnested models. We also present and discuss two empirical applications.

CONSÓRCIOS PÚBLICOS DE SAÚDE: AS ADERÊNCIAS PELOS MUNICÍPIOS E O ATINGIMENTO DA EFICIÊNCIA DOS SERVIÇOS PRESTADOS

O presente trabalho tem como objetivo verificar se os municipios que participam de Consorcios de Saude aumentam a eficiencia dos servicos prestados. Depois de feita a analise de varios municipios respeitando os criterios elegidos, a escolha final resultou numa amostra de 100 municipios do estado de Minas Gerais. Para calcular um indice de eficiencia tecnica por municipio foi proposto o metodo de Data Envelopment Analysis – DEA, que e um metodo nao parametrico de programacao matematica para aferir a eficiencia de processos com diversos recursos e produtos. O modelo de regressao TOBIT, que e conhecido como um modelo de regressao com variavel dependente limitada, foi utilizado para verificar se existe diferenca entre os municipios consorciados ou nao no que se refere a eficiencia dos servicos prestados. O resultado mostrou nao haver evidencias estatisticas de que a aderencia do municipio pelo consorcio de saude implicara o aumento da eficiencia nas acoes de servicos de saude.

An empirical analysis of disinvestment policy in India: does ideology matter?

Using panel data estimation for limited dependent variables and sample selection models, we identify political, industry specific, firm specific and macroeconomic variables which influenced the decision to divest central public enterprises in India between 1991–2010. We find that higher partial privatization is driven by a more right-winged coalition, lower ideological spread in the coalition and lower ideological difference between the center and the state in which the public enterprise is located. We also find that after the government selects larger, more experienced and more profitable firms, it divests the relatively less experienced and less profitable firms to a larger extent.

Measurement of Technical Efficiency in Stochastic Frontier Analysis with Limited and Qualitative Dependent Variables

As vividly demonstrated in Maddala (1983), limited and qualitative dependent variables are often encountered in modern econometric analysis. However, analytical methods for evaluating technical efficiency of stochastic frontier analysis can only be applied to continuous dependent variable. This paper provides closed form formulae for evaluating the technical efficiency of stochastic frontier analysis with limited and qualitative dependent variables. Monte Carlo experiments reveal that the finite sample performances of our formulae are promising.

Experiential learning in high energy physics: a survey of students at the LHC

More than 36 000 students and post-docs will be involved until 2025 in research at the Large Hadron Collider (LHC) mainly through international collaborations. To what extent they value the skills acquired? Do students expect that their learning experience will have an impact on their professional future? By drawing from earlier literature on experiential learning, we have designed a survey of current and former students at LHC. To quantitatively measure the students’ perceptions, we compare the salary expectations of current students with the assessment of those now employed in different jobs. Survey data are analysed by ordered logistic regression models, which allow multivariate statistical analyses with limited dependent variables. Results suggest that experiential learning at LHC positively correlates with both current and former students’ salary expectations. Those already employed clearly confirm the expectations of current students. At least two not mutually exclusive explanations underlie the results. First, the training at LHC is perceived to provide students valuable skills, which in turn affect the salary expectations; secondly, the LHC research experience per se may act as signal in the labour market. Respondents put a price tag on their learning experience, a ‘LHC salary premium’ ranging from 5% to 12% compared with what they would have expected for their career without such an experience at CERN.

Identification and estimation of semiparametric two-step models

Let H0(X) be a function that can be nonparametrically estimated. Suppose E [Y|X]=F0[X⊤β0, H0(X)]. Many models fit this framework, including latent index models with an endogenous regressor and nonlinear models with sample selection. We show that the vector β0 and unknown function F0 are generally point identified without exclusion restrictions or instruments, in contrast to the usual assumption that identification without instruments requires fully specified functional forms. We propose an estimator with asymptotic properties allowing for data dependent bandwidths and random trimming. A Monte Carlo experiment and an empirical application to migration decisions are also included. Identification by functional form double index models two‐step estimators semiparametric regression control function estimators sample selection models empirical process theory limited dependent variables migration C13 C14 C21 D24