Esse trabalho traz os resultados do retorno salarial de se investir em educação no Brasil e desagregado para cada Estado da Unidade da Federação. Diferentes técnicas econométricas foram estimadas, como Mincer, Mincer Adaptado, Método de Variáveis Instrumentais, Método de Heckman e por fim Metodologia de Hansen (2000) que permite inovar a idéia de não linearidade na equação de rendimento proposta por Jaccob Mincer estimada de forma endógena. A analise empírica foi feita com base nas PNAD´s de 2001 a 2008 com dados em dados cross-section. Os resultados apresentados diferenciam em magnitude de cada método econométrico utilizado em destaque o Método de Hansen que permitiu identificar limiares em que o retorno de se investir em educação muda a partir de certo nível de escolaridade quebrada a hipótese de linearidade. Implicando que a não consideração de tal hipótese pode acabar distorcendo a compreensão da função que a educação em si exerce, por exemplo, na distribuição de renda. / This paper presents the results of the wage return of investing in education in Brazil and the disaggregated for each member of the Office of the Federation. Others Different from econometric were estimated as Mincer, Mincer Adapted, All Variable Instrumental, Method of Heckman and finally Methodology Hansen (2000) to innovation to go from no linearity in the equation the revenue proposed by Mincer Jaccob estimated and endogen. The analysis empiric was based on PNAD´s from 2001 to 2008 data with data in cross-section. The presented results differ in magnitude for each method used econometric highlighted the method of which Hansen identified thresholds at which the return of investing in education change it from a certain point of education to broken hypothesis linearity. Implying that if consideration in such a hypothesis can end up distorting the understanding of the function what education the exercise it, for example, distribution the income.

Esse trabalho traz os resultados do retorno salarial de se investir em educação no Brasil e desagregado para cada Estado da Unidade da Federação. Diferentes técnicas econométricas foram estimadas, como Mincer, Mincer Adaptado, Método de Variáveis Instrumentais, Método de Heckman e por fim Metodologia de Hansen (2000) que permite inovar a idéia de não linearidade na equação de rendimento proposta por Jaccob Mincer estimada de forma endógena. A analise empírica foi feita com base nas PNAD´s de 2001 a 2008 com dados em dados cross-section. Os resultados apresentados diferenciam em magnitude de cada método econométrico utilizado em destaque o Método de Hansen que permitiu identificar limiares em que o retorno de se investir em educação muda a partir de certo nível de escolaridade quebrada a hipótese de linearidade. Implicando que a não consideração de tal hipótese pode acabar distorcendo a compreensão da função que a educação em si exerce, por exemplo, na distribuição de renda. / This paper presents the results of the wage return of investing in education in Brazil and the disaggregated for each member of the Office of the Federation. Others Different from econometric were estimated as Mincer, Mincer Adapted, All Variable Instrumental, Method of Heckman and finally Methodology Hansen (2000) to innovation to go from no linearity in the equation the revenue proposed by Mincer Jaccob estimated and endogen. The analysis empiric was based on PNAD´s from 2001 to 2008 data with data in cross-section. The presented results differ in magnitude for each method used econometric highlighted the method of which Hansen identified thresholds at which the return of investing in education change it from a certain point of education to broken hypothesis linearity. Implying that if consideration in such a hypothesis can end up distorting the understanding of the function what education the exercise it, for example, distribution the income.

The interest in telecommuting stems from the potential benefits in alleviating traffic congestion, decreasing vehicle miles traveled (VMT), and improving air quality by reducing the necessity for travel between home and the workplace. Despite the potential economic, environmental, and social benefits, telecommuting has not been widely adopted, and there is little consensus on the actual impacts of telecommuting. One of the major hurdles is lack of a sound instrument to quantify the impacts of telecommuting on individuals’ travel behavior. As a result, the telecommuting phenomenon has not received proper attention in most transportation planning and investment decisions, if not completely ignored. This dissertation addresses the knowledge gap in telecommuting studies by examining several factors. First, it proposes a comprehensive outline to reveal and represent the complexity in telecommuting patterns. There are various types of telecommuting engagement, with different impacts on travel outcomes. It is necessary to identify and distinguish between those people for whom telecommuting involves a substitution of work travel and those for whom telecommuting is an ancillary activity. Secondly, it enhances the current modeling framework by supplementing the choice/frequency approach with daily telework dimensions, since the traditional approach fails to recognize the randomness of telecommuting engagement in a daily context. A multi-stage modeling structure is developed, which incorporates choice, frequency, engagement, and commute, as the fundamental dimensions of telecommuting activity. One pioneering perspective of this methodology is that it identifies non-regular telecommuters, who represent a significant share of daily telecommuters. Lastly, advanced statistical modeling techniques are employed to measure the actual impacts of each telecommuting arrangement on travelers’ daily activity-travel behavior, focusing on time-use analysis and work trip departure times. This research provides a systematic and sound instrument that advances the understanding of the benefits and potentials of telecommuting and impacts on travel outcomes. It is expected to facilitate policy and decision makers with higher accuracy and contribute to the better design and analysis of transportation investment decisions.

<div> <div> <div> <p>First Chapter: The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.<br></p><p><br></p><p>Second Chapter: This paper proposes a new estimation procedure for the first-order spatial autoregressive (SAR) model, where the disturbance term also follows a first-order autoregression and its innovations may be heteroscedastic. The estimation procedure is based on the principle of indirect inference that matches the ordinary least squares estimator of the two SAR coefficients (one in the outcome equation and the other in the disturbance equation) with its approximate analytical expectation. The resulting estimator is shown to be consistent, asymptotically normal and robust to unknown heteroscedasticity. Monte Carlo experiments are provided to show its finite-sample performance in comparison with existing estimators that are based on the generalized method of moments. The new estimation procedure is applied to empirical studies on teenage pregnancy rates and Airbnb accommodation prices.<br></p><p><br></p><p>Third Chapter: This paper presents a sample selection model with spatial autoregressive interactions and studies the maximum likelihood (ML) approach to estimating this model. Consistency and asymptotic normality of the ML estimator are established by the spatial near-epoch dependent (NED) properties of the selection and outcome variables. Monte Carlo simulations, based on the characteristics of female labor supply example, show that the proposed estimator has good finite-sample performance. The new model is applied to empirical study on examining the impact of climate change on agriculture in Southeast Asia.<br></p></div></div></div><div><div><div> </div> </div> </div>

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Dans ce mémoire, je considère un modèle de sélection standard avec sélection non aléatoire. D’abord, je discute la validité et la ‘‘sharpness ’’ des bornes sur l’intervalle interquantile de la distribution de la variable aléatoire latente non censurée, dérivées par Blundell et al. (2007). Ensuite, je dérive les bornes ‘‘sharp ’’ sur l’intervalle interquantile lorsque la distribution observée domine stochastiquement au premier ordre celle non observée. Enfin, je discute la ‘‘sharpness’’ des bornes sur la variance de la distribution de la variable latente, dérivées par Stoye (2010). Je montre que les bornes sont valides mais pas nécessairement ‘‘sharp’’. Je propose donc des bornes inférieures ‘‘sharp’’ pour la variance et le coefficient de variation de ladite distribution. / In this master thesis, I consider a standard selection model with nonrandomly censored outcome. First, I discuss validity and sharpness of bounds on the interquantile range of the distribution of the uncensored outcome, derived by Blundell et al. (2007). Second, I give sharp bounds on the interquantile range respectively under stochastic dominance of the unobserved outcome distribution by the observed one, and in presence of an exclusion variable. Third, I discuss sharpness of the variance bounds given by Stoye (2010). I show that the bounds are not necessarily sharp and I provide sharp lower bounds on the variance and the coe fficient of variation.

Generally, service industries require a rapid innovation of service portfolios to gain and maintain a competitive advantage. In this context, service elimination is a tool of portfolio renewal, where customer retention is a strategic priority for companies. This is especially so because service elimination usually causes higher churn rates than an average churn in telecommunications. Thus, customer retention is seen as a major aspect in enhancing service elimination success. The purpose of this paper is to investigate the factors that increase customer churn in the case of service elimination. We use one of the three Hungarian telecommunication Operator's databases containing usage data three months before and after Service elimination in the course of a major service package reform. Contract-related information and demographics of 10 065 customers are used to differentiate between high and low churn factors, taking care of a possible sample selection problem. The results show that in the course of service elimination there is a significant positive relationship between price decrease, tenure, interaction intensity on the one, and customer retention on the other side. Besides these, demographics (age and residence) also play an important role in explaining churn rates during service elimination. Furthermore, we find that a higher monthly fee after elimination increases the customer´s usage intensity. This research aims to contribute both to service elimination, as well as to customer retention literature, by hierarchical modeling of retention and usage during service elimination with practical implications for decision-makers in rapidly innovating telecommunication markets. / Series: Department of Economics Working Paper Series

Submitted by admin tede (tede@pucgoias.edu.br) on 2017-11-22T13:39:54Z No. of bitstreams: 1 André Luiz Alves.pdf: 760209 bytes, checksum: 09b516d6ffcca2c7f66578b275613b36 (MD5) / Made available in DSpace on 2017-11-22T13:39:54Z (GMT). No. of bitstreams: 1 André Luiz Alves.pdf: 760209 bytes, checksum: 09b516d6ffcca2c7f66578b275613b36 (MD5) Previous issue date: 2017-09-22 / The objective of this work is to compare a proposed algorithm based on the RANdom SAmple Consensus (RANSAC) method for selection of samples, selection of variables and simultaneous selection of samples and variables with the Sucessive Projections Algorithm (SPA) from a chemical data set in the context of multivariate calibration. The proposed method is based on the RANSAC method and Multiple Linear Regression (MLR). The predictive capacity of the models is measured using the Root Mean Square Error of Prediction (RMSEP). The results allow to conclude that the Successive Projection Algorithm improves the predictive capacity of Ransac. It is concluded that the SPA positively influences the Ransac algorithm for selection of samples, for selection of variables and also for simultaneous selection of samples and variables. / O objetivo do trabalho é comparar um algoritmo proposto baseado no método consenso de amostra aleatória (RANdom SAmple Consensus, RANSAC) para seleção de amostras, seleção de variáveis e seleção simultânea de amostras e variáveis com o algoritmo de projeções sucessivas (Sucessive Projections Algorithm, SPA) a partir de conjuntos de dados químicos no contexto da calibração multivariada. O método proposto é baseado no método RANSAC e regressão linear múltipla (Multiple Linear Regression, MLR). A capacidade preditiva dos modelos é medida empregando o erro de previsão da raiz quadrada do erro quadrático médio (Root Mean Square Error Of Prediction, RMSEP). Os resultados permitem concluir que o Algoritmo das Projeções Sucessivas melhora a capacidade preditiva do Ransac. Conclui-se que o SPA influi positivamente no algoritmo Ransac para seleção de amostras, para seleção de variáveis e também para seleção simultânea de amostras e variáveis.

Noncontact, the failure of a postal survey sample member to receive a survey request, is a potential source of nonresponse bias that has largely been ignored. This is due to the difficulty of separating the components of nonresponse in postal surveys when nothing is heard from potential respondents. Yet, the need to understand postal nonresponse is increasing as more studies move to mixed mode designs incorporating a postal element, and technological, resource and societal changes increase the attractiveness of self-administered surveys. Thus, this research sought to estimate the level of noncontact in postal surveys, to identify the direction and magnitude of bias due to it, and to investigate targeted in-field mechanisms for reducing this bias. A series of empirical studies involving New Zealand postal surveys fielded between 2001 and 2006 were undertaken to meet these aims. Noncontact was found to relate to survey-independent demographic variables (e.g., age, household composition). Furthermore, its incidence was estimated to be as much as 400% higher than indicated by ‘gone, no address’ (GNA) returns, although an envelope message tested as part of the research was able to increase levels of GNA reporting significantly. Thus, noncontact was established as a nontrivial source of nonresponse in the surveys examined. As far as bias is concerned, noncontacts had a different profile compared to refusers and ineligibiles, and were estimated to account for up to 40% of net nonresponse error for some of the variables in the surveys examined. Accordingly, there appears to be a clear opportunity for methods targeted at reducing noncontact bias to improve final survey estimates for a range of items. A number of potential methods for reducing noncontact bias were explored, but only one had both a compelling theoretical foundation and potential for wide applicability; the noncontact propensity sampling (NPS) scheme. In a resampling simulation study a prototype of the scheme, which increases the selection probabilities for sample units with a higher predicted propensity for noncontact, consistently improved the demographic profile of valid postal survey returns compared to a simple random sample (SRS). Furthermore, the scheme reduced nonresponse bias by an average of 28% as measured against a range of frame variables (e.g., age, gender) and 17% as measured against survey variables for which census parameters were known (e.g., religiosity, household size, qualifications, income and marital status). Although the prototype NPS procedure increased the standard deviation of simulated point estimates for a given sample size (1,500 in this research), the effect was small; an average of 4% for frame variables and 2% for survey variables. Furthermore, the scheme had almost no impact on reported cooperation rates and is likely to be cost effective compared to other potential targeted in-field mechanisms, particularly in situations where researchers regularly survey a specific population. Pairing the scheme with three common post-survey adjustment methods (frame or census age/sex cell weighing, and response wave extrapolation) did not lead to consistently better estimates than an unweighted SRS. But this was largely due to the shortcomings of these methods because in many cases combining them with either sampling scheme (SRS or NPS) actually degraded estimates. This reinforces the idea that researchers should expend effort minimising bias during the field period rather than relying on post-survey weighting to deal with the issue. Finally, since the NPS scheme aims to reduce error due to noncontact but is not expected to affect error due to other components (e.g., refusal, ineligibility), it presents an opportunity for researchers to begin decomposing the various facets of postal survey nonresponse bias, an important precursor to the development of other targeted bias reduction interventions. Thus, as a methodological tool, the NPS scheme may serve a dual role as both a bias reduction and decomposition mechanism. In addition to their implications for postal survey research, the methods developed and insights into noncontact established in this research are likely to have applications in other domains. In particular, they will inform activities such as research into online survey nonresponse, organisational database management cost reduction and list procurement.