Efeitos da qualidade da educação sobre o diferencial de renda dos jovens no Brasil

Esta dissertação avalia o impacto da educação sobre a renda dos jovens no Brasil, seguindo a tradição de equações de determinação de salários. O trabalho difere dos trabalhos na área realizados no Brasil em quatro aspectos. Em primeiro lugar, Em primeiro lugar, o universo de análise está focado na população jovem brasileira, a qual ingressou recentemente no mercado de trabalho e nele permanecerá por muitos anos, o que traz informações sobre as características desse mercado para os próximos 25 a 35 anos. Além disso, ele difere porque introduz a qualidade do ensino como determinante dos rendimentos. Depois, porque adota um protocolo de imputação da qualidade da educação dos jovens para os quais não se tem informação sobre a qualidade da educação, de sorte a evitar viés de seleção. E, por fim, a dissertação contrasta com os estudos correntes no tema porque explora diferentes métodos de estimação dos retornos da educação e da qualidade do ensino. Além do método tradicional dos estimadores de OLS, este trabalho considera o uso de fronteiras estocásticas de salários. As estimativas foram feitas a partir de um modelo cross-section em dois estágios. No primeiro estágio, estimou-se a equação de determinação da probabilidade de um jovem entre 11 e 21 anos de idade estudar na rede pública ou na rede privada, escolas com diferenças qualitativas grandes no país. No segundo estágio, imputou-se um indicador de qualidade da educação dos jovens num modelo econométrico de determinação da renda dos jovens entre 16 e 25 anos de idade. O procedimento com imputação foi necessário simplesmente pelo fato de nas estatísticas brasileiras não haver informações sobre a qualidade do ensino adquirido pelos indivíduos. A análise permitiu mostrar que a qualidade da educação interfere de forma significativa na renda dos jovens, com grande impacto sobre os índices de desigualdade da distribuição de renda nessa faixa de idade. Também permitiu mostrar que existe um trade-off entre o retorno da e

Estrutura a termo da taxa de juros no Brasil e previsibilidade de ciclos econômicos

O objetivo deste trabalho é caracterizar a Curva de Juros Mensal para o Brasil através de três fatores, comparando dois tipos de métodos de estimação: Através da Representação em Espaço de Estado é possível estimá-lo por dois Métodos: Filtro de Kalman e Mínimos Quadrados em Dois Passos. Os fatores têm sua dinâmica representada por um Modelo Autorregressivo Vetorial, VAR(1), e para o segundo método de estimação, atribui-se uma estrutura para a Variância Condicional. Para a comparação dos métodos empregados, propõe-se uma forma alternativa de compará-los: através de Processos de Markov que possam modelar conjuntamente o Fator de Inclinação da Curva de Juros, obtido pelos métodos empregados neste trabalho, e uma váriavel proxy para Desempenho Econômico, fornecendo alguma medida de previsão para os Ciclos Econômicos.

Estimação de parâmetros de demanda e oferta em mercados de produtos diferenciados

Este trabalho tem por objetivo discutir detalhadamente o desenvolvimento de métodos de estimação de parâmetros de demanda e oferta em mercados de produtos diferenciados. As técnicas apresentadas consideram explicitamente a endogeneidade dos preços e podem ser aplicadas a diferentes tipos de indústrias. O sistema de demandas de mercado é derivado a partir de modelos de escolha discreta descrevendo o comportamento do consumidor. Esse sistema é então combinado com hipóteses sobre as funções custo e sobre o comportamento de determinação dos preços por parte das firmas para gerar preços e quantidades de equilíbrio. Os parâmetros a ser estimados são os que determinam os custos marginais das firmas e a distribuição dos gostos dos consumidores. Essa distribuição determina elasticidades e estas, combinadas com o custo marginal e com uma hipótese de equilíbrio de Nash na determinação de preços, determinam preços de equilíbrio. Essas elasticidades e parâmetros de custo desempenham um papel central em análises de questões descritivas e de mudanças no ambiente do mercado sob análise.

Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The Örst reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modiÖed information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of Ötted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new estimation algorithm where short- and long-term restrictions interact to estimate the cointegrating and the cofeature spaces respectively. We have three basic results. First, ignoring SCCF restrictions has a high cost in terms of model selection, because standard information criteria chooses too frequently inconsistent models, with too small a lag length. Criteria selecting lag and rank simultaneously have a superior performance in this case. Second, this translates into a superior forecasting performance of the restricted VECM over the VECM, with important improvements in forecasting accuracy ñreaching more than 100% in extreme cases. Third, the new algorithm proposed here fares very well in terms of parameter estimation, even when we consider the estimation of long-term parameters, opening up the discussion of joint estimation of short- and long-term parameters in VAR models.

On the statistical estimation of diffusion processes: a survey

Data available on continuos-time diffusions are always sampled discretely in time. In most cases, the likelihood function of the observations is not directly computable. This survey covers a sample of the statistical methods that have been developed to solve this problem. We concentrate on some recent contributions to the literature based on three di§erent approaches to the problem: an improvement of the Euler-Maruyama discretization scheme, the use of Martingale Estimating Functions and the application of Generalized Method of Moments (GMM).

On the statistical estimation of diffusion processes - a partial survey

Data available on continuous-time diffusions are always sampled discretely in time. In most cases, the likelihood function of the observations is not directly computable. This survey covers a sample of the statistical methods that have been developed to solve this problem. We concentrate on some recent contributions to the literature based on three di§erent approaches to the problem: an improvement of the Euler-Maruyama discretization scheme, the employment of Martingale Estimating Functions, and the application of Generalized Method of Moments (GMM).

Testing unit root based on partially adaptive estimation

This paper proposes unit tests based on partially adaptive estimation. The proposed tests provide an intermediate class of inference procedures that are more efficient than the traditional OLS-based methods and simpler than unit root tests based on fully adptive estimation using nonparametric methods. The limiting distribution of the proposed test is a combination of standard normal and the traditional Dickey-Fuller (DF) distribution, including the traditional ADF test as a special case when using Gaussian density. Taking into a account the well documented characteristic of heavy-tail behavior in economic and financial data, we consider unit root tests coupled with a class of partially adaptive M-estimators based on the student-t distributions, wich includes te normal distribution as a limiting case. Monte Carlo Experiments indicate that, in the presence of heavy tail distributions or innovations that are contaminated by outliers, the proposed test is more powerful than the traditional ADF test. We apply the proposed test to several macroeconomic time series that have heavy-tailed distributions. The unit root hypothesis is rejected in U.S. real GNP, supporting the literature of transitory shocks in output. However, evidence against unit roots is not found in real exchange rate and nominal interest rate even haevy-tail is taken into a account.

Optimal IV estimation of systems with stochastic regressors and var disturbances with applications to dynamic systems

This paper considers the general problem of Feasible Generalized Least Squares Instrumental Variables (FG LS IV) estimation using optimal instruments. First we summarize the sufficient conditions for the FG LS IV estimator to be asymptotic ally equivalent to an optimal G LS IV estimator. Then we specialize to stationary dynamic systems with stationary VAR errors, and use the sufficient conditions to derive new moment conditions for these models. These moment conditions produce useful IVs from the lagged endogenous variables, despite the correlation between errors and endogenous variables. This use of the information contained in the lagged endogenous variables expands the class of IV estimators under consideration and there by potentially improves both asymptotic and small-sample efficiency of the optimal IV estimator in the class. Some Monte Carlo experiments compare the new methods with those of Hatanaka [1976]. For the DG P used in the Monte Carlo experiments, asymptotic efficiency is strictly improved by the new IVs, and experimental small-sample efficiency is improved as well.

The impact of structured teaching methods on the quality of education

This paper estimates the impact of the use of structured methods on the quality of education of the students in primary public school in Brazil. Structure methods encompass a range of pedagogical and managerial instruments applied to the education system. In recent years, several municipalities in the State of São Paulo have contracted out private educational providers to implement these structured methods in their schooling system. Their pedagogical proposal involves structuring curriculum contents, elaboration and use of teachers and students textbooks, and training and supervision of the teachers and instructors. Using a difference in differences estimation strategy, we find that the fourth and eighth grader students in the municipalities with structured methods performed better in Portuguese and Math than students in municipalities not exposed to the methods. We find no differences in approval rates. However, a robustness check is not able to discard the possibility that unobserved municipal characteristics may affect the results.

Bayesian analysis of extreme events with threshold estimation

The aim of this paper is to analyze extremal events using Generalized Pareto Distributions (GPD), considering explicitly the uncertainty about the threshold. Current practice empirically determines this quantity and proceeds by estimating the GPD parameters based on data beyond it, discarding all the information available be10w the threshold. We introduce a mixture model that combines a parametric form for the center and a GPD for the tail of the distributions and uses all observations for inference about the unknown parameters from both distributions, the threshold inc1uded. Prior distribution for the parameters are indirectly obtained through experts quantiles elicitation. Posterior inference is available through Markov Chain Monte Carlo (MCMC) methods. Simulations are carried out in order to analyze the performance of our proposed mode1 under a wide range of scenarios. Those scenarios approximate realistic situations found in the literature. We also apply the proposed model to a real dataset, Nasdaq 100, an index of the financiai market that presents many extreme events. Important issues such as predictive analysis and model selection are considered along with possible modeling extensions.