977 resultados para Fixed-effect estimator


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In this paper, we propose a two-step estimator for panel data models in which a binary covariate is endogenous. In the first stage, a random-effects probit model is estimated, having the endogenous variable as the left-hand side variable. Correction terms are then constructed and included in the main regression.

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The FE ('fixed effects') estimator of technical inefficiency performs poorly when N ('number of firms') is large and T ('number of time observations') is small. We propose estimators of both the firm effects and the inefficiencies, which have small sample gains compared to the traditional FE estimator. The estimators are based on nonparametric kernel regression of unordered variables, which includes the FE estimator as a special case. In terms of global conditional MSE ('mean square error') criterions, it is proved that there are kernel estimators which are efficient to the FE estimators of firm effects and inefficiencies, in finite samples. Monte Carlo simulations supports our theoretical findings and in an empirical example it is shown how the traditional FE estimator and the proposed kernel FE estimator lead to very different conclusions about inefficiency of Indonesian rice farmers.

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This paper examines whether European Monetary Union (EMU) countries share fairly the effect of their membership in Eurozone (EZ) or whether are winners and losers in this ''Euro-game''. By using panel data of 27 European Union (EU) Member States for the period 2001-2012 in the context of a gravity model, we focus on estimating the Euro’s effect on bilateral trade and we detect whether this effect differs across the Member States of EZ. Two estimation methods are applied: Pooled OLS estimator and Fixed Effects estimator. The empirical results come to the conclusion that the individual country effects differ and are statistically significant, indicating that EMU’s effect on trade differs across the Member States of EZ. The overall effect of the Euro is statistically insignificant, regardless the estimation method, demonstrating that the common European currency may have no effect on bilateral trade.

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We study the issue of income convergence across countries and regions witha Bayesian estimator which allows us to use information in an efficient andflexible way. We argue that the very slow convergence rates to a commonlevel of per-capita income found, e.g., by Barro and Xavier Sala-i-Martin,is due to a 'fixed effect bias' that their cross-sectional analysisintroduces in the results. Our approach permits the estimation of differentconvergence rates to different steady states for each cross sectional unit.When this diversity is allowed, we find that convergence of each unit to(its own) steady state income level is much faster than previously estimatedbut that cross sectional differences persist: inequalities will only bereduced by a small amount by the passage of time. The cross countrydistribution of the steady state is largely explained by the cross countrydistribution of initial conditions.

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We consider method of moment fixed effects (FE) estimation of technical inefficiency. When N, the number of cross sectional observations, is large it ispossible to obtain consistent central moments of the population distribution of the inefficiencies. It is well-known that the traditional FE estimator may be seriously upward biased when N is large and T, the number of time observations, is small. Based on the second central moment and a single parameter distributional assumption on the inefficiencies, we obtain unbiased technical inefficiencies in large N settings. The proposed methodology bridges traditional FE and maximum likelihood estimation – bias is reduced without the random effects assumption.

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This paper measures the degree of segmentation in the brazilian labor market. Controlling for observable and unobservable characteristics, workers earn more in the formal sector, which supports the segmentation hypothesis. We break down the degree of segmentation by socio-economic attributes to identify the groups where this phenomenon is more prevalent. We investigate the robustness of our findings to the inclusion of self-employed individuals, and apply a two-stage panel probit model using the self-selection correction strategy to investigate a potential weakness of the fixed-effects estimator

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This paper analyses the impact of the decentralization in educational system that is taking place in Brazil in the last decade, as a result of several laws that encourage municipalities to invest in fundamental education. The proficiency tests undertaken by the government allows to follow some public schools in two points in time. Therefore we were able to create an experimental group with the schools that were under state system in the SAEB exam and have migrated to the municipality system by the time of Prova Brasil and a control group with the schools that were under the state system between the two exams and compare the difference in their results using a fixed effect panel data analysis. The difference in difference estimator indicates that there is no significant change in the performance of the students.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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The use of group-randomized trials is particularly widespread in the evaluation of health care, educational, and screening strategies. Group-randomized trials represent a subset of a larger class of designs often labeled nested, hierarchical, or multilevel and are characterized by the randomization of intact social units or groups, rather than individuals. The application of random effects models to group-randomized trials requires the specification of fixed and random components of the model. The underlying assumption is usually that these random components are normally distributed. This research is intended to determine if the Type I error rate and power are affected when the assumption of normality for the random component representing the group effect is violated. ^ In this study, simulated data are used to examine the Type I error rate, power, bias and mean squared error of the estimates of the fixed effect and the observed intraclass correlation coefficient (ICC) when the random component representing the group effect possess distributions with non-normal characteristics, such as heavy tails or severe skewness. The simulated data are generated with various characteristics (e.g. number of schools per condition, number of students per school, and several within school ICCs) observed in most small, school-based, group-randomized trials. The analysis is carried out using SAS PROC MIXED, Version 6.12, with random effects specified in a random statement and restricted maximum likelihood (REML) estimation specified. The results from the non-normally distributed data are compared to the results obtained from the analysis of data with similar design characteristics but normally distributed random effects. ^ The results suggest that the violation of the normality assumption for the group component by a skewed or heavy-tailed distribution does not appear to influence the estimation of the fixed effect, Type I error, and power. Negative biases were detected when estimating the sample ICC and dramatically increased in magnitude as the true ICC increased. These biases were not as pronounced when the true ICC was within the range observed in most group-randomized trials (i.e. 0.00 to 0.05). The normally distributed group effect also resulted in bias ICC estimates when the true ICC was greater than 0.05. However, this may be a result of higher correlation within the data. ^

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My dissertation has three chapters which develop and apply microeconometric tech- niques to empirically relevant problems. All the chapters examines the robustness issues (e.g., measurement error and model misspecification) in the econometric anal- ysis. The first chapter studies the identifying power of an instrumental variable in the nonparametric heterogeneous treatment effect framework when a binary treat- ment variable is mismeasured and endogenous. I characterize the sharp identified set for the local average treatment effect under the following two assumptions: (1) the exclusion restriction of an instrument and (2) deterministic monotonicity of the true treatment variable in the instrument. The identification strategy allows for general measurement error. Notably, (i) the measurement error is nonclassical, (ii) it can be endogenous, and (iii) no assumptions are imposed on the marginal distribution of the measurement error, so that I do not need to assume the accuracy of the measure- ment. Based on the partial identification result, I provide a consistent confidence interval for the local average treatment effect with uniformly valid size control. I also show that the identification strategy can incorporate repeated measurements to narrow the identified set, even if the repeated measurements themselves are endoge- nous. Using the the National Longitudinal Study of the High School Class of 1972, I demonstrate that my new methodology can produce nontrivial bounds for the return to college attendance when attendance is mismeasured and endogenous.

The second chapter, which is a part of a coauthored project with Federico Bugni, considers the problem of inference in dynamic discrete choice problems when the structural model is locally misspecified. We consider two popular classes of estimators for dynamic discrete choice models: K-step maximum likelihood estimators (K-ML) and K-step minimum distance estimators (K-MD), where K denotes the number of policy iterations employed in the estimation problem. These estimator classes include popular estimators such as Rust (1987)’s nested fixed point estimator, Hotz and Miller (1993)’s conditional choice probability estimator, Aguirregabiria and Mira (2002)’s nested algorithm estimator, and Pesendorfer and Schmidt-Dengler (2008)’s least squares estimator. We derive and compare the asymptotic distributions of K- ML and K-MD estimators when the model is arbitrarily locally misspecified and we obtain three main results. In the absence of misspecification, Aguirregabiria and Mira (2002) show that all K-ML estimators are asymptotically equivalent regardless of the choice of K. Our first result shows that this finding extends to a locally misspecified model, regardless of the degree of local misspecification. As a second result, we show that an analogous result holds for all K-MD estimators, i.e., all K- MD estimator are asymptotically equivalent regardless of the choice of K. Our third and final result is to compare K-MD and K-ML estimators in terms of asymptotic mean squared error. Under local misspecification, the optimally weighted K-MD estimator depends on the unknown asymptotic bias and is no longer feasible. In turn, feasible K-MD estimators could have an asymptotic mean squared error that is higher or lower than that of the K-ML estimators. To demonstrate the relevance of our asymptotic analysis, we illustrate our findings using in a simulation exercise based on a misspecified version of Rust (1987) bus engine problem.

The last chapter investigates the causal effect of the Omnibus Budget Reconcil- iation Act of 1993, which caused the biggest change to the EITC in its history, on unemployment and labor force participation among single mothers. Unemployment and labor force participation are difficult to define for a few reasons, for example, be- cause of marginally attached workers. Instead of searching for the unique definition for each of these two concepts, this chapter bounds unemployment and labor force participation by observable variables and, as a result, considers various competing definitions of these two concepts simultaneously. This bounding strategy leads to partial identification of the treatment effect. The inference results depend on the construction of the bounds, but they imply positive effect on labor force participa- tion and negligible effect on unemployment. The results imply that the difference- in-difference result based on the BLS definition of unemployment can be misleading

due to misclassification of unemployment.

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As the population of urban poor living in slums increases, governments are trying to relocate people into government-provided free housing. Slum redevelopment affects every part of a household’s livelihood, but most importantly the health and wellbeing of younger generations. This paper investigates the effect of slum redevelopment schemes on child stunting levels. Data was collected in forty-one buildings under the slum-redevelopment program in Mumbai. The study demonstrates through a fixed effect regression analysis that an additional year of living in the building is associated with an increase in the height-for-age Z-score by 0.124 standard deviations. Possible explanations include an improvement in the overall hygienic environment, sanitation conditions, indoor air pollution, and access to health and water facilities. However, anecdotal evidence suggests that water contamination, loss of livelihood and increased expenses could worsen health outcomes for residents. This study prompts more research on the health effects of slum redevelopment projects, which are becoming increasingly common in the rapidly urbanizing developing world.

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One of the most disputable matters in the theory of finance has been the theory of capital structure. The seminal contributions of Modigliani and Miller (1958, 1963) gave rise to a multitude of studies and debates. Since the initial spark, the financial literature has offered two competing theories of financing decision: the trade-off theory and the pecking order theory. The trade-off theory suggests that firms have an optimal capital structure balancing the benefits and costs of debt. The pecking order theory approaches the firm capital structure from information asymmetry perspective and assumes a hierarchy of financing, with firms using first internal funds, followed by debt and as a last resort equity. This thesis analyses the trade-off and pecking order theories and their predictions on a panel data consisting 78 Finnish firms listed on the OMX Helsinki stock exchange. Estimations are performed for the period 2003–2012. The data is collected from Datastream system and consists of financial statement data. A number of capital structure characteristics are identified: firm size, profitability, firm growth opportunities, risk, asset tangibility and taxes, speed of adjustment and financial deficit. A regression analysis is used to examine the effects of the firm characteristics on capitals structure. The regression models were formed based on the relevant theories. The general capital structure model is estimated with fixed effects estimator. Additionally, dynamic models play an important role in several areas of corporate finance, but with the combination of fixed effects and lagged dependent variables the model estimation is more complicated. A dynamic partial adjustment model is estimated using Arellano and Bond (1991) first-differencing generalized method of moments, the ordinary least squares and fixed effects estimators. The results for Finnish listed firms show support for the predictions of profitability, firm size and non-debt tax shields. However, no conclusive support for the pecking-order theory is found. However, the effect of pecking order cannot be fully ignored and it is concluded that instead of being substitutes the trade-off and pecking order theory appear to complement each other. For the partial adjustment model the results show that Finnish listed firms adjust towards their target capital structure with a speed of 29% a year using book debt ratio.

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Root canal treatment is a frequently performed dental procedure and is carried out on teeth in which irreversible pulpitis has led to necrosis of the dental pulp. Removal of the necrotic tissue remnants and cleaning and shaping of the root canal are important phases of root canal treatment. Treatment options include the use of hand and rotary instruments and methods using ultrasonic or sonic equipment. OBJECTIVES: The objectives of this systematic review of randomized controlled trials were to determine the relative clinical effectiveness of hand instrumentation versus ultrasonic instrumentation alone or in conjunction with hand instrumentation for orthograde root canal treatment of permanent teeth. MATERIAL AND METHODS: The search strategy retrieved 226 references from the Cochrane Oral Health Group Trials Register (7), the Cochrane Central Register of Controlled Trials (CENTRAL) (12), MEDLINE (192), EMBASE (8) and LILACS (7). No language restriction was applied. The last electronic search was conducted on December 13th, 2007. Screening of eligible studies was conducted in duplicate and independently. RESULTS: Results were to be expressed as fixed-effect or random-effects models using mean differences for continuous outcomes and risk ratios for dichotomous outcomes with 95% confdence intervals. Heterogeneity was to be investigated including both clinical and methodological factors. No eligible randomized controlled trials were identifed. CONCLUSIONS: This review illustrates the current lack of published or ongoing randomized controlled trials and the unavailability of high-level evidence based on clinically relevant outcomes referring to the effectiveness of ultrasonic instrumentation used alone or as an adjunct to hand instrumentation for orthograde root canal treatment. In the absence of reliable research-based evidence, clinicians should base their decisions on clinical experience, individual circumstances and in conjunction with patients' preferences where appropriate. Future randomized controlled trials might focus more closely on evaluating the effectiveness of combinations of these interventions with an emphasis on not only clinically relevant, but also patient-centered outcomes.

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Com objetivo de estimar parâmetros genéticos e estudar a utilização de diferentes efeitos em avaliações genéticas para idade ao primeiro parto (IPP) por diferentes modelos, foram utilizados registros de IPP de animais da raça Nelore, nascidos entre os anos de 1990 e 2005. Foram considerados os seguintes modelos (M): M1, incluindo o efeito fixo de GC1 (constituído pelos animais nascidos na mesma fazenda e ano), além da covariável, peso aos 365 dias de idade (efeito linear e quadrático), totalizando 24.263 registros de IPP; M2, considerando os efeitos fixos de GC1, ano e estação de parição, totalizando 59.792 registros de IPP e M3, incluindo os efeitos fixos de GC2 (agrupando os animais nascidos na mesma fazenda, ano e que conceberam no mesmo manejo reprodutivo), ano e estação de parição, totalizando 59.792 registros de IPP. As estimativas dos componentes de variância e herdabilidade e os valores genéticos (VG) foram obtidos pelo método da máxima verossimilhança restrita, com a inclusão da matriz de parentesco disponível. As diferenças esperadas na progênie (DEPs) foram obtidas dividindo os VG por dois. Após a obtenção desses resultados, foram realizadas correlações entre os VG e o ranqueamento das DEPs dos reprodutores para IPP, utilizando-se o procedimento PROC CORR (SAS, 2003). Ao se considerar o ano e a estação de parto nos modelos de análise (M2 e M3), esses produziram um maior R², indicando que tais modelos conseguiram explicar, em maior grau, as diferenças existentes entre os animais para IPP. As herdabilidades estimadas foram de baixa magnitude (0,14 e 0,15). As correlações entre os VG obtidas por diferentes modelos foram 0,73 (M1 x M2); 0,91 (M2 x M3) e 0,66 (M1 x M3).

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This paper analyses the presence of financial constraint in the investment decisions of 367 Brazilian firms from 1997 to 2004, using a Bayesian econometric model with group-varying parameters. The motivation for this paper is the use of clustering techniques to group firms in a totally endogenous form. In order to classify the firms we used a hybrid clustering method, that is, hierarchical and non-hierarchical clustering techniques jointly. To estimate the parameters a Bayesian approach was considered. Prior distributions were assumed for the parameters, classifying the model in random or fixed effects. Ordinate predictive density criterion was used to select the model providing a better prediction. We tested thirty models and the better prediction considers the presence of 2 groups in the sample, assuming the fixed effect model with a Student t distribution with 20 degrees of freedom for the error. The results indicate robustness in the identification of financial constraint when the firms are classified by the clustering techniques. (C) 2010 Elsevier B.V. All rights reserved.