Three Essays On Panel Data Models In Econometrics
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Three Essays On Panel Data Models In Econometrics
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Author : Lina Lu
language : en
Publisher:
Release Date : 2017
Three Essays On Panel Data Models In Econometrics written by Lina Lu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Chapter 3 also considers the extension to an approximate constrained factor model where the idiosyncratic errors are allowed to be weakly dependent processes.
Three Essays On Large Panel Data Models With Cross Sectional Dependence
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Author : Yonghui Zhang
language : en
Publisher:
Release Date : 2013
Three Essays On Large Panel Data Models With Cross Sectional Dependence written by Yonghui Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Econometrics categories.
"My dissertation consists of three essays which contribute new theoretical results to large panel data models with cross-sectional dependence. These essays try to answer or partially answer some prominent questions such as how to detect the presence of cross-sectional dependence and how to capture the latent structure of cross-sectional dependence and estimate parameters efficiently by removing its effects".-- Author's abstract.
Three Essays On Nonlinear Panel Data Models And Quantile Regression Analysis
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Author : Iván Fernández-Val
language : en
Publisher:
Release Date : 2005
Three Essays On Nonlinear Panel Data Models And Quantile Regression Analysis written by Iván Fernández-Val and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
This dissertation is a collection of three independent essays in theoretical and applied econometrics, organized in the form of three chapters. In the first two chapters, I investigate the properties of parametric and semiparametric fixed effects estimators for nonlinear panel data models. The first chapter focuses on fixed effects maximum likelihood estimators for binary choice models, such as probit, logit, and linear probability model. These models are widely used in economics to analyze decisions such as labor force participation, union membership, migration, purchase of durable goods, marital status, or fertility. The second chapter looks at generalized method of moments estimation in panel data models with individual-specific parameters. An important example of these models is a random coefficients linear model with endogenous regressors. The third chapter (co-authored with Joshua Angrist and Victor Chernozhukov) studies the interpretation of quantile regression estimators when the linear model for the underlying conditional quantile function is possibly misspecified.
Three Essays On Dynamic Panel Data Estimation
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Author :
language : en
Publisher:
Release Date : 2004
Three Essays On Dynamic Panel Data Estimation written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
This dissertation consists of three essays, first two of which consider a new estimation method of dynamic panel data models and the last one considers an application of these models. The first essay (Chapter 1) offers empirical likelihood (EL) estimation of dynamic panel data models, which provide great flexibility to empirical researchers. EL estimation method is shown to have great advantages in usual settings, however little is known on the relative merits of these estimators in panel data models. With this essay, we try to fill that gap by establishing the asymptotic properties of the EL estimator for a dynamic panel model with individual effects when both the time and the cross-section dimensions tend to infinity. We give the conditions under which this estimator is consistent and asymptotically normal. In the second essay (Chapter 2), via a Monte Carlo study, we assess the relative finite sample performances of EL, generalized method of moments, and limited information maximum likelihood estimators for an autoregressive panel data model when there are many moment conditions. We also extend our results to the many weak moments settings. Our results suggest that when the overall performances are concerned, in terms of median, interquartile range and median absolute error of the estimators, in both strong and weak moments settings, EL is more reliable. In the final essay (Chapter 3) we consider an application of dynamic panel data models to examine the determinants of the allocation of state highway funds using panel data for North Carolina's 100 counties for the years 1990 to 2005. We make two main contributions with this essay. First, although there have been numerous studies of highway funding at the state level, to our knowledge, there is no analysis at the sub-state or county levels. Second, by using dynamic panel data models and sophisticated methods to estimate them, we account for any potential persistence in the process of adjustment toward an equilibri.
Three Essays In Econometrics
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Author : Panutat Satchachai
language : en
Publisher:
Release Date : 2009
Three Essays In Econometrics written by Panutat Satchachai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Econometrics categories.
Three Essays In Econometrics
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Author : Shu Shen
language : en
Publisher:
Release Date : 2014
Three Essays In Econometrics written by Shu Shen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
My dissertation includes three essays that examine or relax classical restrictive assumptions used in econometrics estimation methods. The first chapter proposes methods for examining how a response variable is influenced by a covariate. Rather than focusing on the conditional mean I consider a test of whether a covariate has an effect on the entire conditional distribution of the response variable given the covariate and other conditioning variables. This type of analysis is useful in situations where the econometrician or policy maker is interested in knowing whether a variable or policy would improve the distribution of the response outcomes in a stochastic dominance sense. The response variable is assumed to be continuous, while both discrete and continuous covariate cases are considered. I derive the asymptotic distribution of the test statistics and show that they have simple known asymptotic distributions under the null by using and extending conditional empirical process results given by Horvath and Yandell (1988). Monte Carlo experiments are conducted, and the tests are shown to have good small sample behavior. The tests are applied to a study on father's labor supply. The second chapter is based on previous joint work with Jason Abrevaya. It considers estimation of censored panel-data models with individual-specific slope heterogeneity. The slope heterogeneity may be random (random-slopes model) or related to covariates (correlated-random-slopes model). Maximum likelihood and censored least-absolute deviations estimators are proposed for both models. Specification tests are provided to test the slope-heterogeneity models against nested alternatives. The proposed estimators and tests are used for an empirical study of Dutch household portfolio choice. Strong evidence of correlated random slopes for the age variables is found, indicating that the age profile of portfolio adjustment varies significantly with other household characteristics. The third chapter proposes specification tests in models with endogenous covariates. In empirical studies, econometricians often have little information on the functional form of the structural model, regardless of whether covariates in model are exogenous or endogenous. In this chapter, I propose tests for restricted structural model specifications with endogenous covariates against the fully nonparametric alternative. The restricted model specifications include the nonparametric specification with a restricted set of covariates, the semiparametric single index specification and the parametric linear specification. Test statistics are "leave-one-out" type kernel U-statistic as used in Fan and Lee (1996). They are constructed using the idea of the control function approach. Monte Carlo results are provided and tests are shown to have reasonable small sample behavior.
Three Essays On Econometrics
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Author : Myungsup Kim
language : en
Publisher:
Release Date : 2005
Three Essays On Econometrics written by Myungsup Kim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Bootstrap (Statistics) categories.
Essays On The Econometrics Of Data Quality
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Author : Elan Segarra
language : en
Publisher:
Release Date : 2021
Essays On The Econometrics Of Data Quality written by Elan Segarra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This dissertation consists of three essays which explore scenarios where data quality issues interfere with the goals of empirical research. These situations motivate closer analysis of existing econometric methods and even the provision of new methods to account for the deficiencies present in the data. In all three cases the work presented aims to provide clarity and advice to aid researchers so they may accomplish their primary objective while simultaneously managing the shortcomings in their data. In the first chapter I consider survival analysis when durations are subject to mismeasurement due to record linkage errors that manifest during data collection and processing. Panel data have a long history of use across the social sciences; however, they can be imperfect representations of reality when record linkage methods are employed during their creation. When conducting survival analysis (e.g. firm death, mortality, or emigration), missed linkages induce error in the observed lifetime durations, and thus inconsistency in standard survival estimators. New methods are developed which restore consistency of the estimators of parameters without correcting the linkages. This work makes three distinct theoretical contributions under increasingly relaxed assumptions. First, under the strong assumption of a known independent linkage error process I show that the marginal distribution of time to death is nonparametrically identified from linkage error induced durations. Second, when data on start and end dates are introduced, I show that nonparametric point identification of the joint distribution of lifetimes and linkage error is typically achieved. Third, when no restriction is placed on the dependence structure, I apply partial identification methods to derive sharp informative bounds on the marginal distribution of lifetimes. New estimators and inference methods are introduced across all scenarios and their validity is established formally. The methods are applied to longitudinal business data (where linkage error occurs due to establishment relocation), and show that establishment death rates in the first 3 years can be overestimated by as much as 10 percentage points with naive methods, while those proposed here are able to recover true rates of survival from mis-linked data. The second chapter investigates the estimation of discrete choice models when market size is unobserved or mismeasured. Estimates of elasticities are a common output of interest in discrete choice models, however they can besignificantly biased when the population size is misspecified. In this chapter we decompose the bias in elasticity estimates in the logit model into a direct effect and an indirect effect coming from bias in the structural parameter estimates. Since these effects can go in opposite directions addressing bias from the indirect channel, via market fixed effects, will have an indeterminate effect on the total bias in the elasticity. We provide a complete characterization of when including market fixed effects will mitigate versus exacerbate elasticity bias. Our results reveal that for own characteristic elasticities products with small shares will typically benefit most from market fixed effects while the benefit (or detriment) for cross characteristic elasticities is independent of share. The third chapter explores instrumental variables estimation in the presence of outcome attrition and presents a novel estimator to handle this missingness. Instrumental variables (IV) methods are a ubiquitous tool for estimating causal effects. However, when data are subject to missingness the exclusion restriction can be violated leading to significant bias in IV estimators. This work proposes a new method, termed the missingness instrumental variables (MIV) estimator, to recover causal effects in the presence of outcome attrition. The method leverages statistical independences to replace the infeasible moments of the IV estimator with moments that can be estimated using data subject to missingness. Just like IV methods with complete data, MIV is able to estimate many causal effects of interest including average treatment effects, local average treatment effects, and marginal treatment effects. The method is compared with inverse probability weighting methods and multiple imputation methods, and Monte Carlo simulations highlight how MIV fares better than alternative methods when positivity is violated or under misspecification of error distributions.
Mostly Panel Econometrics
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Author : Ovidijus Stauskas
language : en
Publisher:
Release Date : 2022
Mostly Panel Econometrics written by Ovidijus Stauskas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Bootstrap (Statistics) categories.
This thesis consists of five chapters which focus on panel data theory. Four of them analyze explicit panel data models and one chapter deals with time series forecasting model, where external panel data help us estimate unobserved explanatory variables. The broad topics discussed in the thesis include i) simplification of distribution of a statistical test under double asymptotics, ii) elimination of fixed effects and bias correction in dynamic panels, iii) accounting for cross-section dependence and estimation of latent factors when they can be non-stationary and iv) usage of latent factors to improve out-of-sample forecasts and testing competing forecast models. In Chapter I, we re-visit a problem posed by Phillips and Lee (2015, Econometric Reviews). They considered a simple bivariate vector autoregression (VAR), where both series exhibited different degrees of non-stationarity: near unit root and mild explosiveness. While one is interested in testing whether both series are in the lower vicinity of unit root and share the same persistence features, unfortunately, Wald test statistic degenerates under the null. We re-consider this setup in the context of panel data, where we use extra observations from the cross-section to simplify asymptotic distributions in order to obtain chi-square-based inference.??Chapter II looks into very popular factor augmented linear forecast models and tests to evaluate out-of-sample forecasting accuracy. In large macroeconomic datasets, various series tend to co-move together and it is modelled by employing a small number of latent factors (see e.g. Stock and Watson, 1999 and 2002). Instead of using a large number of available variables, researchers reduce the dataset dimension by estimating the driving factors and use those estimates directly. We further explore two tests of equal forecasting accuracy for nested models to investigate if factor augmented model outperforms parsimonious model with known set of variables. Unlike Gonçalves el. al (2017, Journal of Econometrics), where the factors are estimated using Principal Components (PC) under presumably known number of factors, we employ Common Correlated Effects (CCE) estimator which is very user friendly and employs a common thematic block structure of large macro datasets. Factors are estimated as block averages to proxy the common underlying information given by factors.??We continue discussing latent factors in Chapter III and Chapter IV. Here we focus on panel data, where unobserved factors model strong cross-section dependence among the panel units and possible endogeneity within the individual time series. Pesaran (2006, Econometrica) suggested solving these issues by augmenting the regression with cross-section averages of the dependent and independent variables. This is CCE estimator. While very simple, pooled version of CCE (CCEP) is asymptotically biased under homogeneous slopes, unless the number of individuals dominates the length of time series in the panel. Moreover, typically the bias is inestimable and analytic correction is not possible. In Chapter III, we analyze the properties of a simple 'pairs' bootstrap algorithm discussed in Kapetanios (2008, Econometrics Journal) in the context of CCE and develop bootstrap-based bias correction procedure. In Chapter IV, we continue the study of Westerlund (2018, Econometrics Journal), where CCE was extended to non-stationary factors of a very general type. In the latter study, however, only CCEP under homogeneous slopes was examined, but we extend the analysis to heterogeneous slopes and explore the properties of the mean group (CCEMG) estimator in order to further model unobserved heterogeneity.??The thesis concludes with Chapter V, where we re-visit at a classical problem in dynamic panels with fixed effects known as Nickel Bias. De-meaning the data to purge individual-specific effects in dynamic panels makes the model errors correlated, and the bias accumulates if the time dimension is large. On the other hand, if we estimate the fixed effects, we run into incidental parameter problem. Bai (2013, Econometrica) considered the so-called Factor Analytical (FA) estimator, which circumvents these issues by estimating the sample variance of individual effects rather than the effects themselves. In the latter study, panel AR(1) model with autoregressive parameter in the stationary region was explored. We extend this to autoregressive coefficient tending to unity and incidental trends, similarly to Moon and Phillips (2004, Econometrica) in order to account for trending and drifting variables.
Three Essays On Health Econometrics
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Author : Bidisha Mandal
language : en
Publisher:
Release Date : 2007
Three Essays On Health Econometrics written by Bidisha Mandal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
This dissertation incorporates several estimation procedures and modeling techniques to investigate important issues in health economics. All of the essays are tied to the application of econometrics in health related topics, but the techniques used in this research can be applied to many issues in agricultural, environmental and development economics.