Download or read book Three Essays on Panel Data Models in Econometrics written by Lina Lu and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter 3 also considers the extension to an approximate constrained factor model where the idiosyncratic errors are allowed to be weakly dependent processes.
Download or read book Three Essays on Large Panel Data Models with Cross sectional Dependence written by Yonghui Zhang and published by . This book was released on 2013 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.
Download or read book Three Essays in Econometrics written by Panutat Satchachai and published by . This book was released on 2009 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays in Panel Data Econometrics written by Marc Nerlove and published by Cambridge University Press. This book was released on 2005-11-10 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume collects seven classic essays on panel data econometrics, and a cogent essay on the history of the subject.
Download or read book Essays in Panel Data Econometrics Examining Selection Bias and Average Treatment Effects written by Kamyar Nasseh and published by . This book was released on 2007 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Three Essays in Applied Econometrics with Applications to International Trade and Finance written by Patrice Whitely and published by . This book was released on 2007 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Three Essays on High dimensional Model Econometrics written by Zhentao Shi and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Panel Data Analysis using EViews written by I. Gusti Ngurah Agung and published by John Wiley & Sons. This book was released on 2013-12-31 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and accessible guide to panel data analysis using EViews software This book explores the use of EViews software in creating panel data analysis using appropriate empirical models and real datasets. Guidance is given on developing alternative descriptive statistical summaries for evaluation and providing policy analysis based on pool panel data. Various alternative models based on panel data are explored, including univariate general linear models, fixed effect models and causal models, and guidance on the advantages and disadvantages of each one is given. Panel Data Analysis using EViews: Provides step-by-step guidance on how to apply EViews software to panel data analysis using appropriate empirical models and real datasets. Examines a variety of panel data models along with the author’s own empirical findings, demonstrating the advantages and limitations of each model. Presents growth models, time-related effects models, and polynomial models, in addition to the models which are commonly applied for panel data. Includes more than 250 examples divided into three groups of models (stacked, unstacked, and structured panel data), together with notes and comments. Provides guidance on which models not to use in a given scenario, along with advice on viable alternatives. Explores recent new developments in panel data analysis An essential tool for advanced undergraduate or graduate students and applied researchers in finance, econometrics and population studies. Statisticians and data analysts involved with data collected over long time periods will also find this book a useful resource.
Download or read book Three Essays on Econometrics written by Chirok Han and published by . This book was released on 2001 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays on the Econometrics of Data Quality written by Elan Segarra and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Three Essays on Dynamic Panel Data Estimation written by Gunce Eryuruk and published by . This book was released on 2009 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: system GMM estimator, highway spending, dynamic panel data, empirical likelihood estimator.
Download or read book Three Essays on Econometrics written by Wei Siang Wang and published by . This book was released on 2009 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mostly Panel Econometrics written by Ovidijus Stauskas and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Analysis of Panels and Limited Dependent Variable Models written by Cheng Hsiao and published by Cambridge University Press. This book was released on 1999-07-29 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important collection brings together leading econometricians to discuss advances in the areas of the econometrics of panel data. The papers in this collection can be grouped into two categories. The first, which includes chapters by Amemiya, Baltagi, Arellano, Bover and Labeaga, primarily deal with different aspects of limited dependent variables and sample selectivity. The second group of papers, including those by Nerlove, Schmidt and Ahn, Kiviet, Davies and Lahiri, consider issues that arise in the estimation of dyanamic (possibly) heterogeneous panel data models. Overall, the contributors focus on the issues of simplifying complex real-world phenomena into easily generalisable inferences from individual outcomes. As the contributions of G. S. Maddala in the fields of limited dependent variables and panel data were particularly influential, it is a fitting tribute that this volume is dedicated to him.
Download or read book Essays on Panel Data Econometrics written by Ayden Higgins and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Three Essays on Econometrics written by Joonhwan Lee and published by . This book was released on 2014 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis consists of three chapters that cover separate topics in econometrics. The first chapter demonstrate a negative result on the asymptotic sizes of subset Anderson- Rubin tests with weakly identified nuisance parameters and general covariance structure. The result of Guggenberger et al (2012) in case of homoskedasticity is shown to break down when general covariance structure is allowed. I provide a thorough simulation results to show that the break-down of the result can be observed in wide range of parameters that is plausible in empirical applications. The second chapter propose an inference procedure on Quasi-Bayesian estimators accounting for Monte-Carlo numerical errors. Quasi-Bayesian method have been applied to numerous applications to tackle the non-convex shape arises in certain extremum estimations. The method involves drawing finite number of Monte Carlo Markov chains to make inference and thus some degree of numerical error is inevitable. This chapter quantifies the degree of numerical error arising from the finite draws and provides a method to incorporate such errors into the final inference. I show that a sufficient condition for establishing correct numerical standard errors is geometric ergodicity of the MCMC chain. It is also shown that geometric ergodicity is satisfied under Metropolis Hastings chains with quasi-posterior for the whole class of extremum estimators. The third chapter considers fixed effects estimation and inference in nonlinear panel data models with random coefficients and endogenous regressors. The quantities of interest are estimated by cross sectional sample moments of generalized method of moments (GMM) estimators applied separately to the time series of each individual. To deal with the incidental parameter problem introduced by the noise of the within-individual estimators in short panels, we develop bias corrections. These corrections are based on higher-order asymptotic expansions of the GMM estimators and produce improved point and interval estimates in moderately long panels. Under asymptotic sequences where the cross sectional and time series dimensions of the panel pass to infinity at the same rate, the uncorrected estimators have asymptotic biases of the same order as their asymptotic standard deviations. The bias corrections remove the bias without increasing variance.
Download or read book Three Essays on the Estimation and Testing of an Error Component Model written by Qi Li and published by . This book was released on 1991 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt:
