Essays On Dynamic Panel Data Models With Interactive Effects
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Essays On Dynamic Panel Data Models With Interactive Effects
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Author : Yan-Ting Chen
language : en
Publisher:
Release Date : 2021
Essays On Dynamic Panel Data Models With Interactive Effects written by Yan-Ting Chen 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.
Essays In Panel Data Modelling
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Author : Artūras Juodis
language : en
Publisher:
Release Date : 2015
Essays In Panel Data Modelling written by Artūras Juodis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
"Panel data are repeated observations on the same cross section unit, typically of individuals or firms (in microeconomic applications), observed for several time periods. The use of panel data has been increasingly popular in empirical macroeconomic and (especially) microeconomic studies and there are several reasons behind the success story. This thesis analyses the properties of the estimation techniques for panel data models with additive and multiplicative error structures. First, this thesis discusses the relative merits of the maximum likelihood estimators in dynamic panel data models. Second, it provides an in-depth analysis of genuine and pseudo panel data models with unobserved interactive effects."--Samenvatting auteur.
Essays On Spatial Panel Data Models With Common Factors
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Author : Wei Shi
language : en
Publisher:
Release Date : 2016
Essays On Spatial Panel Data Models With Common Factors written by Wei Shi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
My dissertation research addresses issues in spatial panel data models, which study the interactions of economic units across space and time. Individuals interact with their neighbors and the outcomes are interdependent. The strength of the interaction depends on the distance between the individuals, which can be based on geography or constructed from economic theory. Accounting for spatial interactions allows one to quantify both the direct effect of a variable and its indirect effect through impacting neighbors. However, two issues often arise. First, spatial dependence can be alternatively generated from common unobserved factors (e.g. economy-wide shocks) where neighbors have similar responses. Second, the distance between economic units can be endogenous, and this will in fact be the case if the distance is constructed from variables that correlate with disturbances in the outcomes. The first chapter studies the estimation of a dynamic spatial panel data model with interactive individual and time effects with large n and T. The model has a rich spatial structure including contemporaneous spatial interaction and spatial heterogeneity. Dynamic features include individual time lag and spatial diffusion. In a standard two way fixed effects panel regression model, the unobservables contain an individual specific but time invariant component, and a component that is time variant but common across individuals. We generalize this model by allowing the interaction between time effects and individual effects. This chapter provides a tool for empirical researchers to guard against attributing correlated responses to common time effects as spatial effects. The interactive effects are treated as parameters, so as to allow correlations between the interactive effects and the regressors. We consider a quasi-maximum likelihood estimation and show estimator consistency and characterize its asymptotic distribution. The Monte Carlo experiment shows that the estimator performs well and the proposed bias correction is effective. The second chapter proposes a unified approach to model endogenous spatial dependences while accounting for common factors. The spatial weights matrices are constructed from variables that may correlate with the disturbances in the outcomes. We make minimal assumptions on the distributions of the factors and follow a fixed effects approach. We provide conditions under which the quasi-maximum likelihood estimator is consistent and asymptotically normal, under the asymptotics where both the cross section and time dimensions become large. The limiting distribution is normal but may not be centered for the estimates of the spatial interaction coefficient and the variances. An analytical bias correction is proposed to improve the inference. The Monte Carlo simulations demonstrate good finite sample properties of the bias corrected estimator. We illustrate the empirical relevance of the theory by applying the method to analyze the effect of house price dynamics on reverse mortgage origination rates.
Three Essays On Panel Data Models With Interactive And Unobserved Effects
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Author : Nicholas Lynn Brown
language : en
Publisher:
Release Date : 2022
Three Essays On Panel Data Models With Interactive And Unobserved Effects written by Nicholas Lynn Brown and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Electronic dissertations categories.
Chapter 1: More Efficient Estimation of Multiplicative Panel Data Models in the Presence of Serial Correlation (with Jeffrey Wooldridge)We provide a systematic approach in obtaining an estimator asymptotically more efficient than the popular fixed effects Poisson (FEP) estimator for panel data models with multiplicative heterogeneity in the conditional mean. In particular, we derive the optimal instrumental variables under appealing `working' second moment assumptions that allow underdispersion, overdispersion, and general patterns of serial correlation. Because parameters in the optimal instruments must be estimated, we argue for combining our new moment conditions with those that define the FEP estimator to obtain a generalized method of moments (GMM) estimator no less efficient than the FEP estimator and the estimator using the new instruments. A simulation study shows that the GMM estimator behaves well in terms of bias, and it often delivers nontrivial efficiency gains -- even when the working second-moment assumptions fail.Chapter 2: Information equivalence among transformations of semiparametric nonlinear panel data modelsI consider transformations of nonlinear semiparametric mean functions which yield moment conditions for estimation. Such transformations are said to be information equivalent if they yield the same asymptotic efficiency bound. I first derive a unified theory of algebraic equivalence for moment conditions created by a given linear transformation. The main equivalence result states that under standard regularity conditions, transformations which create conditional moment restrictions in a given empirical setting need only to have an equal rank to reach the same efficiency bound. Example applications are considered, including nonlinear models with multiplicative heterogeneity and linear models with arbitrary unobserved factor structures.Chapter 3: Moment-based Estimation of Linear Panel Data Models with Factor-augmented ErrorsI consider linear panel data models with unobserved factor structures when the number of time periods is small relative to the number of cross-sectional units. I examine two popular methods of estimation: the first eliminates the factors with a parameterized quasi-long-differencing (QLD) transformation. The other, referred to as common correlated effects (CCE), uses the cross-sectional averages of the independent and response variables to project out the space spanned by the factors. I show that the classical CCE assumptions imply unused moment conditions which can be exploited by the QLD transformation to derive new linear estimators which weaken identifying assumptions and have desirable theoretical properties. I prove asymptotic normality of the linear QLD estimators under a heterogeneous slope model which allows for a tradeoff between identifying conditions. These estimators do not require the number of cross-sectional variables to be less than T-1, a strong restriction in fixed-$T$ CCE analysis. Finally, I investigate the effects of per-student expenditure on standardized test performance using data from the state of Michigan.
Essays In Honor Of Joon Y Park
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Author : Yoosoon Chang
language : en
Publisher: Emerald Group Publishing
Release Date : 2023-04-24
Essays In Honor Of Joon Y Park written by Yoosoon Chang and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-24 with Business & Economics categories.
Volumes 45a and 45b of Advances in Econometrics honor Professor Joon Y. Park, who has made numerous and substantive contributions to the field of econometrics over a career spanning four decades since the 1980s and counting.
Panel Data
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Author : Badi H. Baltagi
language : en
Publisher: Physica
Release Date : 2004-04-16
Panel Data written by Badi H. Baltagi and has been published by Physica this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-04-16 with Business & Economics categories.
The present book is a collection of panel data papers, both theoretical and applied. Theoretical topics include methodology papers on panel data probit models, treatment models, error component models with an ARMA process on the time specific effects, asymptotic tests for poolability and their bootstrapped versions, confidence intervals for a doubly heteroskedastic stochastic production frontiers, estimation of semiparametric dynamic panel data models and a review of survey attrition and nonresponse in the European Community Household Panel. Applications include as different topics as e.g. the impact of uncertainty on UK investment, a Tobin-q investment model using US firm data, cost efficiency of Spanish banks, immigrant integration in Canada, the dynamics of individual health in the UK, the relation between inflation and growth among OECD and APEC countries, technical efficiency of cereal farms in England, and employment effects of education for disabled workers in Norway.
The Econometrics Of Panel Data
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Author : Lászlo Mátyás
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-04-06
The Econometrics Of Panel Data written by Lászlo Mátyás and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-06 with Business & Economics categories.
This restructured, updated Third Edition provides a general overview of the econometrics of panel data, from both theoretical and applied viewpoints. Readers discover how econometric tools are used to study organizational and household behaviors as well as other macroeconomic phenomena such as economic growth. The book contains sixteen entirely new chapters; all other chapters have been revised to account for recent developments. With contributions from well known specialists in the field, this handbook is a standard reference for all those involved in the use of panel data in econometrics.
Essays In Honor Of Aman Ullah
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Author : R. Carter Hill
language : en
Publisher: Emerald Group Publishing
Release Date : 2016-06-29
Essays In Honor Of Aman Ullah written by R. Carter Hill and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-29 with Business & Economics categories.
Volume 36 of Advances in Econometrics recognizes Aman Ullah's significant contributions in many areas of econometrics and celebrates his long productive career.
Essays In Dynamic Panel Data Models And Labor Supply
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Author : Kolobadia Ada Nayihouba
language : en
Publisher:
Release Date : 2019
Essays In Dynamic Panel Data Models And Labor Supply written by Kolobadia Ada Nayihouba and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
This thesis is organized in three chapters. The first two chapters propose a regularization approach to the estimation of two estimators of the dynamic panel data model : the Generalized Method of Moment (GMM) estimator and the Limited Information Maximum Likelihood (LIML) estimator. The last chapter of the thesis is an application of regularization to the estimation of labor supply elasticities using pseudo panel data models. In a dynamic panel data model, the number of moment conditions increases rapidly with the time dimension, resulting in a large dimensional covariance matrix of the instruments. Inverting this large dimensional matrix to compute the estimator leads to poor finite sample properties. To address this issue, we propose a regularization approach to the estimation of such models where a generalized inverse of the covariance matrix of the intruments is used instead of its usual inverse. Three regularization schemes are used : Principal components, Tikhonov which is based on Ridge regression (also called Bayesian shrinkage) and finally Landweber Fridman which is an iterative method. All these methods involve a regularization parameter which is similar to the smoothing parameter in nonparametric regressions. The finite sample properties of the regularized estimator depends on this parameter which needs to be selected between many potential values. In the first chapter (co-authored with Marine Carrasco), we propose the regularized GMM estimator of the dynamic panel data models. Under double asymptotics, we show that our regularized estimators are consistent and asymptotically normal provided that the regularization parameter goes to zero slower than the sample size goes to infinity. We derive a data driven selection of the regularization parameter based on an approximation of the higher-order Mean Square Error and show its optimality. The simulations confirm that regularization improves the properties of the usual GMM estimator. As empirical application, we investigate the effect of financial development on economic growth. In the second chapter (co-authored with Marine Carrasco), we propose the regularized LIML estimator of the dynamic panel data model. The LIML estimator is known to have better small sample properties than the GMM estimator but its implementation becomes problematic when the time dimension of the panel becomes large. We derive the asymptotic properties of the regularized LIML under double asymptotics. A data-driven procedure to select the parameter of regularization is proposed. The good performances of the regularized LIML estimator over the usual (not regularized) LIML estimator, the usual GMM estimator and the regularized GMM estimator are confirmed by the simulations. In the last chapter, I consider the estimation of the labor supply elasticities of Canadian men through a regularization approach. Unobserved heterogeneity and measurement errors on wage and income variables are known to cause endogeneity issues in the estimation of labor supply models. A popular solution to the endogeneity issue is to group data in categories based on observable characteristics and compute the weighted least squares at the group level. This grouping estimator has been proved to be equivalent to instrumental variables (IV) estimator on the individual level data using group dummies as intruments. Hence, in presence of large number of groups, the grouping estimator exhibites a small bias similar to the one of the IV estimator in presence of many instruments. I take advantage of the correspondance between grouping estimators and the IV estimator to propose a regularization approach to the estimation of the model. Using this approach leads to wage elasticities that are substantially different from those obtained through grouping estimators.
Interactive Effects Panel Data Models With General Factors And Regressors
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Author : Bin Peng
language : en
Publisher:
Release Date : 2021
Interactive Effects Panel Data Models With General Factors And Regressors written by Bin Peng 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.