Download or read book Three Essays on Unobserved Heterogeneity in Panel and Network Data Models written by Hualei Shang and published by . This book was released on 2020 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three chapters that study unobserved heterogeneity in panel and network data models. In Chapter 1, I propose a semi-nonparametric panel data model with a latent group structure. I assume that individual parameters are heterogeneous across groups but homogeneous within a group while the group membership is unknown. I first approximate the infinite-dimensional function with a sieve expansion; then, I propose a Classifier-Lasso(C-Lasso) procedure to simultaneously identify the individuals' membership and estimate the group-specific parameters. I show that: (i) the classification exhibits uniform consistency; (ii) C-Lasso and post-Lasso estimators achieve oracle properties so that they are asymptotically equivalent to infeasible estimators as if the group membership is known; and (iii) the estimators are consistent and asymptotically normally distributed. Simulations demonstrate an excellent finite sample performance of this approach in both classification and estimation. In Chapter 2 (joint with Wenyu Zhou), we study a nonparametric additive panel regression model with grouped heterogeneity. The model can be regarded as a natural extension to the heterogeneous panel model studied in Su, Shi, and Phillips (2016). We propose to estimate the nonparametric components using a sieve-approximation-based Classifier-Lasso method. We establish the asymptotic properties of the estimator and show that they enjoy the so-called oracle property. In addition, we present the decision rule for group classification and establish its consistency. Then, a BIC-type information criterion is developed to determine the group pattern of each nonparametric component. We further investigate the finite sample performance of the estimation method and the information criterion through Monte Carlo simulations. Results show that both work well. Finally, we apply the model and the estimation method to study the demand for cigarettes in the United States using panel data of 46 states from 1963 to 1992. In Chapter 3, I study a network sample selection model in which 1) bilateral fixed effects enter the pairwise outcome equation additively; 2) link formation depends on latent variables from both sides nonparametrically. I first propose a four-cycle structure to difference out the fixed effects; next, utilizing the idea proposed in Auerbach (2019), I manage to use the kernel function to control for the selection bias. I then introduce estimators for the parameters of interest and characterize their asymptotic properties.

Download or read book Three Essays on Panel Data Models with Interactive and Unobserved Effects written by Nicholas Lynn Brown and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Essays on Nonlinear Panel Models with Unobserved Heterogeneity written by Robert Martin and published by . This book was released on 2017 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays on Unobserved Heterogeneity written by and published by . This book was released on 2007 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays in Honor of M Hashem Pesaran written by Alexander Chudik and published by Emerald Group Publishing. This book was released on 2022-01-18 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: The collection of chapters in Volume 43 Part B of Advances in Econometrics serves as a tribute to one of the most innovative, influential, and productive econometricians of his generation, Professor M. Hashem Pesaran.

Download or read book Three Essays on Continuous and Discrete Spatial Heterogeneity written by Mauricio Alejandro Sarrias Jeraldo and published by . This book was released on 2016 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continuous and discrete unobserved heterogeneity have been widely used in modeling discrete choice models. In this dissertation I investigate how these modeling strategies can be used to capture and model spatial heterogeneity or locally varying coefficients for different latent structures. In the first chapter, I outline the main advantages and disadvantages of both continuous and discrete spatial modeling strategies. Then I conduct a simulation experiment in order to understand the ability of both approaches to retrieve the true representation of the spatially varying process under small sample size situations. The results show that the data requirement to achieve lower bias in the continuous case is substantial compared with the discrete case. I have also found that, as the number of individuals per spatial unit increases, both models are able to identify the regional-specific estimates. However, the discrete case is able to retrieve the true spatial heterogeneity surface with lower bias and better coverage. In the second chapter, I show the Rchoice package for R that allows estimating models with individual heterogeneity for both cross-sectional and panel data. In particular, the package allows binary, ordinal and count response, as well as continuous and discrete covariates. This chapter is a general description of Rchoice and all functionalities are illustrated using real databases. The last chapter shows how continuous and discrete spatial heterogeneity models can be applied in order to analyze whether monetary subjective well-being eval- uations vary across space using a cross-sectional dataset from Chile. The results show that focusing just on the average estimates of compensating variations veils useful local variation. Moreover, the discrete approach shows some weak superiority over the continuous case in terms of model fit and interpretation.

Download or read book Econometric Analysis of Cross Section and Panel Data second edition written by Jeffrey M. Wooldridge and published by MIT Press. This book was released on 2010-10-01 with total page 1095 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

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 Panel Data Analysis written by Minyu Han and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first chapter, Two-Way Fixed Effects versus Panel Factor Augmented Estimators: Asymptotic Comparison among Pre-testing Procedures, provides asymptotic analyses of pretesting procedures when the slope coefficients are heterogeneous across cross-sectional units. Empirical researchers may wonder whether or not a two-way fixed effects estimator (with individual and time fixed effects) is sufficiently sophisticated to isolate the influence of common shocks on the estimation of slope coefficients. If it is not, practitioners need to run the so-called panel factor augmented regression instead. There are two pre-testing procedures available in the literature: the use of the estimated number of factors and the direct test of estimated factor loading coefficients. This chapter compares the two pre-testing methods asymptotically. Under the presence of the heterogeneous factor loadings, both pre-testing procedures suggest using the Common Correlated Effects (CCE) estimator. By comparing asymptotic variances, this chapter finds that when the slope coefficients are heterogeneous with homogeneous factor loadings, the CCE estimation is, surprisingly, more efficient than the two-way fixed effects estimation. The second chapter, A New Test for Slope Homogeneity in a Panel Regression with Interactive Fixed Effects, proposes a new test for slope homogeneity in a panel regression with interactive fixed effects without any restriction on the relative expansion rate of n, the number of cross-sectional units, and T, the number of periods.This test is based on a comparison of the estimated number of common factors from two regression residuals. The first regression is an unconstrained regression with heterogeneous slope parameters. The second regression is a pooled regression based on the principal components mean group method. Under the slope heterogeneity, this chapter shows that the estimated number of common factors from the first regression residuals is asymptotically smaller than that of the second regression residuals. In the third chapter, Identification of Outliers for Testing Weak ϳ-Convergence, the authors suggest three novel procedures for separating the divergent series from a convergent club. Weak ϳ8́2convergence test is designed to detect whether cross-sectional variances of a panel data of interest show consistent diminution over time. When the panel data of interest includes divergent series, the cross-sectional variances become contaminated, which results in a seemingly divergent behavior. This chapter deals with this problem. We propose three novel detection procedures for identifying divergence series and provide the asymptotic justification. Utilizing Monte Carlo simulations, the finite sample properties are examined. We demonstrate the effectiveness of the newly proposed methods by using infant mortality rates in 42 countries. Even though all infant mortality rates have shown a downward trending behavior over time, the cross-sectional variance of log infant mortality rates is diverging over time. By using the proposed sieving methods, we identify six outliers. After excluding these outliers, the rest of the infant mortality rates are weakly ϳ-converging over time. Altogether, this dissertation provides methods for a better understanding of the source and nature of the cross-sectional dependence in panel data models.

Download or read book Handbook of Econometrics written by and published by Elsevier. This book was released on 2020-11-25 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Econometrics, Volume 7A, examines recent advances in foundational issues and "hot" topics within econometrics, such as inference for moment inequalities and estimation of high dimensional models. With its world-class editors and contributors, it succeeds in unifying leading studies of economic models, mathematical statistics and economic data. Our flourishing ability to address empirical problems in economics by using economic theory and statistical methods has driven the field of econometrics to unimaginable places. By designing methods of inference from data based on models of human choice behavior and social interactions, econometricians have created new subfields now sufficiently mature to require sophisticated literature summaries. - Presents a broader and more comprehensive view of this expanding field than any other handbook - Emphasizes the connection between econometrics and economics - Highlights current topics for which no good summaries exist

Download or read book Inferential Network Analysis written by Skyler J. Cranmer and published by Cambridge University Press. This book was released on 2020-11-19 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pioneering introduction of unprecedented breadth and scope to inferential and statistical methods for network analysis.

Download or read book Data Science and Productivity Analytics written by Vincent Charles and published by Springer Nature. This book was released on 2020-05-23 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of ‘productivity analysis/data envelopment analysis’ and ‘data science/big data’. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others. Examples of data science techniques include linear and logistic regressions, decision trees, Naïve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubt that nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data. Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis.

Download or read book Dissertation Abstracts International written by and published by . This book was released on 2009 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Panel Data Econometrics written by Mike Tsionas and published by Academic Press. This book was released on 2019-06-19 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Panel Data Econometrics: Theory introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made. - Provides a vast array of empirical applications useful to practitioners from different application environments - Accompanied by extensive case studies and empirical exercises - Includes empirical chapters accompanied by supplementary code in R, helping researchers replicate findings - Represents an accessible resource for diverse industries, including health, transportation, tourism, economic growth, and banking, where researchers are not always econometrics experts

Download or read book Panel Data Econometrics written by Mike Tsionas and published by Academic Press. This book was released on 2019-06-20 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: Panel Data Econometrics: Empirical Applications introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made. Provides a vast array of empirical applications useful to practitioners from different application environments Accompanied by extensive case studies and empirical exercises Includes empirical chapters accompanied by supplementary code in R, helping researchers replicate findings Represents an accessible resource for diverse industries, including health, transportation, tourism, economic growth, and banking, where researchers are not always econometrics experts

Download or read book Social Dynamics written by Steven N. Durlauf and published by MIT Press. This book was released on 2001 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of essays presents a variety of approaches to understanding the dynamics of human interaction.

Download or read book The Econometric Analysis of Network Data written by Bryan Graham and published by Academic Press. This book was released on 2020-06-03 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Econometric Analysis of Network Data serves as an entry point for advanced students, researchers, and data scientists seeking to perform effective analyses of networks, especially inference problems. It introduces the key results and ideas in an accessible, yet rigorous way. While a multi-contributor reference, the work is tightly focused and disciplined, providing latitude for varied specialties in one authorial voice. Answers both 'why' and 'how' questions in network analysis, bridging the gap between practice and theory allowing for the easier entry of novices into complex technical literature and computation Fully describes multiple worked examples from the literature and beyond, allowing empirical researchers and data scientists to quickly access the 'state of the art' versioned for their domain environment, saving them time and money Disciplined structure provides latitude for multiple sources of expertise while retaining an integrated and pedagogically focused authorial voice, ensuring smooth transition and easy progression for readers Fully supported by companion site code repository 40+ diagrams of 'networks in the wild' help visually summarize key points