Three Essays On Unobserved Heterogeneity In Panel And Network Data Models
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Three Essays On Unobserved Heterogeneity In Panel And Network Data Models
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Author : Hualei Shang
language : en
Publisher:
Release Date : 2020
Three Essays On Unobserved Heterogeneity In Panel And Network Data Models written by Hualei Shang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
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.
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 On Nonlinear Panel Models With Unobserved Heterogeneity
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Author : Robert Martin
language : en
Publisher:
Release Date : 2017
Essays On Nonlinear Panel Models With Unobserved Heterogeneity written by Robert Martin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Electronic dissertations categories.
Three Essays On Unobserved Heterogeneity
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Author :
language : en
Publisher:
Release Date : 2007
Three Essays On Unobserved Heterogeneity written by 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.
Essays In Honor Of M Hashem Pesaran
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Author : Alexander Chudik
language : en
Publisher: Emerald Group Publishing
Release Date : 2022-01-18
Essays In Honor Of M Hashem Pesaran written by Alexander Chudik 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 2022-01-18 with Business & Economics categories.
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.
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 Unbalanced Panel Data Models
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Author : Do Won Kwak
language : en
Publisher:
Release Date : 2011
Three Essays On Unbalanced Panel Data Models written by Do Won Kwak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Electronic dissertations categories.
Handbook Of Econometrics
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Author :
language : en
Publisher: Elsevier
Release Date : 2020-11-25
Handbook Of Econometrics written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Business & Economics categories.
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
Two Essays In Applied Economics
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Author : Jerome Segura (III)
language : en
Publisher:
Release Date : 2013
Two Essays In Applied Economics written by Jerome Segura (III) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
I provide two exercises which attempt to arrive at consistent estimates through the utilization of various instrumental variable (IV) and general method of moments (GMM) estimation approaches. My first study asks: is social network formation pro-cyclical or counter-cyclical? While viewing social network formation as an investment concept at the individual level is well-established, how this mechanism is affected by aggregate fluctuations has not yet been studied. I use the General Social Survey (1972-2010) to empirically test the net effect of aggregate fluctuations on individual-level social network investment. In my estimation, I attempt to address the reflection problem through the application of the Lee (2007) linear-in-means model which is most recently applied in Bramoullé et al. (2009) and Boucher et al. (2012). I also attempt to address possible bias resulting from unobserved heterogeneity. My findings indicate that social network investment is counter-cyclical. I use alternative measures of business cycle fluctuations and ad-hoc reference group formations; the results remain robust to these alternative measures and specifications. My second study asks: what are the growth effects of state and local fiscal policy. In deriving my estimable equation I combine a partial adjustment process with a factor market approach for modeling regional output. I utilize dynamic panel data estimation procedures in an attempt to arrive at a more refined set of estimates for the growth effects associated with state and local fiscal policy. I use annual observations for 48 contiguous U.S. jurisdictions ranging from 1977-2008 to empirically test the net effect of government fiscal policy on the growth rate of gross state product (GSP). To my knowledge, this is the first study which attempts to address the potential endogeneity of state and local fiscal policy. My findings indicate a large degree of heterogeneity between regions in response to effective tax rate hikes by state and local government. Although these results are robust to an alternative sample and following a reduction in the number of instruments, I am unable to verify the robustness of the estimated coefficients after a number of other alternative specifications. I interpret the results as an indication that policymakers should err on the side of caution in extrapolating the results of empirical studies to their own states and time periods.
Data Science And Productivity Analytics
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Author : Vincent Charles
language : en
Publisher: Springer Nature
Release Date : 2020-05-23
Data Science And Productivity Analytics written by Vincent Charles and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-23 with Business & Economics categories.
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.