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Author : Amrei Stammann
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (113 download)
Download or read book Nonlinear Panel Data Models with High-Dimensional Fixed Effects written by Amrei Stammann and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Martin Mugnier
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Identification and (Fast) Estimation of Large Nonlinear Panel Models with Two-Way Fixed Effects written by Martin Mugnier and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study a nonlinear two-way fixed effects panel model that allows for unobserved individual heterogeneity in slopes (interacting with covariates) and (unknown) flexibly specified link function. The former is particularly relevant when the researcher is interested in the distributional causal effects of covariates, and the latter mitigates potential misspecification errors due to imposing a known link function. We show that the fixed effects parameters and the (nonparametrically specified) link function can be identified when both individual and time dimensions are large. We propose a novel iterative Gauss-Seidel estimation procedure that overcomes the practical challenge of dimensionality in the number of fixed effects when the dataset is large. We revisit two empirical studies in trade (Helpman et al., 2008) and innovation (Aghion et al., 2013), and find non-negligible unobserved dispersion in trade elasticity (across countries) and the effect of institutional ownership on innovation (across firms). These exercises emphasize the usefulness of our method in capturing flexible (and unobserved) heterogeneity in the causal relationship of interest that may have important implications for the subsequent policy analysis.
Author : Feng Qu
Publisher : World Scientific
ISBN 13 : 9811220794
Total Pages : 167 pages
Book Rating : 4.8/5 (112 download)
Download or read book Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes written by Feng Qu and published by World Scientific. This book was released on 2020-08-24 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to fill the gap between panel data econometrics textbooks, and the latest development on 'big data', especially large-dimensional panel data econometrics. It introduces important research questions in large panels, including testing for cross-sectional dependence, estimation of factor-augmented panel data models, structural breaks in panels and group patterns in panels. To tackle these high dimensional issues, some techniques used in Machine Learning approaches are also illustrated. Moreover, the Monte Carlo experiments, and empirical examples are also utilised to show how to implement these new inference methods. Large-Dimensional Panel Data Econometrics: Testing, Estimation and Structural Changes also introduces new research questions and results in recent literature in this field.
Author : James W. Hardin
Publisher : Stata Press
ISBN 13 : 1597180149
Total Pages : 413 pages
Book Rating : 4.5/5 (971 download)
Download or read book Generalized Linear Models and Extensions, Second Edition written by James W. Hardin and published by Stata Press. This book was released on 2007 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deftly balancing theory and application, this book stands out in its coverage of the derivation of the GLM families and their foremost links. This edition has new sections on discrete response models, including zero-truncated, zero-inflated, censored, and hurdle count models, as well as heterogeneous negative binomial, and more.
Author : Laszlo Balazsi
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (95 download)
Download or read book The Estimation of Multi-dimensional Fixed Effects Panel Data Models written by Laszlo Balazsi and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Edward W. Frees
Publisher : Cambridge University Press
ISBN 13 : 9780521535380
Total Pages : 492 pages
Book Rating : 4.5/5 (353 download)
Download or read book Longitudinal and Panel Data written by Edward W. Frees and published by Cambridge University Press. This book was released on 2004-08-16 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.
Author : David W. Hughes
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)
Download or read book Estimating Nonlinear Network Data Models with Fixed Effects written by David W. Hughes and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : William H. Greene
Publisher :
ISBN 13 :
Total Pages : 48 pages
Book Rating : 4.:/5 (129 download)
Download or read book Fixed and Random Effects in Nonlinear Models written by William H. Greene and published by . This book was released on 2008 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper surveys recently developed approaches to analyzing panel data with nonlinear models. We summarize a number of results on estimation of fixed and random effects models in nonlinear modelingframeworks such as discrete choice, count data, duration, censored data, sample selection, stochastic frontier and, generally, models that are nonlinear both in parameters and variables. We show thatnotwithstanding their methodological shortcomings, fixed effects are much more practical than heretofore reflected in the literature. For random effects models, we develop an extension of a random parametersmodel that has been used extensively, but only in the discrete choice literature. This model subsumes the random effects model, but is far more flexible and general, and overcomes some of the familiar shortcomings of the simple additive random effects model as usually formulated. Once again, the range of applications is extended beyond the familiar discrete choice setting. Finally, we draw together several strands of applications of a model that has taken a semiparametric approach to individual heterogeneity inpanel data, the latent class model. A fairly straightforward extension is suggested that should make this more widely useable by practitioners. Many of the underlying results already appear in the literature, but,once again, the range of applications is smaller than it could be.
Author : Dalia A. Ghanem
Publisher :
ISBN 13 : 9781303193743
Total Pages : 233 pages
Book Rating : 4.1/5 (937 download)
Download or read book Nonseparable Panel Data Models Identification, Estimation and Testing written by Dalia A. Ghanem and published by . This book was released on 2013 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microeconomic panel data, also known as longitudinal data or repeated measures, allow the researcher to observe the same individual across time. One of the advantages of panel data is that they allow the researcher to control for unobservable individual heterogeneity. The linear fixed effects model is the most commonly used method in empirical work to control for unobservable heterogeneity. Chapter 1 reviews the special features of the linear fixed effects model in detail, giving special attention to the definition of fixed effects and correlated random effects. It discusses the issues that arise when we move from a linear model to fully nonseparable models and reviews the two strands of the literature that are relevant for this dissertation: (1) the literature on nonlinear parametric panel data models with fixed effects, (2) the literature on nonparametric identification in nonseparable panel data models. Chapter 2 falls under the parametric nonlinear panel data models with fixed effects. Nonlinear panel data models with fixed effects are an important example in econometrics where the incidental parameter problem arises and the maximum likelihood estimator (MLE) is asymptotically biased. Bias correction of the MLE achieves consistency without increasing the asymptotic variance. Chapter 2 proposes a shrinkage estimator that combines that is shown to lead to a higher-order mean-square error improvement over the analytical bias-corrected estimator. Chapter 3 falls under the literature on nonparametric identification in nonseparable panel data models. Starting from a general DGP that exhibits nonseparability of the structural function, arbitrary individual and time heterogeneity, I give a necessary and sufficient condition for the point-identification of the APE for a subpopulation. This condition is then used to characterize the trade-off between assumptions on unobservable heterogeneity and the structural function that achieve identification. The identifying assumptions here have clear testable implications on the distribution of observables. I hence propose bootstrap-adjusted Kolmogorv-Smirnov and Cramer-von-Mises statistics to test these implications. Chapter 4 is an empirical paper that studies the issue of manipulation of air pollution data by Chinese cities. It applies tests similar in spirit to the tests proposed in Chapter 3 to test the presence of manipulation.
Author : Degui Li
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (752 download)
Download or read book Nonparametric Time-varying Coefficient Panel Data Models with Fixed Effects written by Degui Li and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is concerned with developing a nonparametric time varying coefficient model with fixed effects to characterize nonstationarity and trending phenomenon in nonlinear panel data analysis. We develop two methods to estimate the trend function and the coefficient function without taking the first difference to eliminate the fixed effects. The first one eliminates the fixed effects by taking cross{sectional averages, and then uses a nonparametric local linear approach to estimate the trend function and the coefficient function. The asymptotic theory for this approach reveals that although the estimates of both the trend function and the coefficient function are consistent, the estimate of the coefficient function has a rate of convergence of (Th) that is slower than that of the trend function, which has a rate of (NTh). To estimate the coefficient function more efficiently, we propose a pooled local linear dummy variable approach. This is motivated by a least squares dummy variable method proposed in parametric panel data analysis. This method removes the fixed effects by deducting a smoothed version of cross{time average from each individual. It estimates the trend function and the coeficient function with a rate of convergence of (NTh). The asymptotic distributions of both of the estimates are established when T tends to infinity and N is fixed or both T and N tend to infinity. Simulation results are provided to illustrate the finite sample behavior of the proposed estimation methods.
Author : John M. Abowd
Publisher : Université de Montréal, Centre de recherche et développement en économique
ISBN 13 :
Total Pages : 94 pages
Book Rating : 4.:/5 (318 download)
Download or read book High Wage Workers and High Wage Firms written by John M. Abowd and published by Université de Montréal, Centre de recherche et développement en économique. This book was released on 1994 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study a longitudinal sample of over one million French workers and over 500,000 employing firms. Real total annual compensation per worker is decomposed into components related to observable characteristics, worker heterogeneity, firm heterogeneity and residual variation. Except for the residual, all components may be correlated in an arbitrary fashion. At the level of the individual, we find that person-effects, especially those not related to observables like education, are the most important source of wage variation in France. Firm-effects, while important, are not as important as person-effects. At the level of firms, we find that enterprises that hire high-wage workers are more productive but not more profitable. They are also more capital and high-skilled employee intensive. Enterprises that pay higher wages, controlling for person-effects, are more productive and more profitable. They are also more capital intensive but are not more high-skilled labor intensive. We also find that person-effects explain 92% of inter-industry wage differentials.
Author : Kao Chihwa
Publisher : World Scientific
ISBN 13 : 9811200173
Total Pages : 180 pages
Book Rating : 4.8/5 (112 download)
Download or read book High-dimensional Econometrics And Identification written by Kao Chihwa and published by World Scientific. This book was released on 2019-04-10 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many applications of econometrics and economics, a large proportion of the questions of interest are identification. An economist may be interested in uncovering the true signal when the data could be very noisy, such as time-series spurious regression and weak instruments problems, to name a few. In this book, High-Dimensional Econometrics and Identification, we illustrate the true signal and, hence, identification can be recovered even with noisy data in high-dimensional data, e.g., large panels. High-dimensional data in econometrics is the rule rather than the exception. One of the tools to analyze large, high-dimensional data is the panel data model.High-Dimensional Econometrics and Identification grew out of research work on the identification and high-dimensional econometrics that we have collaborated on over the years, and it aims to provide an up-todate presentation of the issues of identification and high-dimensional econometrics, as well as insights into the use of these results in empirical studies. This book is designed for high-level graduate courses in econometrics and statistics, as well as used as a reference for researchers.
Author : Nicholas Lynn Brown
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.4/5 (387 download)
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.
Author : Mingli Chen
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (119 download)
Download or read book Nonlinear Factor Models for Network and Panel Data written by Mingli Chen and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Factor structures or interactive effects are convenient devices to incorporate latent variables in panel data models. We consider fixed effect estimation of nonlinear panel single-index models with factor structures in the unobservables, which include logit, probit, ordered probit and Poisson specifications. We establish that fixed effect estimators of model parameters and average partial effects have normal distributions when the two dimensions of the panel grow large, but might suffer from incidental parameter bias. We show how models with factor structures can also be applied to capture important features of network data such as reciprocity, degree heterogeneity, homophily in latent variables and clustering. We illustrate this applicability with an empirical example to the estimation of a gravity equation of international trade between countries using a Poisson model with multiple factors.
Author : A. Colin Cameron
Publisher : Cambridge University Press
ISBN 13 : 1139444867
Total Pages : 1058 pages
Book Rating : 4.1/5 (394 download)
Download or read book Microeconometrics written by A. Colin Cameron and published by Cambridge University Press. This book was released on 2005-05-09 with total page 1058 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.
Author : Iván Fernández-Val
Publisher :
ISBN 13 :
Total Pages : 54 pages
Book Rating : 4.:/5 (129 download)
Download or read book Estimation of Structural Parameters and Marginal Effects in Binary Choice Panel Data Models with Fixed Effects written by Iván Fernández-Val and published by . This book was released on 2005 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fixed effects estimators of nonlinear panel models can be severely biased due to the incidental parameters problem. In this paper I find that the most important component of this incidental parameters bias for probit fixed effects estimators of index coefficients is proportional to the true value of these coefficients, using a large-T expansion of the bias. This result allows me to derive a lower bound for this bias, and to show that fixed effects estimates of ratios of coefficients and average marginal effects have zero bias in the absence of heterogeneity and have negligible bias relative to their true values for a wide variety of distributions of regressors and individual effects. Numerical examples suggest that this small bias property also holds for logit and linear probability models, and for exogenous variables in dynamic binary choice models. An empirical analysis of female labor force participation using data from the PSID shows that whereas the significant biases in fixed effects estimates of index coefficients do not contaminate the estimates of marginal effects in static models, estimates of both index coefficients and marginal effects can be severely biased in dynamic models. Improved bias corrected estimators for index coefficients and marginal effects are also proposed for both static and dynamic models.
Author : Lang Wu
Publisher : CRC Press
ISBN 13 : 9781420074086
Total Pages : 431 pages
Book Rating : 4.0/5 (74 download)
Download or read book Mixed Effects Models for Complex Data written by Lang Wu and published by CRC Press. This book was released on 2009-11-11 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
