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Panel Data Models With Nonadditive Unobserved Heterogeneity
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Book Synopsis Panel Data Models with Nonadditive Unobserved Heterogeneity by : Joonhwan Lee
Download or read book Panel Data Models with Nonadditive Unobserved Heterogeneity written by Joonhwan Lee and published by . This book was released on 2014 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers fixed effects estimation and inference in linear and nonlinear panel data models with random coefficients and endogenous regressors. The quantities of interest - means, variances, and other moments of the random coefficients - are estimated by cross sectional sample moments of 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 estimator has an asymptotic bias of the same order as the asymptotic variance. The bias corrections remove the bias without increasing variance. An empirical example on cigarette demand based on Becker, Grossman and Murphy (1994) shows significant heterogeneity in the price effect across U.S. states.
Book Synopsis Essays on Nonlinear Panel Models with Unobserved Heterogeneity by : Robert Martin
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:
Book Synopsis Panel Data Models with Unobserved Effects and Endogenous Explanatory Variables by : Irina Murtazashvili
Download or read book Panel Data Models with Unobserved Effects and Endogenous Explanatory Variables written by Irina Murtazashvili and published by . This book was released on 2007 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Initial Conditions Problem in Dynamic, Nonlinear Panel Data Models with Unobserved Heterogeneity by : Jeffrey M. Wooldridge
Download or read book The Initial Conditions Problem in Dynamic, Nonlinear Panel Data Models with Unobserved Heterogeneity written by Jeffrey M. Wooldridge and published by . This book was released on 2000 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Panel Data Econometrics by : Manuel Arellano
Download or read book Panel Data Econometrics written by Manuel Arellano and published by Oxford University Press. This book was released on 2003 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by one of the world's leading experts on dynamic panel data reviews, this volume reviews most of the important topics in the subject. It deals with static models, dynamic models, discrete choice and related models.
Book Synopsis Three Essays on Unobserved Heterogeneity in Panel and Network Data Models by : Hualei Shang
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.
Book Synopsis Nonseparable Panel Data Models Identification, Estimation and Testing by : Dalia A. Ghanem
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.
Book Synopsis Missing Data Methods by : David M. Drukker
Download or read book Missing Data Methods written by David M. Drukker and published by Emerald Group Publishing. This book was released on 2011-11-23 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains 16 chapters authored by specialists in the field, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas; and, Consistent Estimation and Orthogonality.
Book Synopsis Identification of Non-Additive Fixed Effects Models by : Jinyong Hahn
Download or read book Identification of Non-Additive Fixed Effects Models written by Jinyong Hahn and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Panel or grouped data are often used to allow for unobserved individual heterogeneity in econometric models via fixed effects. In this paper, we discuss identification of a panel data model in which the unobserved heterogeneity both enters additively and interacts with treatment variables. We present identification and estimation methods for parameters of interest in this model under both strict and weak exogeneity assumptions. The key identification insight is that other periods' treatment variables are instruments for the unobserved fixed effects. We apply our proposed estimator to matched student-teacher data used to estimate value-added models of teacher quality. We show that the common assumption that the return to unobserved teacher quality is the same for all students is rejected by the data. We also present evidence that No Child Left Behind-era school accountability increased the effectiveness of teacher quality for lower performing students.
Book Synopsis Extended Mundlak Device for Heterogeneity in Nonlinear Panel Data Models with Positive Response Variables by : Shengwu Shang
Download or read book Extended Mundlak Device for Heterogeneity in Nonlinear Panel Data Models with Positive Response Variables written by Shengwu Shang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using the models in Wooldridge (1999), We compare three main estimation methods for positive response variable-- FE method for log linear model (LFE), Poisson Quasi-Maximum Likelihood (PQML) and Generalized Method of Moment (GMM) -- by Mont Carlo Simulation and real life data set. It is not surprising that LFE estimator is not consistent when PQML is; however, we do find circumstance where both LFE and PQML estimators are consistent plus LFE is more efficient. With this regard, we introduce GMM to improve the efficiency of PQML estimator as well as keeping the consistency; this way also finds a solution to the problem raised in Wooldridge (1999). From the simulation results, we find that GMM can reduce the standard error of PQML estimator by almost a half. We also apply the GMM to a US domestic airlines data set and the result shows that GMM improves the efficiency by about $10 %$ compared with PQML. On the other hand, to make full use of the PQML method, We propose a semiparametric estimator of average partial effect after consistently estimating the parameters of interest; the result automatically extends the results in Ai and Norton (2008) from cross sectional setting to panel data models. In order to catch the positivity of the unknown conditional expectation function of the unobserved heterogeneity, we borrow the idea of power series approximation of unknown function in Newey(1993,1994) and develop an ``exponential sieves" estimator suggested in Wooldridge(1992a).
Book Synopsis Unobserved Heterogeneity in Panel Time Series Models by : Ana-Maria Fuertes
Download or read book Unobserved Heterogeneity in Panel Time Series Models written by Ana-Maria Fuertes and published by . This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, the large T panel literature has emphasized unobserved, time-varying heterogeneity that may stem from omitted common variables or global shocks that affect each individual unit differently. These latent common factors induce cross-section dependence and may lead to inconsistent regression coefficient estimates if they are correlated with the explanatory variables. Moreover, if the process underlying these factors is nonstationary, the individual regressions will be spurious but pooling or averaging across individual estimates still permits consistent estimation of a long-run coefficient. The need to tackle both error cross-section dependence and persistent autocorrelation is motivated by the evidence of their pervasiveness found in three well-known, international finance and macroeconomic examples. A range of estimators is surveyed and their finite-sample properties are examined by means of Monte Carlo experiments. These reveal that a mean group version of the common-correlated-effects estimator stands out as the most robust since it is the preferred choice in rather general (non) stationary settings where regressors and errors share common factors and their factor loadings are possibly dependent. Other approaches which perform reasonably well include the two-way fixed effects, demeaned mean group and between estimators but they are less efficient than the common-correlated-effects estimator.
Book Synopsis An Exponential Class of Dynamic Binary Choice Panel Data Models with Fixed Effects by : Majid Al-Sadoon
Download or read book An Exponential Class of Dynamic Binary Choice Panel Data Models with Fixed Effects written by Majid Al-Sadoon and published by . This book was released on 2016 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes an exponential class of dynamic binary choice panel data models for the analysis of short T (time dimension) large N (cross section dimension) panel data sets that allows for unobserved heterogeneity (fixed effects) to be arbitrarily correlated with the covariates. The paper derives moment conditions that are invariant to the fixed effects which are then used to identify and estimate the parameters of the model. Accordingly, GMM estimators are proposed that are consistent and asymptotically normally distributed at the root-N rate. We also study the conditional likelihood approach, and show that under exponential specification it can identify the effect of state dependence but not the effects of other covariates. Monte Carlo experiments show satisfactory finite sample performance for the proposed estimators, and investigate their robustness to miss-specification.
Book Synopsis Three Essays on Panel Data Models with Interactive and Unobserved Effects by : Nicholas Lynn Brown
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.
Book Synopsis An Exponential Class of Dynamic Binary Choice Panel Data Models with Fixed Effects by : Majid M. al- Sadoon
Download or read book An Exponential Class of Dynamic Binary Choice Panel Data Models with Fixed Effects written by Majid M. al- Sadoon and published by . This book was released on 2012 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Econometric Analysis of Cross Section and Panel Data, second edition by : Jeffrey M. Wooldridge
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.
Book Synopsis Identification and (Fast) Estimation of Large Nonlinear Panel Models with Two-Way Fixed Effects by : Martin Mugnier
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.
Book Synopsis A Dynamic Model for Binary Panel Data With Unobserved Heterogeneity Admitting a Root-N Consistent Conditional Estimator by : Francesco Bartolucci
Download or read book A Dynamic Model for Binary Panel Data With Unobserved Heterogeneity Admitting a Root-N Consistent Conditional Estimator written by Francesco Bartolucci and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A model for binary panel data is introduced which allows for state dependence and unobserved heterogeneity beyond the effect of strictly exogenous covariates. The model is of quadratic exponential type and its structure closely resembles that of the dynamic logit model. An economic interpretation of its assumptions, based on expectation about future outcomes, is provided. The main advantage of the proposed model, with respect to the dynamic logit model, is that each individual-specific parameter for the unobserved heterogeneity may be eliminated by conditioning on the sum of the corresponding response variables. A conditional likelihood results which allows us to identify the structural parameters of the model with at least three observations (included an initial observation assumed to be exogenous), even in the presence of time dummies. A root-n consistent conditional estimator of these parameters also results which is very simple to compute. Its finite sample properties are studied by means of a simulation study. Extensions of the proposed approach are discussed with reference, in particular, to the case of more elaborated structures for the state dependence and to that of categorical response variables with more than two levels.
