Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
A Dynamic Model For Binary Panel Data With Unobserved Heterogeneity Admitting A Root N Consistent Conditional Estimator
Download A Dynamic Model For Binary Panel Data With Unobserved Heterogeneity Admitting A Root N Consistent Conditional Estimator full books in PDF, epub, and Kindle. Read online A Dynamic Model For Binary Panel Data With Unobserved Heterogeneity Admitting A Root N Consistent Conditional Estimator ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
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.
Book Synopsis Analysis of Panel Data by : Cheng Hsiao
Download or read book Analysis of Panel Data written by Cheng Hsiao and published by Cambridge University Press. This book was released on 2014-12-08 with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive, coherent, and intuitive review of panel data methodologies that are useful for empirical analysis. Substantially revised from the second edition, it includes two new chapters on modeling cross-sectionally dependent data and dynamic systems of equations. Some of the more complicated concepts have been further streamlined. Other new material includes correlated random coefficient models, pseudo-panels, duration and count data models, quantile analysis, and alternative approaches for controlling the impact of unobserved heterogeneity in nonlinear panel data models.
Book Synopsis Dynamic Unobserved Effects Model for Continuous and Binary Response by : Chung-Jung Lee
Download or read book Dynamic Unobserved Effects Model for Continuous and Binary Response written by Chung-Jung Lee and published by . This book was released on 2000 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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 The Oxford Handbook of Organizational Change and Innovation by : Marshall Scott Poole
Download or read book The Oxford Handbook of Organizational Change and Innovation written by Marshall Scott Poole and published by Oxford University Press. This book was released on 2021-05-20 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: Organizational change and innovation are central and enduring issues in management theory and practice. Dramatic changes in population demographics, technology, competitive survival, and social, economic, and environmental health and sustainability concerns means the need to understand how organizations repond to these shifts through change and innovation has never been greater. Why and what organizations change is generally well known; how organizations change is therefore the central focus of this Handbook. It focuses on processes of change — or the sequence of events in which organizational characteristics and activities change and develop over time — and the factors that influence these processes, with the organization as the central unit of analysis. Across the diverse and wide-ranging contributions, three central questions evolve: what is the nature of change and process?; what are the key concepts and models for understanding organization change and innovation?; and how should we study change and innovation? This Handbook presents critical evolving scholarship from leading experts across a range of disciplines, and explores its implications for future research and practice.
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 Estimation and Testing in Dynamic, Nonlinear Panel Data Models by : Margaret Susan Loudermilk
Download or read book Estimation and Testing in Dynamic, Nonlinear Panel Data Models written by Margaret Susan Loudermilk and published by . This book was released on 2006 with total page 256 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 Estimating Panel Data Models with Endogeneity and Selection by : Anastasia Semykina
Download or read book Estimating Panel Data Models with Endogeneity and Selection written by Anastasia Semykina and published by . This book was released on 2006 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Consistent Estimation of Binary-Choice Panel Data Models with Heterogeneous Linear Trends by : Alban Thomas
Download or read book Consistent Estimation of Binary-Choice Panel Data Models with Heterogeneous Linear Trends written by Alban Thomas and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents an extension of fixed effects binary choice models for panel data, to the case of heterogeneous linear trends. Two estimators are proposed: a Logit estimator based on double conditioning and a semiparametric, smoothed maximum score estimator based on double differences. We investigate small-sample properties of these estimators with a Monte Carlo simulation experiment, and compare their statistical properties with standard fixed effects procedures. An empirical application to land renting decisions of Russian households between 1996 and 2002 is proposed.
Book Synopsis A Root-N Consistent Semiparametric Estimator for Fixed Effect Binary Response Panel Data by : M.J Lee
Download or read book A Root-N Consistent Semiparametric Estimator for Fixed Effect Binary Response Panel Data written by M.J Lee and published by . This book was released on 1998 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a root-N-consistent estimator for binary response panel data where the individual specific effect may be correlated with the regressors. The estimator is asymptotically normal with a simple variance matrix.
Book Synopsis Initial Conditions and Moment Restrictions in Dynamic Panel Data Models by : Richard Blundell
Download or read book Initial Conditions and Moment Restrictions in Dynamic Panel Data Models written by Richard Blundell and published by . This book was released on 1995 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Essays in Dynamic Panel Data Models and Labor Supply by : Kolobadia Ada Nayihouba
Download or read book Essays in Dynamic Panel Data Models and Labor Supply written by Kolobadia Ada Nayihouba and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Semiparametric Analysis of Random Effects Linear Models from Binary Panel Data by : Charles F. Manski
Download or read book Semiparametric Analysis of Random Effects Linear Models from Binary Panel Data written by Charles F. Manski and published by . This book was released on 1985 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
