Download or read book A Note on the Identification of Structural Parameters in Linear Models Containing Unobservable Variables written by C. L. F. Attfield and published by . This book was released on 1978 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Estimation and Identification of Structural Parameters in Models Containing Unobservable Variables written by Cliff Attfield and published by . This book was released on 1975 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Further Notes on Identification in Structural Equation Models which Contain Unobservable Variables written by Cliff Attfield and published by . This book was released on 1975 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Linear Models in Statistics written by Alvin C. Rencher and published by John Wiley & Sons. This book was released on 2008-01-07 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.

Download or read book Identification in dynamic linear models with rational expectations written by Olivier J. Blanchard and published by . This book was released on 1982 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper characterizes identification in dynamic linear models. It shows that identification restrictions are linear in the structural parameters and are therefore easy to use. Using these restrictions, it analyzes the role of exogenous variables in helping to achieve identification.

Download or read book Unobserved Variables written by David J. Bartholomew and published by Springer Science & Business Media. This book was released on 2013-09-07 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​The classical statistical problem typically involves a probability distribution which depends on a number of unknown parameters. The form of the distribution may be known, partially or completely, and inferences have to be made on the basis of a sample of observations drawn from the distribution; often, but not necessarily, a random sample. This brief deals with problems where some of the sample members are either unobserved or hypothetical, the latter category being introduced as a means of better explaining the data. Sometimes we are interested in these kinds of variable themselves and sometimes in the parameters of the distribution. Many problems that can be cast into this form are treated. These include: missing data, mixtures, latent variables, time series and social measurement problems. Although all can be accommodated within a Bayesian framework, most are best treated from first principles.

Download or read book Partial Least Squares Structural Equation Modeling PLS SEM Using R written by Joseph F. Hair Jr. and published by Springer Nature. This book was released on 2021-11-03 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.

Download or read book Structural Equation Models in the Social Sciences written by Social Science Research Council (U.S.) and published by New York : Seminar Press. This book was released on 1973 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monographic compilation of conference papers on methodology for the use of mathematical models in the social sciences - presents interdisciplinary research applications of structural equation (causal) models, and includes recent techniques of mathematical analysis and statistical analysis, etc. References and statistical tables. Conference held in madison 1970 November 12 to 16.

Download or read book STRUCTURAL EQUATION MODELS WITH UNOBSERVABLE VARIABLES AND MEASUREMENT ERROR ALGEBRA AND STATISTICS written by CLAES FORNELL and published by . This book was released on 1981 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Structural Equation Modeling with LISREL written by Leslie A. Hayduk and published by JHU Press. This book was released on 1987 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hayduk is equally at ease explaining the simplest and most advanced applications of the program . . . Hayduk has written more than just a solid text for use in advanced graduate courses on statistical modeling. Those with a firm mathematical background who wish to learn about the approach, or those who know a little about the program and want to know more, will find this an excellent reference.

Download or read book Essays on Partial Identification in Structural Models written by Lixiong Li and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: My studies focus on the partial identification in structural econometric models. This dissertation includes two chapters on partial identification and one chapter on a numerical method of estimating structural discrete choice models. Chapter 1Structural econometric models usually involve parametric distributional assumptions for unobserved heterogeneity. Although these assumptions are typically not informed by economic theory, and undermine the robustness of empirical results, they are generally thought to be necessary to simulate counterfactual predictions. In partially identified and incomplete structural models, counterfactual analysis is also hampered by the multiplicity of admissible structural parameter values and the multiplicity of counterfactual predictions for each structural parameter value. This paper shows how to construct identification conditions for both structural and counterfactual parameters in a large class of structural econometric models, including partially identified and incomplete ones, without imposing parametric distributional assumptions for unobserved variables. The identified set is characterized by moment inequalities, so that existing inferential methods can be applied, including subvector inference when only counterfactual parameters are of interest. The novelty and computational tractability of the methodology is illustrated on a class of discrete choice models and a class of entry models.Chapter 2I investigate a model of one-to-one matching with transferable utilities, where the matching process is subject to time-consuming search frictions. I assume agents have unobserved (to economists) characteristics, which affect the matching surplus along with matching specific random shocks under a separability assumption. I show the matching surplus can be non-parametrically identified with data on matching patterns and distributions on unmatched durations across agents, given any known distribution on unobserved characteristics. In contrast to the existing literature, my identification strategy does not hinge on data on payoffs and panel data with long time series. As in frictionless matching models, I show any interior matching patterns can be rationalized by the model under some parameters. For one type of corner solution, only set identification is attained and a sharp bound has been derived.Chapter 3This paper describes a numerical method to solve for mean product qualities which equates the real market share to the market share predicted by a discrete choice model. The method covers a general class of discrete choice model, including the pure characteristics model in \cite{berry_pure_2007} and the random coefficient logit model in \cite{berry_automobile_1995} (hereafter BLP). The method transforms the original market share inversion problem to an unconstrained convex minimization problem, so that any convex programming algorithm can be used to solve the inversion. Moreover, such results also imply that the computational complexity of inverting a demand model should be no more than that of a convex programming problem. In simulation examples, I show the method outperforms the contraction mapping algorithm in BLP. I also find the method remains robust in pure characteristics models with near-zero market shares.

Download or read book Composite Based Structural Equation Modeling written by Jörg Henseler and published by Guilford Publications. This book was released on 2020-12-24 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents powerful tools for integrating interrelated composites--such as capabilities, policies, treatments, indices, and systems--into structural equation modeling (SEM). Jörg Henseler introduces the types of research questions that can be addressed with composite-based SEM and explores the differences between composite- and factor-based SEM, variance- and covariance-based SEM, and emergent and latent variables. Using rich illustrations and walked-through data sets, the book covers how to specify, identify, estimate, and assess composite models using partial least squares path modeling, maximum likelihood, and other estimators, as well as how to interpret findings and report the results. Advanced topics include confirmatory composite analysis, mediation analysis, second-order constructs, interaction effects, and importance–performance analysis. Most chapters conclude with software tutorials for ADANCO and the R package cSEM. The companion website includes data files and syntax for the book's examples, along with presentation slides.

Download or read book Handbook of Latent Variable and Related Models written by and published by Elsevier. This book was released on 2011-08-11 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables. - Covers a wide class of important models - Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data - Includes illustrative examples with real data sets from business, education, medicine, public health and sociology. - Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.

Download or read book Identification and Estimation of Dynamic Structural Models with Unobserved Choices written by Yingyao Hu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops identification and estimation methods for dynamic structural models when agents' actions are unobserved by econometricians. We provide conditions under which choice probabilities and latent state transition rules are nonparametrically identified with a continuous state variable in a single-agent dynamic discrete choice model. Our identification results extend to (1) models with serially correlated unobserved heterogeneity and continuous choices, (2) cases in which only discrete state variables are available, and (3) dynamic discrete games. We apply our method to study moral hazard problems in US gubernatorial elections. We find that the probabilities of shirking increase as the governors approach the end of their terms.

Download or read book Journal of the American Statistical Association written by and published by . This book was released on 2004 with total page 684 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advanced Issues in Partial Least Squares Structural Equation Modeling written by Joseph F. Hair, Jr. and published by SAGE Publications. This book was released on 2017-04-25 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written as an extension of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) Second Edition, this easy-to-understand, practical guide covers advanced content on PLS-SEM to help students and researchers apply techniques to research problems and accurately interpret results. The book provides a brief overview of basic concepts before moving to the more advanced material. Offering extensive examples on SmartPLS 3 software and accompanied by free downloadable data sets, the book emphasizes that any advanced PLS-SEM approach should be carefully applied to ensure that it fits the appropriate research context and the data characteristics that underpin the research.

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.