Seemingly Unrelated Regression Equations Models

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Seemingly Unrelated Regression Equations Models

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Author : Virendera K. Srivastava
language : en
Publisher: CRC Press
Release Date : 1987-05-29

Seemingly Unrelated Regression Equations Models written by Virendera K. Srivastava and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987-05-29 with Mathematics categories.

The seemingly unrelated regression equations model; The least squares estimator and its variants; Approximate destribution theory for feasible generalized least squares estimators; Exact finite-sample properties of feasible generalized least squares estimators; Iterative estimators; Shrinkage estimators; Autoregressive disturbances; Heteroscedastic disturbances; Constrained error covariance structures; Prior information; Some miscellaneous topics.

Seemingly Unrelated Regression Equations Models

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Author : Virendera K. Srivastava
language : en
Publisher: CRC Press
Release Date : 2020-08-14

Seemingly Unrelated Regression Equations Models written by Virendera K. Srivastava and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-14 with Mathematics categories.

This book brings together the scattered literature associated with the seemingly unrelated regression equations (SURE) model used by econometricians and others. It focuses on the theoretical statistical results associated with the SURE model.

Applied Econometrics With R

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Author : Christian Kleiber
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-12-10

Applied Econometrics With R written by Christian Kleiber and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12-10 with Business & Economics categories.

R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.

Dynamic Linear Models With R

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Author : Giovanni Petris
language : en
Publisher: Springer
Release Date : 2009-06-02

Dynamic Linear Models With R written by Giovanni Petris and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-02 with Mathematics categories.

State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.

Multiple Regression And Beyond

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Author : Timothy Z. Keith
language : en
Publisher: Routledge
Release Date : 2019-01-14

Multiple Regression And Beyond written by Timothy Z. Keith and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-14 with Education categories.

Companion Website materials: https://tzkeith.com/ Multiple Regression and Beyond offers a conceptually-oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. This book: • Covers both MR and SEM, while explaining their relevance to one another • Includes path analysis, confirmatory factor analysis, and latent growth modeling • Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises • Extensive use of figures and tables providing examples and illustrating key concepts and techniques New to this edition: • New chapter on mediation, moderation, and common cause • New chapter on the analysis of interactions with latent variables and multilevel SEM • Expanded coverage of advanced SEM techniques in chapters 18 through 22 • International case studies and examples • Updated instructor and student online resources

Econometric Analysis Of Cross Section And Panel Data Second Edition

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Author : Jeffrey M. Wooldridge
language : en
Publisher: MIT Press
Release Date : 2010-10-01

Econometric Analysis Of Cross Section And Panel Data Second Edition written by Jeffrey M. Wooldridge and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-01 with Business & Economics categories.

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.

Parallel Algorithms For Linear Models

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Author : Erricos Kontoghiorghes
language : en
Publisher: Springer Science & Business Media
Release Date : 2000-01-31

Parallel Algorithms For Linear Models written by Erricos Kontoghiorghes and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-01-31 with Business & Economics categories.

Parallel Algorithms for Linear Models provides a complete and detailed account of the design, analysis and implementation of parallel algorithms for solving large-scale linear models. It investigates and presents efficient, numerically stable algorithms for computing the least-squares estimators and other quantities of interest on massively parallel systems. The monograph is in two parts. The first part consists of four chapters and deals with the computational aspects for solving linear models that have applicability in diverse areas. The remaining two chapters form the second part, which concentrates on numerical and computational methods for solving various problems associated with seemingly unrelated regression equations (SURE) and simultaneous equations models. The practical issues of the parallel algorithms and the theoretical aspects of the numerical methods will be of interest to a broad range of researchers working in the areas of numerical and computational methods in statistics and econometrics, parallel numerical algorithms, parallel computing and numerical linear algebra. The aim of this monograph is to promote research in the interface of econometrics, computational statistics, numerical linear algebra and parallelism.

Using R For Principles Of Econometrics

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Author : Constantin Colonescu
language : en
Publisher: Lulu.com
Release Date : 2017-12-28

Using R For Principles Of Econometrics written by Constantin Colonescu and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-28 with Business & Economics categories.

This is a beginner's guide to applied econometrics using the free statistics software R. It provides and explains R solutions to most of the examples in 'Principles of Econometrics' by Hill, Griffiths, and Lim, fourth edition. 'Using R for Principles of Econometrics' requires no previous knowledge in econometrics or R programming, but elementary notions of statistics are helpful.

Solving Seemingly Unrelated Regression Equations Models Using Orthogonal Decompositions

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Author : E. Kontoghiorghes
language : en
Publisher:
Release Date : 1993

Solving Seemingly Unrelated Regression Equations Models Using Orthogonal Decompositions written by E. Kontoghiorghes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with categories.