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An introduction to modern econometrics using Stata / Christopher F. Baum

Title : An introduction to modern econometrics using Stata Material Type: printed text Authors: Christopher F. Baum, Author Publisher: Stata Press Publication Date: 2006 Pagination: xviii, 341 p. : |b ill. ; |c 24 cm. Layout: ill. Size: 24 cm ISBN (or other code): 978-1-597-18013-9 General note: Includes bibliographical references (p. [321]-327) and indexes Languages : English ( eng) Original Language : English (eng)Descriptors: Dataprocessing

Econometrics - Computer programs

Econometrie

SoftwareClass number: 519.50285 Abstract: Integrating a contemporary approach to econometrics with the powerful computational tools offered by Stata, An Introduction to Modern Econometrics Using Stata focuses on the role of method-of-moments estimators, hypothesis testing, and specification analysis and provides practical examples that show how the theories are applied to real data sets using Stata. As an expert in Stata, the author successfully guides readers from the basic elements of Stata to the core econometric topics. He first describes the fundamental components needed to effectively use Stata. The book then covers the multiple linear regression model, linear and nonlinear Wald tests, constrained least-squares estimation, Lagrange multiplier tests, and hypothesis testing of nonnested models. Subsequent chapters center on the consequences of failures of the linear regression model's assumptions. The book also examines indicator variables, interaction effects, weak instruments, underidentification, and generalized method-of-moments estimation. The final chapters introduce panel-data analysis and discrete- and limited-dependent variables and the two appendices discuss how to import data into Stata and Stata programming. Presenting many of the econometric theories used in modern empirical research, this introduction illustrates how to apply these concepts using Stata. The book serves both as a supplementary text for undergraduate and graduate students and as a clear guide for economists and financial analysts. Contents note: Notation and Typography introduction; Working with economic and financial data in stata; Organizing and handling economic data; Linear regression; Specifying the functional form; Regression with non-i.i.d. errors; Regression with indicator variables; Instrumental-variables estimators; Panel-data models; Models of discrete and limited dependent variables; Appendix a: Getting the data into stata; The basics of stata programming. Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=15450 ## Copies

Barcode Call number Media type Location Section Status 1702-001102 519.502 Bau-int 2006 General Collection Library "Max van der Stoel" English Not for loanMicroeconometrics Using Stata / A. Colin Cameron

Title : Microeconometrics Using Stata Material Type: printed text Authors: A. Colin Cameron, Author ; Pravin K. Trivedi, Author Publisher: Stata Press Publication Date: 2010 Pagination: xxxiv, 706 p. Size: 24 cm ISBN (or other code): 978-1-597-18073-3 Price: 60 $ General note: Includes glossary (p. [675]-677)

Includes bibliographical references (p. [678]-686)

Includes index (p. [689]-706)Languages : English ( eng) Original Language : English (eng)Descriptors: Econometric models

Econometrics - Computer programs

MicroeconomicsClass number: 338.5 Abstract: A complete and up-to-date survey of microeconometric methods available in Stata, "Microeconometrics Using Stata, Revised Edition" is an outstanding introduction to microeconometrics and how to execute microeconometric research using Stata. It covers topics left out of most microeconometrics textbooks and omitted from basic introductions to Stata. This revised edition has been updated to reflect the new features available in Stata 11 that are useful to microeconomists. Instead of using mfx and the user-written marge ff commands, the authors employ the new margins command, emphasizing both marginal effects at the means and average marginal effects. They also replace the xi command with factor variables, which allow you to specify indicator variables and interaction effects. Along with several new examples, this edition presents the new gmm command for generalized method of moments and nonlinear instrumental-variables estimation. In addition, the chapter on maximum likelihood estimation incorporates enhancements made to ml in Stata 11. Throughout the book, the authors use simulation methods to illustrate features of the estimators and tests described and provide an in-depth Stata example for each topic discussed. They also show how to use Stata's programming features to implement methods for which Stata does not have a specific command. The unique combination of topics, intuitive introductions to methods, and detailed illustrations of Stata examples make this book an invaluable, hands-on addition to the library of anyone who uses microeconometric methods. Contents note: Stata Basics; Interactive use; Documentation; Command syntax and operators; Do-files and log files; Scalars and matrices; Using results from; Stata commands; Global and local macros; Looping commands; Some useful commands; Template do-file; User-written commands; Data Management and Graphics; Introduction Types of data; Inputting data; Data management; Manipulating data sets; Graphical display of data; Linear Regression; Basics Introduction; Data and data summary; Regression in levels and logs; Basic regression analysis; Specification analysis; Prediction Sampling weights OLS using Mata Simulation; Introduction Pseudorandom-number generators: Introduction Distribution of the sample mean; Pseudorandom-number generators: Further details Computing integrals; Simulation for regression: Introduction GLS Regression; Introduction GLS and FGLS regression; Modeling heteroskedastic data System of linear regressions Survey data: Weighting, clustering, and stratification Linear; Instrumental-Variables; Regression; Introduction; IV estimation; IV example; Weak instruments; Better inference with weak instruments; 3SLS systems estimation; Quantile regression; Introduction; QR QR for medical expenditures data; QR for generated heteroskedastic data; QR for count data; Linear Panel-Data Models: Basics; Introduction; Panel-data methods overview; Panel-data summary; Pooled or population-averaged estimators; Within estimator; Between estimator RE estimator; Comparison of estimators First-difference estimator; Long panels; Panel-data management; Linear Panel-Data Models: Extensions; Introduction; Panel IV estimation Hausman-Taylor estimator; Arellano-Bond estimator; Mixed linear models; Clustered data; Nonlinear regression methods; Introduction; Nonlinear example: Doctor visits Nonlinear regression methods Different estimates of the VCE; Prediction; Marginal effects; Model diagnostics; Nonlinear optimization methods; Introduction; Newton-Raphson method; Gradient methods; The ml command: lf method; Checking the program; The ml command: d0, d1, d2, lf0, lf1, and lf2 methods; The Mata optimize() function; Generalized method of moments; Testing Methods; Introduction; Critical values and p-values; Wald tests and confidence intervals; Likelihood-ratio tests; Lagrange multiplier test (or score test); Test size and power; Specification tests; Bootstrap methods; Introduction Bootstrap methods; Bootstrap pairs using the vce(bootstrap) option; Bootstrap pairs using the bootstrap command; Bootstraps with asymptotic refinement; Bootstrap pairs using bsample and simulate; Alternative re-sampling schemes; The jackknife; Binary Outcome Models; Introduction; Some parametric models; Estimation; Example; Hypothesis and specification tests; Goodness of fit and prediction; Marginal effects; Endogenous regressors; Grouped data; Multinomial models; Introduction; Multinomial models overview; Multinomial example: Choice of fishing mode; Multinomial logit model; Conditional logit model; Nested logit model; Multinomial probit model Random-parameters logit; Ordered outcome models; Multivariate outcomes; Tobit and selection models; Introduction; Tobit model; Tobit model example; Tobit for lognormal data; Two-part model in logs; Selection model; Prediction from models with outcome in logs; Count-Data models; Introduction; Features of count data; Empirical example 1 Empirical example 2 Models with endogenous regressions; Nonlinear panel models; Introduction; Nonlinear panel-data overview; Nonlinear panel-data example; Binary outcome models; Tobit model; Count-data models; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=15451 ## Hold

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Barcode Call number Media type Location Section Status 1702-001109 338.5 Cam-Mic 2010 General Collection Library "Max van der Stoel" English Available