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 Microeconomics
| Class 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 |
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