List of Dummies In Stata E-book

Download Business & Economics, Mathematics, Psychology ebook, Stata Press, Christopher F. Baum, Christopher F. (Boston College Baum, Chestnut Hill Massachusetts USA).

An Introduction to Modern Econometrics Using Stata
by Christopher F. Baum, Christopher F. (Boston College Baum, Chestnut Hill Massachusetts USA)

Table of Contents ” REFACE NOTATION AND TYPOGRAPHY INTRODUCTION An Overview of Stata’s Distinctive Features Installing the Necessary Software Installing the Support Materials WORKING WITH ECONOMIC AND FINANCIAL DATA IN STATA The Basics Common Data Transformations ORGANIZING AND HANDLING ECONOMIC DATA Cross-Sectional Data and Identifier Variables Time-Series Data Pooled Cross-Sectional Time-Series Data Panel Data Tools for Manipulating Panel Data Combining Cross-Sectional and Time-Series Datasets Creating Long-Format Datasets with Append The Reshape Command Using Stata for Reproducible Research LINEAR REGRESSION Introduction Computing Linear Regression Estimates Interpreting Regression Estimates Presenting Regression Estimates Hypothesis Tests, Linear Restrictions, and Constrained Least Squares Computing Residuals and Predicted Values Computing Marginal Effects Appendix A: Regression as a Least-Squares Estimator Appendix B: The Large-Sample VCE for Linear Regression SPECIFYING THE FUNCTIONAL FORM Introduction Specification Error Endogeneity and Measurement Error REGRESSION WITH NON-I.I.D. ERRORS The Generalized Linear Regression Model Heteroskedasticity in the Error Distribution Serial Correlation in the Error Distribution REGRESSION WITH INDICATOR VARIABLES Testing for Significance of a Qualitative Factor Regression with Qualitative and Quantitative Factors Seasonal Adjustment with Indicator Variables Testing for Structural Stability and Structural Change INSTRUMENTAL-VARIABLES ESTIMATORS Introduction Endogeneity in Economic Relationships 2SLS The ivreg Command Identification and Tests of Overidentifying Restrictions Computing IV Estimates ivreg2 and GMM Estimation Testing and Overidentifying Restrictions in GMM Testing for Heteroskedasticity in the IV Context Testing the Relevance of Instruments Durbin-Wu-Hausman Tests for Endogeneity in IV Estimation Appendix A: Omitted-Variables Bias Appendix B: Measurement Error PANEL-DATA MODELS FE and RE Models IV Models for Panel Data Dynamic Panel-Data Models Seemingly Unrelated Regression Models Moving-Window Regression Estimates MODELS OF DISCRETE AND LIMITED DEPENDENT VARIABLES Binomial Logit and Probit Models Ordered Logit and Probit Models Truncated Regression and Tobit Models Incidental Truncation and Sample-Selection Models Bivariate Probit and Probit with Selection APPENDIX A: GETTING THE DATA INTO STATA Inputting Data from ASCII Text Files and Spreadsheets Importing Data from Other Package Formats APPENDIX B: THE BASICS OF STATA PROGRAMMING Local and Global Macros Scalars Loop Constructs Matrices return and ereturn The Program and Syntax Statements Using Mata Functions in Stata Programs REFERENCES AUTHOR INDEX SUBJECT INDEX.

Statistics with STATA
by Lawrence C. Hamilton

For students and practicing researchers alike, STATISTICS WITH STATA opens the door to the full use of the popular Stata program a fast, flexible, and easy-to-use environment for data management and statistics analysis. Now integrating Stata’s impressive new graphics, this comprehensive book presents hundreds of examples showing how you can apply Stata to accomplish a wide variety of tasks. Like Stata itself, STATISTICS WITH STATA will make it easier for you to move fluidly through the world of modern data analysis.
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Multilevel and Longitudinal Modeling Using Stata, Second Edition
by Sophia Rabe-Hesketh, Anders Skrondal

Multilevel and Longitudinal Modeling Using Stata, Second Edition discusses regression modeling of clustered or hierarchical data, such as data on students nested in schools, patients in hospitals, or employees in firms. Longitudinal data are also clustered with, for instance, repeated measurements on patients or several panel waves per survey respondent. Multilevel and longitudinal modeling can exploit the richness of such data and can disentangle processes operating at different levels.

Assuming some knowledge of linear regression, this bestseller explains models and their assumptions, applies methods to real data using Stata, and shows how to interpret the results. The applications and exercises span a wide range of disciplines, making the book suitable for courses on multilevel and longitudinal modeling in the medical, social, and behavioral sciences and in applied statistics. This extensively revised second edition includes 3 new chapters, comprehensive updates for Stata 10, 38 new exercises, and 27 new data sets.

The authors teach multilevel and longitudinal modeling at their universities and frequently hold workshops at international conferences. They have been developing a general modeling framework, GLLAMM, and Stata software gllamm for multilevel and latent variable modeling. This work has been published in their highly acclaimed book Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models and in many journals, including Biometrics, Psychometrika, Journal of Econometrics, and Journal of the Royal Statistical Society.


Data Analysis Using Stata
by Ulrich Kohler, Frauke Kreuter

Data Analysis Using Stata provides a comprehensive introduction to Stata with an emphasis on data management, linear regression, logistic modeling, and using programs to automate repetitive tasks. Throughout the book, the authors make extensive use of examples using data from the German Socioeconomic Panel, a large survey of households containing demographic, income, employment, and other key information.

The book begins with an introduction to the Stata interface and then proceeds with a discussion of Stata syntax and simple programming tools like foreach loops. The core of the book includes chapters on producing tables and graphs, performing linear regression, and using logistic regression. The remainder of the book includes chapters on reading text files, writing programs and ado-files, and Internet resources, such as the search command and the SSC archive. All key concepts are illustrated with multiple examples.

Data Analysis Using Stata will serve as a valuable introduction to Stata, both for those who are new to statistics and statistical computing as well as for those new to Stata but familiar with other programs.


Econometrics For Dummies
by Roberto Pedace

Score your highest in econometrics? Easy.

Econometrics can prove challenging for many students unfamiliar with the terms and concepts discussed in a typical econometrics course. Econometrics For Dummieseliminates that confusion with easy-to-understand explanations of important topics in the study of economics.

Econometrics For Dummiesbreaks down this complex subject and provides you with an easy-to-follow course supplement to further refine your understanding of how econometrics works and how it can be applied in real-world situations.

  • An excellent resource for anyone participating in a college or graduate level econometrics course
  • Provides you with an easy-to-follow introduction to the techniques and applications of econometrics
  • Helps you score high on exam day

If you’re seeking a degree in economics and looking for a plain-English guide to this often-intimidating course, Econometrics For Dummies has you covered.


A Stata® Companion to Political Analysis
by Philip H. Pollock III

With Philip Pollock’s Third Edition of A Stata Companion to Political Analysis, students quickly learn Stata via step-by-step instruction, more than 50 exercises, customized datasets, annotated screen shots, boxes that highlight Stata’s special capabilities, and guidance on using Stata to read raw data. This attractive and value-priced workbook, an ideal complement to Pollock’s Essentials of Political Analysis, is a must-have for any political science student working with Stata.

Statistics with Stata 5
by Lawrence C. Hamilton

This book is an excellent tool for anyone using, or considering using Stata 5 to do statistical analysis for social science, economics or biostatistical research. Users will appreciate Hamilton’s excellent tutorials and plentiful real-world examples. Early chapters cover topics that correspond to a first course in statistics: data entry, elementary univariate and bivariate techniques, and multiple regression. Later chapters include essential tools for doing original research, such as regression diagnostics, survival analysis and Monte Carlo and bootstrap methods. This book also offers:
— Examples illustrated with output, graphs, and other figures
— Greatly expanded coverage of data management, reflecting on “Stata’s” improvements in this area and its importance to data analysis
— Two entirely new chapters: Chapter I I on survival analysis and Chapter 13 on programming
— New Internet resources, notably “Stata’s” website, technical support via e-mail, and Statalist (the independent discussion forum)

A Stata companion to political analysis
by Philip H. Pollock

With a robust statistical package and eye-catching graphics Stata is fast becoming the statistical program of choice for political scientists. For speed, versatility, comprehensiveness, and ease-of-use, Stata is hard to beat. It can be a little daunting for beginning students, but no longer. Philip Pollocks accessible introduction to the program offers students a step-by-step tutorial, leading them through descriptive statistics, cross-tabulation analysis, mean comparisons, linear correlation and regression (including dummy variables and interaction effects), and logistic regressionall tailored to political science material. With over 40 carefully crafted exercises and generous use of annotated screenshots, students will be navigating Statas graphics routines in no time. XX feature boxes highlight some of Statas special capabilities while a concluding chapter provides guidance on how to set up a research project, as well as how to use Stata to read raw data.

Measuring the burden of disease and returns to education in rural West Africa
by Ralph P. Würthwein

The success of health economics and its guidance for health policy heavily rests on the availability of reliable empirical evidence on the demographic, economic, and epidemiological environment, on behavioral relationships, and on the impact of policy interventions. For Sub-Saharan Africa, especially the epidemiological situation is unclear, since comprehensive systems of mortality and health statistics are often absent.The economic analysis of health naturally places a special focus on the interrelation between health and economic well-being: the overall disease burden decreases when a country grows richer, and the share of communicable diseases decreases in the process of economic development, whereas the share of non-communicable diseases increases. In those parts of Sub-Saharan Africa that are mainly dominated by traditional subsistence farming, however, it is difficult to examine questions of income and health for simple fundamental reasons. A vital prerequisite for an empirical investigation is the thorough and accurate measurement of income. Yet, both the measurement of the burden of disease and the measurement of income are research tasks that are far from being fulfilled for Sub-Saharan Africa. A further issue that is related with economic well-being and health is education. For poor rural regions predominated by traditional subsistence farming it is far from clear whether investments in human capital are worthwhile.The present study addesses this research gap by producing empirical evidence on the measurement of the burden of disease, the structure of income, and returns to education in rural West Africa. Concretely it deals with the collection and analysis of mortality, morbidity, and socio-economic data in the Nouna Health District in the North-West of Burkina Faso. The study was accepted as a doctoral thesis at the University of Heidelberg. Earlier versions of some of its chapters have been published as working papers or in international journals.