Specifying the functional form -- 6. Regression with non-i. Regression with indicator variables -- 8. Instrumental-variables estimators -- 9.
Panel-data models -- Models of discrete and limited dependent variables -- A. Getting the data into Stata -- B. The basics of Stata programming. It serves as a basic text for those who wish to learn and apply econometric analysis in empirical research.
The level of presentation is as simple as possible to make it useful for undergraduates as well as graduate students. It contains several examples with real data and Stata programmes and interpretation of the results. While discussing the statistical tools needed to understand empirical economic research, the book attempts to provide a balance between theory and applied research.
Various concepts and techniques of econometric analysis are supported by carefully developed examples with the use of statistical software package, Stata The topics covered in this book are divided into four parts. Part I discusses introductory econometric methods for data analysis that economists and other social scientists use to estimate the economic and social relationships, and to test hypotheses about them, using real-world data.
There are five chapters in this part covering the data management issues, details of linear regression models, the related problems due to violation of the classical assumptions. Part II discusses some advanced topics used frequently in empirical research with cross section data.
In its three chapters, this part includes some specific problems of regression analysis. Part III deals with time series econometric analysis. The book presents a contemporary approach to econometrics, emphasizing the role of method-of-moments estimators, hypothesis testing, and specification analysis while providing practical examples showing how the theory is applied to real datasets by using Stata.
The first three chapters are dedicated to the basic skills needed to effectively use Stata: loading data into Stata; using commands like generate and replace , egen , and sort to manipulate variables; taking advantage of loops to automate tasks; and creating new datasets by using merge and append.
Baum succinctly yet thoroughly covers the elements of Stata that a user must learn to become proficient, providing many examples along the way. Chapter 4 begins the core econometric material of the book and covers the multiple linear regression model, including efficiency of the ordinary least-squares estimator, interpreting the output from regress , and point and interval prediction.
The chapter covers both linear and nonlinear Wald tests, as well as constrained least-squares estimation, Lagrange multiplier tests, and hypothesis testing of nonnested models. Chapter 5 addresses topics like omitted-variable bias, misspecification of functional form, and outlier detection. Chapter 7 is dedicated to the use of indicator variables and interaction effects. Instrumental-variables estimation has been an active area of research in econometrics, and chapter 8 commendably addresses issues like weak instruments, underidentification, and generalized method-of-moments estimation.
In this chapter, Baum extensively uses his wildly popular ivreg2 command. The last two chapters briefly introduce panel-data analysis and discrete and limited-dependent variables.
Two appendices detail importing data into Stata and Stata programming. As in all chapters, Baum presents many Stata examples. The book is also useful to economists and businesspeople wanting to learn Stata by using practical examples. This book provides an excellent resource for both teaching and learning modern microeconometric practice, using the most popular software package in this area.
The coverage includes discrete choice models and models for panel data, as well as linear regression and instrumental variables methods.
I particularly like the material on handling large datasets and developing efficient programs within Stata, which provide the reader with an invaluable introduction to good practice in empirical research.
Kit Baum provides students and researchers a hands-on guide to modern econometric techniques by means of many well-documented examples in Stata. The examples are also useful templates for those who need to write Stata routines for their own work. Treatment and transformation of cross-section, time-series, and panel data are carefully explained. The coverage of the text is broad and up to date. An Introduction to Modern Econometrics Using Stata is a valuable companion to undergraduate- and graduate-level econometric textbooks.
Economic and financial consultants will find this text to be an invaluable guide to using Stata for creating reproducible, error-free data and econometric analysis, as well as quality graphic presentations.
This text should serve as an excellent learning and reference guide for every consultant. Zaur Rzakhanov, Ph. 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. Designed to assist those working in health research, An Introduction to Stata for Health Researchers, explains how to maximize the versatile Strata program for data management, statistical analysis, and graphics for research.
The first nine chapters are devoted to becoming familiar with Stata and the essentials of effective data management. The text is also a valuable companion reference for more advanced users. This is a book about applied multilevel and longitudinal modeling.
Other terms for multilevel models include hierarchical models, random-effects or random-coefficient models, mixed-effects models, or simply mixed models. Longitudinal data are also referred to as panel data, repeated measures, or cross-sectional time series. A popular type of multilevel model for longitudinal data is the growth-curve model. Our emphasis is on explaining the models and their assumptions, applying the methods to real data, and interpreting results.
With each new release of Stata, a comprehensive resource is needed to highlight the improvements as well as discuss the fundamentals of the software. This edition covers many new features of Stata, including a new command for mixed models and a new matrix language.
Each chapter describes the analysis appropriate for a particular application, focusing on the medical, social, and behavioral fields. The authors begin each chapter with descriptions of the data and the statistical techniques to be used.
The methods covered include descriptives, simple tests, variance analysis, multiple linear regression, logistic regression, generalized linear models, survival analysis, random effects models, and cluster analysis.
The core of the book centers on how to use Stata to perform analyses and how to interpret the results. The chapters conclude with several exercises based on data sets from different disciplines. A concise guide to the latest version of Stata, A Handbook of Statistical Analyses Using Stata, Fourth Edition illustrates the benefits of using Stata to perform various statistical analyses for both data analysis courses and self-study. Whether you are new to Stata graphics or a seasoned veteran, A Visual Guide to Stata Graphics, Second Edition will teach you how to use Stata to make publication-quality graphs that will stand out and enhance your statistical results.
With over illustrated examples and quick-reference tabs, this book quickly guides you to the information you need for creating and customizing high-quality graphs for any types of statistical data. After reading this introductory text, you will be able to enter, build, and manage a data set as well as perform fundamental statistical analyses. New to the Third Edition A new chapter on the analysis of missing data and the use of multiple-imputation methods Extensive revision of the chapter on ANOVA Additional material on the application of power analysis The book covers data management; good work habits, including the use of basic do-files; basic exploratory statistics, including graphical displays; and analyses using the standard array of basic statistical tools, such as correlation, linear and logistic regression, and parametric and nonparametric tests of location and dispersion.
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