An Introduction to Modern Econometrics Using StataTable 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. |
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Contents
2 Working with economic and | 7 |
3 Organizing and handling economic | 43 |
4 Linear regression | 69 |
5 Specifying the functional form | 115 |
6 Regression with noniid errors | 133 |
7 Regression with indicator variables | 161 |
1990q1 1992q3 sal | 178 |
ci | 183 |
9 Paneldata models | 219 |
10 15 20 10 15 | 244 |
10 Models of discrete and limited | 247 |
A Getting the data into Stata | 277 |
B The basics of Stata programming | 289 |
| 321 | |
Author index | 329 |
8 Instrumentalvariables estimators | 185 |
Common terms and phrases
_cons 2SLS autocorrelation Cntrl Coef coefficients compute Conf consistent estimates constant term correlated dataset define df MS Model discussed display distribution disturbance process do-file e(sample econometrics endogenous regressors equation explanatory F R-squared factors FGLS format function heteroskedasticity homoskedasticity housing price indicator variables instance instruments integer Interval ivreg2 lagged large-sample likelihood-ratio test linear regression logit lwage macro marginal effects Mata matrix measure medage missing values null hypothesis Number of obs Obs Mean Std observations OLS estimator option P-value panel data parameters popsize population predicted values Prob probit probit model R-squared R-squared Adj R-squared R-squared Root MSE regression estimates regression model regressors residuals response variable robust sample scalar semean Source SS df specify standard errors Stata commands string variables summarize syntax tenure time-series variable name Variable Obs Mean variance varlist vector Wald test zero zero-conditional-mean assumption
References to this book
Bibliographic information
| Title | An Introduction to Modern Econometrics Using Stata Stata Press publication |
| Author | Christopher F. Baum |
| Edition | illustrated |
| Publisher | Stata Press, 2006 |
| ISBN | 1597180130, 9781597180139 |
| Length | 341 pages |
| Subjects | › › Business & Economics / Econometrics Business & Economics / Statistics Computers / Mathematical & Statistical Software Mathematics / Probability & Statistics / General Psychology / Research & Methodology |
| Export Citation | BiBTeX EndNote RefMan |