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Using Stata for Principles of Econometrics, 3rd Edition
Comments from the Stata technical groupUsing Stata for Principles of Econometrics, Third Edition, by Lee C. Adkins and R. Carter Hill, is a companion to the introductory econometrics textbook Principles of Econometrics, Third Edition. Adkins and Hill provide a quick introduction to using Stata’s menu system and command line before moving on to their many examples. Because the main textbook uses a learning-by-doing approach, this companion book is especially useful to emphasize more “doing”. Using Stata for Principles of Econometrics, Third Edition, shows how to use Stata to reproduce examples from the main textbook and to interpret the output. The authors also provide important information needed to perform and understand econometric analyses. Table of contents Chapter 1 Introducing Stata
1.1 Starting Stata
1.2 The opening display 1.3 Exiting Stata 1.4 Stata data files for Principles of Econometrics
1.4.1 A working directory
1.5 Opening Stata data files 1.4.2 Data definition files
1.5.1 The use command
1.6 The variables window 1.5.2 Using the toolbar 1.5.3 Using files on the Internet 1.5.4 Locating POE files on the Internet 1.7 Describing the data and obtaining summary statistics 1.8 The Stata help system
1.8.1 Using keyword search
1.9 Stata commands syntax
1.8.2 Using command search 1.8.3 Opening a dialog box
1.9.1 Syntax of summarize
1.10 Saving your work
1.9.2 Learning syntax using the review window
1.10.1 Copying and pasting
1.11 Using the data browser 1.10.2 Using a log file 1.10.3 Viewing a log file 1.10.4 Translating a log file to a text file 1.10.5 Using Stata commands for log files 1.12 Using Stata graphics
1.12.1 Histograms
1.13 Using Stata do-files 1.12.2 Scatter diagrams 1.14 Creating and managing variables
1.14.1 Creating (generating) new variables
1.15 Using Stata density functions 1.14.2 Using the expression builder 1.14.3 Dropping or renaming a variable 1.14.3 Using arithmetic operators 1.14.5 Using Stata math functions
1.15.1 Cumulative distribution functions
1.16 Using and displaying scalars 1.15.2 Inverse cumulative distribution functions
1.16.1 Example of standard normal cdf
1.17 A scalar dialog box 1.16.2 Example of t-distribution tail-cdf 1.16.3 Example of computing percentile of the standard normal 1.16.4 Example of computing percentile of the t-distribution Key terms Chapter 1 Do-file Chapter 2 Simple linear regression
2.1 The flood expenditure data
2.1.1 Starting a new problem
2.2 Computing summary statistics 2.1.2 Starting a log file 2.1.3 Opening a Stata data file 2.1.4 Browsing and listing the data 2.3 Creating a scatter diagram
2.3.1 Enhancing the plot
2.4 Regression
2.4.1 Fitted values and residuals
2.5 Using Stata to obtain predicted values 2.4.2 Computing an elasticity 2.4.3 Plotting the fitted regression line 2.4.4 Estimating the variance of the error term 2.4.5 Viewing estimated variances and covariances 2.6 Saving the Stata data file and ending the session Key Terms Chapter 2 Do-file Chapter 3 Interval Estimation and Hypotheses Testing
3.1 Interval estimates
3.1.1 Critical values from the t-distribution
3.2 Hypothesis tests 3.1.2 Creating an interval estimate
3.2.1 Right tail test of significance
3.3 P-values 3.2.2 Right tail test of an economic hypothesis 3.2.3 Left tail test of an economic hypothesis 3.2.4 Two tail test of an economic hypothesis
3.3.1 P-value test of a right tail test
Key terms 3.3.2 P-value test of a left tail test 3.3.3 P-value test of a two tail test 3.3.4 P-values in Stata output Chapter 3 Do-file Chapter 4 Prediction, Goodness-of-Fit and Modeling Issues
4.1 Least squares prediction
4.1.1 Editing the data
4.2 Measuring goodness-of-fit
4.1.2 Estimate the regression and obtain post-estimation results 4.1.3 Creating the prediction interval
4.2.1 Correlations and R2
4.3 The effects of scaling and transforming the data
4.3.1 The reciprocal functional form
4.4 Analyzing the residuals 4.3.2 Editing graphs 4.3.3 The linear-log model
4.4.1 The Jarque-Bera test
4.5 Another empirical example
4.4.2 Chi-square distribution critical values 4.4.3 Chi-square distribution p-values
4.5.1 Examining the data
4.6 Estimating a log-linear wage equation
4.5.2 Estimating and checking the linear relationship 4.5.3 Estimating and checking a cubic equation
4.6.1 The log-linear model
Key Terms 4.6.2 Calculating wage predictions 4.6.3 Constructing wage plots 4.6.4 Generalized R2 4.6.5 Prediction intervals in the log-linear model Chapter 4 Do-file Chapter 5 Multiple Linear Regression
5.1 Big Andy’s burger barn
5.2 Prediction 5.3 Sampling precision 5.4 Confidence intervals 5.5 Hypothesis tests 5.6 Goodness-of-fit Key Terms Chapter 5: Do-file Chapter 6 Further Inference in the Multiple Regression Model
6.1 The F-test
6.2 Testing the significance of the model 6.3 An extended model 6.4 Testing some economic hypotheses
6.4.1 Significance of advertising
6.5 Nonsample information 6.4.2 Optimal advertising 6.6 Model specification
6.6.1 Omitted variables
6.7 Poor data, collinearity and insignificance 6.6.2 Irrelevant variables 6.6.3 Choosing the model Key Terms Chapter 6 Do-File Chapter 7 Nonlinear Relationships
7.1 Nonlinear Relationships
7.1.1 Summarize data and estimate regression
7.2 Dummy variables 7.1.2 Calculate marginal effect 7.1.3 Plotting wage-experience profile
7.2.1 Creating dummy variables
7.3 Applying dummy variables 7.2.2 Using tabulate 7.2.3 Estimating a dummy variable regression 7.2.4 Testing the significance of the dummy variables 7.2.5 Further calculations
7.3.1 Interactions between qualitative factors
7.4 Interactions between continuous variables 7.3.2 Adding regional dummies 7.3.3 Testing the equivalence of two regressions 7.3.4 Estimating separate regressions 7.5 Dummy variables in log-linear models Key Terms Chapter 7 Do-file Chapter 8 Heteroskedasticity
8.1 The nature of heteroskedasticity
8.2 Using the least squares estimator 8.3 The generalized least squares estimator
8.3.1 Transforming the model
8.4 Detecting Heteroskedasticity 8.3.2 Estimating the variance function 8.3.3 A Heteroskedastic partition
8.4.1 Residual plots
Key Terms 8.4.2 The Goldfeld-Quandt test 8.4.3 Testing the variance function
8.4.3a The White test
Chapter 8 Do-file Chapter 9 Dynamic Models, Autocorrelation, and Forecasting
9.1 Lags in the error term: autocorrelation
9.2 Area response for sugar 9.3 Estimating an AR(1) model
9.3.1 Least squares and HAC standard errors
9.4 Detecting autocorrelation 9.3.2 Nonlinear least squares 9.3.3 A more general model 9.5 Autoregressive models 9.6 Finite distributed lags 9.7 Autoregressive distributed lag models Appendix Key Terms Chapter 9 Do-file Chapter 10 Random Regressors and Moment Based Estimation
10.1 Least squares with simulated data
10.2 Instrumental variables estimation with simulated data
10.2.1 IV estimation in two steps
10.3 The Hausman test: simulated data 10.2.2 IV estimation in one step 10.2.3 IV estimation with surplus instruments 10.4 Testing for weak instruments: simulated Stata 10.5 Testing the validity of surplus instruments 10.6 Estimation using the Mroz data
10.6.1 Least squares regression
10.7 Testing the endogeneity of education 10.6.2 Two-stage least squares 10.6.3 Instrumental variables 10.6.4 Instrumental variables estimation with surplus instruments 10.8 Testing for weak instruments 10.9 Testing the validity of surplus instruments Key Terms Chapter 10 Do-file Chapter 11 Simultaneous Equations Models
11.1 Truffle supply and demand
11.2 Estimating the reduced form equations 11.3 2SLS estimates of truffle demand 11.4 2SLS estimates of truffle supply 11.5 Supply and demand of fish 11.6 Reduced forms for fish price and quantity Chapter 12 Nonstationary Time Series Data and Cointegration
12.1 Stationary and nonstationary data
12.2 Spurious regressions 12.3 Unit root tests for stationarity 12.4 Integration and cointegration 12.5 Engle-Granger test Key Terms Chapter 12 Do-file Chapter 13 An Introduction to Macroeconometrics: VEC and VAR Models
13.1 VEC and VAR models
13.2 Estimating a VEC model 13.3 Estimating a VAR 13.4 Impulse responses and variance decompositions Key Terms Chapter 13 Do-file Chapter 14 An Introduction to Financial Econometrics: Time-Varying Volatility and ARCH models
14.1 ARCH model and time-varying volatility
14.2 Testing, estimating, and forecasting 14.3 Extensions
14.3.1 GARCH
Key Terms 14.3.2 Threshold GARCH 14.3.3 GARCH-in-mean Chapter 14 Do-file Chapter 15 Panel Data models
15.1 Sets of regression equations
15.2 Seemingly unrelated regression 15.3 The fixed effects model
15.3.1 A dummy variable
15.4 Random effects estimation 15.3.2 The fixed effects estimator 15.3.3 The fixed effects estimator for a microeconometric panel
15.4.1 Breusch–Pagan test
Key Terms 15.4.2 Hausman test Chapter 15 Do-file Chapter 16 Qualitative and Limited Dependent Variable Models
16.1 Models with binary dependent variables
16.2 Multinomial logit 16.3 Conditional logit
16.3.1 Release 9: clogit
16.4 Ordered choice models 16.3.2 Release 10: asclogit 16.5 Models for cont data 16.6 Censored data models
16.6.1 Simulated data example
16.7 Selection bias 16.6.2 Mroz data example Key Terms Chapter 16 Do-file Appendix A Review of Math Essentials
A.1 Stata math and logical operators
A.2 Math functions A.3 Extensions to generate operations A.4 The calculator A.5 Scientific notation Key Terms Appendix B Review of Probability
B.1 Stata probability functions
B.2 Binomial probability functions B.3 Normal probability calculations B.4 t-distribution probability calculations B.5 F-distribution probability calculations B.6 Chi-square distribution probability calculations Key Terms Appendix B Do-file Appendix C Review of Statistical Inference
C.1 Examining the hip data
C.1.1 Constructing a histogram
C.2 Using simulated data values C.1.2 Obtaining summary statistics C.1.3 Estimating the population mean C.3 The central limit theorem C.4 Interval estimation
C.4.1 Using simulated data
C.5 Testing the mean of a normal population C.4.2 Using the hip data
C.5.1 Right rail test
C.6 Testing the variance of a normal population C.5.2 Two tail test C.7 Testing the equality of two normal population means
C.7.1 Population variances are equal
C.8 Testing the equality of two normal population variances C.7.2 Population variances are unequal C.9 Testing normality C.10 Maximum likelihood estimation Key Terms Appendix C Do-file Index
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