Comments from the Stata technical group
Using 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 how 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
1.4.2 Data definition files

1.5 Opening Stata data files

1.5.1 The use command
1.5.2 Using the toolbar
1.5.3 Using files on the Internet
1.5.4 Locating POE files on the Internet
1.6 The variables window
1.7 Describing the data and obtaining summary statistics
1.8 The Stata help system
1.8.1 Using keyword search
1.8.2 Using command search
1.8.3 Opening a dialog box

1.9 Stata commands syntax
1.9.1 Syntax of summarize
1.9.2 Learning syntax using the review window
1.10 Saving your work
1.10.1 Copying and pasting
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
1.11 Using the data browser
1.12 Using Stata graphics

1.12.1 Histograms
1.12.2 Scatter diagrams

1.13 Using Stata do-files
1.14 Creating and managing variables

1.14.1 Creating (generating) new variables
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 Using Stata density functions

1.15.1 Cumulative distribution functions
1.15.2 Inverse cumulative distribution functions
1.16 Using and displaying scalars

1.16.1 Example of standard normal cdf
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

1.17 A scalar dialog box
Key terms
Chapter 1 Do-file

Chapter 2   Simple linear regression
2.1 The flood expenditure data
2.1.1 Starting a new problem
2.1.2 Starting a log file
2.1.3 Opening a Stata data file
2.1.4 Browsing and listing the data

2.2 Computing summary statistics
2.3 Creating a scatter diagram
2.3.1 Enhancing the plot

2.4 Regression

2.4.1 Fitted values and residuals
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

2.5 Using Stata to obtain predicted values
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
3.1.2 Creating an interval estimate

3.2 Hypothesis tests

3.2.1 Right tail test of significance
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 P-values

3.3.1 P-value test of a right tail test
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

Key terms
Chapter 3 Do-file

Chapter 4   Prediction, Goodness-of-Fit and Modeling Issues
4.1 Least squares prediction
4.1.1 Editing the data
4.1.2 Estimate the regression and obtain post-estimation results
4.1.3 Creating the prediction interval
4.2 Measuring goodness-of-fit
4.2.1 Correlations and R2
4.3 The effects of scaling and transforming the data
4.3.1 The reciprocal functional form
4.3.2 Editing graphs
4.3.3 The linear-log model

4.4 Analyzing the residuals

4.4.1 The Jarque-Bera test
4.4.2 Chi-square distribution critical values
4.4.3 Chi-square distribution p-values
4.5 Another empirical example
4.5.1 Examining the data
4.5.2 Estimating and checking the linear relationship
4.5.3 Estimating and checking a cubic equation

4.6 Estimating a log-linear wage equation
4.6.1 The log-linear model
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
Key Terms
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
6.4.2 Optimal advertising

6.5 Nonsample information
6.6 Model specification

6.6.1 Omitted variables
6.6.2 Irrelevant variables
6.6.3 Choosing the model

6.7 Poor data, collinearity and insignificance
Key Terms
Chapter 6 Do-File
Chapter 7   Nonlinear Relationships
7.1 Nonlinear Relationships

7.1.1 Summarize data and estimate regression
7.1.2 Calculate marginal effect
7.1.3 Plotting wage-experience profile

7.2 Dummy variables

7.2.1 Creating 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 Applying dummy variables

7.3.1 Interactions between qualitative factors
7.3.2 Adding regional dummies
7.3.3 Testing the equivalence of two regressions
7.3.4 Estimating separate regressions
7.4 Interactions between continuous variables
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
8.3.2 Estimating the variance function
8.3.3 A Heteroskedastic partition
8.4 Detecting Heteroskedasticity

8.4.1 Residual plots
8.4.2 The Goldfeld-Quandt test
8.4.3 Testing the variance function

8.4.3a The White test
Key Terms
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
9.3.2 Nonlinear least squares
9.3.3 A more general model

9.4 Detecting autocorrelation
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
10.2.2 IV estimation in one step
10.2.3 IV estimation with surplus instruments

10.3 The Hausman test: simulated data
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
10.6.2 Two-stage least squares
10.6.3 Instrumental variables
10.6.4 Instrumental variables estimation with surplus instruments
10.7 Testing the endogeneity of education
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
14.3.2 Threshold GARCH
14.3.3 GARCH-in-mean

Key Terms
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

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