Using Stata for Principles of Econometrics, Fourth Edition, by Lee C. Adkins and R. Carter Hill, is a companion to the introductory econometrics textbook Principles of Econometrics, Fourth Edition. Together, the two books provide a very good introduction to econometrics for undergraduate students and first-year graduate students.
The main textbook takes a learn-by-doing approach to econometric analysis, and this companion book illustrates the “doing” part using Stata. Adkins and Hill briefly show how to use Stata’s menu system and command line before delving into their many examples.
Using Stata for Principles of Econometrics, Fourth Edition shows how to use Stata to reproduce the examples in the main textbook and how to interpret the output. The current edition has been updated to include features introduced in Stata 11, such as the margins command to compute elasticities. Together with Principles of Econometrics, Fourth Edition, the reader will not only learn econometrics but also gain the confidence needed to perform his or her own work using Stata.
CHAPTER 1 INTRODUCING STATA
Starting Stata
The opening display
Exiting Stata
Stata data files for Principles of Econometrics
A working directory
Opening Stata data files
The use command
Using the toolbar
Using files on the Internet
Locating book files on the Internet
The variables window
Using the data editor for a single label
Using the data utility for a single label
Using variables manager
Describing data and obtaining summary statistics
The Stata help system
Using keyword search
Using command search
Opening a dialog box
Complete documentation in Stata manuals
Stata command syntax
Syntax of summarize
Learning syntax using the review window
Saving your work
Copying and pasting
Using a log file
Using the data browser
Using Stata graphics
Histograms
Scatter diagrams
Using Stata do-files
Creating and managing variables
Creating (generating) new variables
Using the expression builder
Dropping or keeping variables and observations
Using arithmetic operators
Using Stata math functions
Using Stata density functions
Cumulative distribution functions
Inverse cumulative distribution functions
Using and displaying scalars
Example of standard normal cdf
Example of t-distribution tail-cdf
Example of computing percentile of the standard normal
Example of computing percentile of the t-distribution
A scalar dialog box
Using factor variables
Creating indicator variables using a logical operator
Creating indicator variables using tabulate
Key terms
Chapter 1 do-file
CHAPTER 2 SIMPLE LINEAR REGRESSION
The food expenditure data
Starting a new problem
Starting a log file
Opening a Stata data file
Browsing and listing the data
Computing summary statistics
Creating a scatter diagram
Enhancing the plot
Regression
Fitted values and residuals
Computing an elasticity
Plotting the fitted regression line
Estimating the variance of the error term
Viewing estimated variances and covariances
Using Stata to obtain predicted values
Saving the Stata data file
Estimating nonlinear relationships
A quadratic model
A log-linear model
Regression with indicator variables
Appendix 2A Average marginal effects
Elasticity in a linear relationship
Elasticity in a quadratic relationship
Slope in a log-linear model
Appendix 2B A simulation experiment
Key terms
Chapter 2 do-file
CHAPTER 3 INTERVAL ESTIMATION AND HYPOTHESIS TESTING
Interval estimates
Critical values from the t-distribution
Creating an interval estimate
Hypothesis tests
Right-tail test of significance
Right-tail test of an economic hypothesis
Left-tail test of an economic hypothesis
Two-tail test of an economic hypothesis
p-values
p-value of a right-tail test
p-value of a left-tail test
p-value for a two-tail test
p-values in Stata output
Testing and estimating linear combinations of parameters
Appendix 3A Graphical tools
Appendix 3B Monte Carlo simulation
Key terms
Chapter 3 do-file
CHAPTER 4 PREDICTION, GOODNESS-OF-FIT AND MODELING ISSUES
Least squares prediction
Editing the data
Estimate the regression and obtain postestimation results
Creating the prediction interval
Measuring goodness-of-fit
Correlations and R2
The effects of scaling and transforming the data
The linear-log functional form
Plotting the fitted linear-log model
Editing graphs
Analyzing the residuals
The Jarque-Bera test
Chi-square distribution critical values
Chi-square distribution p-values
Polynomial models
Estimating and checking the linear relationship
Estimating and checking a cubic equation
Estimating a log-linear yield growth model
Estimating a log-linear wage equation
The log-linear model
Calculating wage predictions
Constructing wage plots
Generalized R2
Prediction intervals in the log-linear model
A log-log model
Key terms
Chapter 4 do-file
CHAPTER 5 MULTIPLE LINEAR REGRESSION
Big Andy’s Burger Barn
Least squares prediction
Sampling precision
Confidence intervals
Confidence interval for a linear combination of parameters
Hypothesis tests
Two-sided tests
One-sided tests
Testing a linear combination
Polynomial equations
Optimal advertising: nonlinear combinations of parameters
Using factor variables for interactions
Interactions
Goodness-of-fit
Key terms
Chapter 5 do-file
CHAPTER 6 FURTHER INFERENCE IN THE MULTIPLE REGRESSION MODEL
The F-test
Testing the significance of the model
Relationship between t- and F-tests
More general F-tests
Nonsample information
Model specification
Omitted variables
Irrelevant variables
Choosing the model
Poor data, collinearity, and insignificance
Key terms
Chapter 6 do-file
CHAPTER 7 USING INDICATOR VARIABLES
Indicator variables
Creating indicator variables
Estimating an indicator variable regression
Testing the significance of the indicator variables
Futher calculations
Computing average marginal effects
Applying indicator variables
Interactions between qualitative factors
Adding regional indicators
Testing the equivalence of two regressions
Estimating separate regressions
Indicator variables in log-linear models
The linear probability model
Treatment effects
Differences-in-differences estimation
Key terms
Chapter 7 do-file
CHAPTER 8 HETEROSKEDASTICITY
The nature of heteroskedasticity
Detecting heteroskedasticity
Residual plots
Lagrange multiplier tests
The Goldfeld-Quandt test
Heteroskedastic-consistent standard errors
The generalized least squares estimator
GLS using grouped data
Feasible GLS–a more general case
Heteroskedasticity in the linear probability model
Key terms
Chapter 8 do-file
CHAPTER 9 REGRESSION WITH TIME-SERIES DATA: STATIONARY VARIABLES
Introduction
Defining time-series in Stata
Time-series plots
tata’s lag and difference operators
Finite distributed lags
Serial correlation
Other tests for serial correlation
Estimation with serially correlated errors
Least squares and HAC standard errors
Nonlinear least squares
A more general model
Autoregressive distributed lag models
Phillips curve
Okun’s law
Autoregressive models
Forecasting
Forecasting with an AR model
Exponential smoothing
Multiplier analysis
Appendix
Durbin-Watson test
Prais-Winsten FGLS
Key terms
Chapter 9 do-file
CHAPTER 10 RANDOM REGRESSORS AND MOMENT BASED ESTIMATION
Least squares estimation of a wage equation
Two-stage least squares
IV estimation with surplus instruments
Illustrating partial correlations
The Hausman test for endogeneity
Testing the validity of surplus instruments
Testing for weak instruments
Calculating the Cragg-Donald F-statistic
A simulation experiment
Key terms
Chapter 10 do-file
CHAPTER 11 SIMULTANEOUS EQUATIONS MODELS
Truffle supply and demand
Estimating the reduced form equations
2SLS estimates of truffle demand
2SLS estimates of truffle supply
Supply and demand of fish
Reduced forms for fish price and quantity
2SLS estimates of fish demand
2SLS alternatives
Monte Carlo simulation
Key terms
Chapter 11 do-file
CHAPTER 12 REGRESSION WITH TIME-SERIES DATA: NONSTATIONARY VARIABLES
Stationary and nonstationary data
Review: generating dates in Stata
Extracting dates
Graphing the data
Spurious regressions
Unit root tests for stationarity
Integration and cointegration
Engle-Granger test
Error-correction model
Key terms
Chapter 12 do-file
CHAPTER 13 VECTOR ERROR CORRECTION AND VECTOR AUTOREGRESSIVE MODELS
VEC and VAR models
Estimating a VEC model
Estimating a VAR
Impulse responses and variance decompositions
Key terms
Chapter 13 do-file
CHAPTER 14 TIME-VARYING VOLATILITY AND ARCH MODELS
ARCH model and time-varying volatility
Estimating, testing, and forecasting
Extensions
GARCH
T-GARCH
GARCH-in-mean
Key terms
Chapter 14 do-file
CHAPTER 15 PANEL DATA MODELS
A microeconomic panel
A pooled model
Cluster-robust standard errors
The fixed effects model
The fixed effects estimator
The fixed effects estimator using xtreg
Fixed effects using the complete panel
Random effects estimation
The GLS transformation
The Breusch-Pagan test
The Hausman test
The Hausman-Taylor model
Sets of regression equations
Seemingly unrelated regressions
SUR with wide data
Mixed models
Key terms
Chapter 15 do-file
CHAPTER 16 QUALITATIVE AND LIMITED DEPENDENT VARIABLE MODELS
Models with binary dependent variables
Average marginal effects
Probit marginal effects: details
Standard error of average marginal effect
The logit model for binary choice
Wald tests
Likelihood ratio tests
Logit estimation
Out-of-sample prediction
Multinomial logit
Conditional logit
Estimation using asclogit
Ordered choice models
Models for count data
Censored data models
Simulated data example
Mroz data example
Selection bias
Key terms
Chapter 16 do-file
APPENDIX A REVIEW OF MATH ESSENTIALS
Stata math and logical operators
Math functions
Extensions to generate
The calculator
Scientific notation
Numerical derivatives and integrals
Key terms
Appendix A do-file
APPENDIX B REVIEW OF PROBABILITY
Stata probability functions
Binomial distribution
Normal distribution
Normal density plots
Normal probability calculations
Student’s t-distribution
Plot of standard normal and t(3)
t-distribution probabilities
Graphing tail probabilities
F-distribution
Plotting the F-density
F-distribution probability calculations
Chi-square distribution
Plotting the chi-square density
Chi-square probability calculations
Random numbers
Using inversion method
Creating uniform random numbers
Key terms
Appendix B do-file
APPENDIX C REVIEW OF STATISTICAL INFERENCE
Examining the hip data
Constructing a histogram
Obtaining summary statistics
Estimating the population mean
Using simulated data values
The central limit theorem
Interval estimation
Using simulated data
Using the hip data
Testing the mean of a normal population
Right-tail test
Two-tail test
Testing the variance of a normal population
Testing the equality of two normal population means
Population variances are equal
Population variances are unequal
Testing the equality of two normal population variances
Testing normality
Maximum likelihood estimation
Kernel density estimator
Key terms
Appendix C do-file
