Using Stata for Principles of Econometrics, Fourth Edition
Authors: 
Lee C. Adkins and R. Carter Hill 
Publisher: 
Wiley 
Copyright: 
2011 
ISBN13: 
9781118032084 
Pages: 
611; paperback 
Price: 
$74.50 



Comment from the Stata technical group
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 firstyear graduate students.
The main textbook takes a learnbydoing 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.
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
1.5.1 The use command
1.5.2 Using the toolbar
1.5.3 Using files on the Internet
1.5.4 Locating book files on the Internet
1.6 The variables window
1.6.1 Using the data editor for a single label
1.6.2 Using the data utility for a single label
1.6.3 Using variables manager
1.7 Describing 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
1.8.4 Complete documentation in Stata manuals
1.9 Stata command 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
1.11 Using the data browser
1.12 Using Stata graphics
1.12.1 Histograms
1.12.2 Scatter diagrams
1.13 Using Stata dofiles
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 keeping variables and observations
1.14.4 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 tdistribution tailcdf
1.16.3 Example of computing percentile of the standard normal
1.16.4 Example of computing percentile of the tdistribution
1.17 A scalar dialog box
1.18 Using factor variables
1.18.1 Creating indicator variables using a logical operator
1.18.2 Creating indicator variables using tabulate
Key terms
Chapter 1 dofile
Chapter 2 Simple linear regression
2.1 The food 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
2.5.1 Saving the Stata data file
2.6 Estimating nonlinear relationships
2.6.1 A quadratic model
2.6.2 A loglinear model
2.7 Regression with indicator variables
Appendix 2A Average marginal effects
2A.1 Elasticity in a linear relationship
2A.2 Elasticity in a quadratic relationship
2A.3 Slope in a loglinear model
Appendix 2B A simulation experiment
Key terms
Chapter 2 dofile
Chapter 3 Interval Estimation and Hypothesis Testing
3.1 Interval estimates
3.1.1 Critical values from the tdistribution
3.1.2 Creating an interval estimate
3.2 Hypothesis tests
3.2.1 Righttail test of significance
3.2.2 Righttail test of an economic hypothesis
3.2.3 Lefttail test of an economic hypothesis
3.2.4 Twotail test of an economic hypothesis
3.3 pvalues
3.3.1 pvalue of a righttail test
3.3.2 pvalue of a lefttail test
3.3.3 pvalue for a twotail test
3.3.4 pvalues in Stata output
3.3.5 Testing and estimating linear combinations of parameters
Appendix 3A Graphical tools
Appendix 3B Monte Carlo simulation
Key terms
Chapter 3 dofile
Chapter 4 Prediction, GoodnessofFit and Modeling Issues
4.1 Least squares prediction
4.1.1 Editing the data
4.1.2 Estimate the regression and obtain postestimation results
4.1.3 Creating the prediction interval
4.2 Measuring goodnessoffit
4.2.1 Correlations and R^{2}
4.3 The effects of scaling and transforming the data
4.3.1 The linearlog functional form
4.3.2 Plotting the fitted linearlog model
4.3.3 Editing graphs
4.4 Analyzing the residuals
4.4.1 The JarqueBera test
4.4.2 Chisquare distribution critical values
4.4.3 Chisquare distribution pvalues
4.5 Polynomial models
4.5.1 Estimating and checking the linear relationship
4.5.2 Estimating and checking a cubic equation
4.5.3 Estimating a loglinear yield growth model
4.6 Estimating a loglinear wage equation
4.6.1 The loglinear model
4.6.2 Calculating wage predictions
4.6.3 Constructing wage plots
4.6.4 Generalized R^{2}
4.6.5 Prediction intervals in the loglinear model
4.7 A loglog model
Key terms
Chapter 4 dofile
Chapter 5 Multiple Linear Regression
5.1 Big Andy’s Burger Barn
5.2 Least squares prediction
5.3 Sampling precision
5.4 Confidence intervals
5.4.1 Confidence interval for a linear combination of parameters
5.5 Hypothesis tests
5.5.1 Twosided tests
5.5.2 Onesided tests
5.5.3 Testing a linear combination
5.6 Polynomial equations
5.6.1 Optimal advertising: nonlinear combinations of parameters
5.6.2 Using factor variables for interactions
5.7 Interactions
5.8 Goodnessoffit
Key terms
Chapter 5 dofile
Chapter 6 Further Inference in the Multiple Regression Model
6.1 The Ftest
6.1.1 Testing the significance of the model
6.1.2 Relationship between t and Ftests
6.1.3 More general Ftests
6.2 Nonsample information
6.3 Model specification
6.3.1 Omitted variables
6.3.2 Irrelevant variables
6.3.3 Choosing the model
6.4 Poor data, collinearity, and insignificance
Key terms
Chapter 6 dofile
Chapter 7 Using Indicator Variables
7.1 Indicator variables
7.1.1 Creating indicator variables
7.1.2 Estimating an indicator variable regression
7.1.3 Testing the significance of the indicator variables
7.1.4 Futher calculations
7.1.5 Computing average marginal effects
7.2 Applying indicator variables
7.2.1 Interactions between qualitative factors
7.2.2 Adding regional indicators
7.2.3 Testing the equivalence of two regressions
7.2.4 Estimating separate regressions
7.2.5 Indicator variables in loglinear models
7.3 The linear probability model
7.4 Treatment effects
7.5 Differencesindifferences estimation
Key terms
Chapter 7 dofile
Chapter 8 Heteroskedasticity
8.1 The nature of heteroskedasticity
8.2 Detecting heteroskedasticity
8.2.1 Residual plots
8.2.2 Lagrange multiplier tests
8.2.3 The GoldfeldQuandt test
8.3 Heteroskedasticconsistent standard errors
8.4 The generalized least squares estimator
8.4.1 GLS using grouped data
8.4.2 Feasible GLS–a more general case
8.5 Heteroskedasticity in the linear probability model
Key terms
Chapter 8 dofile
Chapter 9 Regression with TimeSeries Data: Stationary Variables
9.1 Introduction
9.1.1 Defining timeseries in Stata
9.1.2 Timeseries plots
9.1.3 Stata's lag and difference operators
9.2 Finite distributed lags
9.3 Serial correlation
9.4 Other tests for serial correlation
9.5 Estimation with serially correlated errors
9.5.1 Least squares and HAC standard errors
9.5.2 Nonlinear least squares
9.5.3 A more general model
9.6 Autoregressive distributed lag models
9.6.1 Phillips curve
9.6.2 Okun's law
9.6.3 Autoregressive models
9.7 Forecasting
9.7.1 Forecasting with an AR model
9.7.2 Exponential smoothing
9.8 Multiplier analysis
9.9 Appendix
9.9.1 DurbinWatson test
9.9.2 PraisWinsten FGLS
Key terms
Chapter 9 dofile
Chapter 10 Random Regressors and Moment Based Estimation
10.1 Least squares estimation of a wage equation
10.2 Twostage least squares
10.3 IV estimation with surplus instruments
10.3.1 Illustrating partial correlations
10.4 The Hausman test for endogeneity
10.5 Testing the validity of surplus instruments
10.6 Testing for weak instruments
10.7 Calculating the CraggDonald Fstatistic
10.8 A simulation experiment
Key terms
Chapter 10 dofile
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
11.7 2SLS estimates of fish demand
11.8 2SLS alternatives
11.9 Monte Carlo simulation
Key terms
Chapter 11 dofile
Chapter 12 Regression with TimeSeries Data: Nonstationary Variables
12.1 Stationary and nonstationary data
12.1.1 Review: generating dates in Stata
12.1.2 Extracting dates
12.1.3 Graphing the data
12.2 Spurious regressions
12.3 Unit root tests for stationarity
12.4 Integration and cointegration
12.4.1 EngleGranger test
12.4.2 Errorcorrection model
Key terms
Chapter 12 dofile
Chapter 13 Vector Error Correction and Vector Autoregressive 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 dofile
Chapter 14 TimeVarying Volatility and ARCH Models
14.1 ARCH model and timevarying volatility
14.2 Estimating, testing, and forecasting
14.3 Extensions
14.3.1 GARCH
14.3.2 TGARCH
14.3.3 GARCHinmean
Key terms
Chapter 14 dofile
Chapter 15 Panel Data Models
15.1 A microeconomic panel
15.2 A pooled model
15.2.1 Clusterrobust standard errors
15.3 The fixed effects model
15.3.1 The fixed effects estimator
15.3.2 The fixed effects estimator using xtreg
15.3.3 Fixed effects using the complete panel
15.4 Random effects estimation
15.4.1 The GLS transformation
15.4.2 The BreuschPagan test
15.4.3 The Hausman test
15.4.4 The HausmanTaylor model
15.5 Sets of regression equations
15.5.1 Seemingly unrelated regressions
15.5.2 SUR with wide data
15.6 Mixed models
Key terms
Chapter 15 dofile
Chapter 16 Qualitative and Limited Dependent Variable Models
16.1 Models with binary dependent variables
16.1.1 Average marginal effects
16.1.2 Probit marginal effects: details
16.1.3 Standard error of average marginal effect
16.2 The logit model for binary choice
16.2.1 Wald tests
16.2.2 Likelihood ratio tests
16.2.3 Logit estimation
16.2.4 Outofsample prediction
16.3 Multinomial logit
16.4 Conditional logit
16.4.1 Estimation using asclogit
16.5 Ordered choice models
16.6 Models for count data
16.7 Censored data models
16.7.1 Simulated data example
16.7.2 Mroz data example
16.8 Selection bias
Key terms
Chapter 16 dofile
Appendix A Review of Math Essentials
A.1 Stata math and logical operators
A.2 Math functions
A.3 Extensions to generate
A.4 The calculator
A.5 Scientific notation
A.6 Numerical derivatives and integrals
Key terms
Appendix A dofile
Appendix B Review of Probability
B.1 Stata probability functions
B.2 Binomial distribution
B.3 Normal distribution
B.3.1 Normal density plots
B.3.2 Normal probability calculations
B.4 Student's tdistribution
B.4.1 Plot of standard normal and t(3)
B.4.2 tdistribution probabilities
B.4.3 Graphing tail probabilities
B.5 Fdistribution
B.5.1 Plotting the Fdensity
B.5.2 Fdistribution probability calculations
B.6 Chisquare distribution
B.6.1 Plotting the chisquare density
B.6.2 Chisquare probability calculations
B.7 Random numbers
B.7.1 Using inversion method
B.7.2 Creating uniform random numbers
Key terms
Appendix B dofile
Appendix C Review of Statistical Inference
C.1 Examining the hip data
C.1.1 Constructing a histogram
C.1.2 Obtaining summary statistics
C.1.3 Estimating the population mean
C.2 Using simulated data values
C.3 The central limit theorem
C.4 Interval estimation
C.4.1 Using simulated data
C.4.2 Using the hip data
C.5 Testing the mean of a normal population
C.5.1 Righttail test
C.5.2 Twotail test
C.6 Testing the variance of a normal population
C.7 Testing the equality of two normal population means
C.7.1 Population variances are equal
C.7.2 Population variances are unequal
C.8 Testing the equality of two normal population variances
C.9 Testing normality
C.10 Maximum likelihood estimation
C.11 Kernel density estimator
Key terms
Appendix C dofile
Index