Using Stata for Principles of Econometrics, 3rd Edition
by Lee C. Adkins and R. Carter Hill
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 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
15.3.1 A dummy variable
15.3.2 The fixed effects estimator
15.3.3 The fixed effects estimator for a microeconometric panel
15.4 Random effects estimation
15.4.1 Breusch–Pagan test
15.4.2 Hausman test
Key Terms
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
16.3.2 Release 10: asclogit
16.4 Ordered choice models
16.5 Models for cont data
16.6 Censored data models
16.6.1 Simulated data example
16.6.2 Mroz data example
16.7 Selection bias
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.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 Right rail test
C.5.2 Two tail 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
Key Terms
Appendix C Do-file
Index
© Copyright StataCorp LP 1996-2010.
|
