Lee C. Adkins, R. Carter hill

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
    
  • 1.14.4 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 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