Using Stata for Principles of Econometrics 4th Edition by Lee Adkins, Carter Hill – Ebook PDF Instant Download/Delivery: 111803208X, 978-1118032084
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ISBN 10: 111803208X
ISBN 13: 978-1118032084
Author: Lee Adkins, Carter Hill
Principles of Econometrics is an introductory book for undergraduate students in economics and finance, and can be used for MBA and first-year graduate students in many fields. The 4th Edition provides students with an understanding of why econometrics is necessary and a working knowledge of basic econometric tools. This text emphasizes motivation, understanding and implementation by introducing very simple economic models and asking economic questions that students can answer.
Using Stata for Principles of Econometrics 4th 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 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 and 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 t-distribution tail-cdf
1.16.3 Example computing percentile of the standard normal
1.16.4 Example computing percentile of the t-distribution
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 Do-file
CHAPTER 2: The Simple Linear Regression Model
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 covariance
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 log-linear model
2.7 Regression using 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 log-linear model
Appendix 2B A simulation experiment
Key Terms
Chapter 2 Do-file
CHAPTER 3: Interval Estimation and Hypothesis 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 of a right-tail test
3.3.2 p-value of a left-tail test
3.3.3 p-value for a two-tail test
3.3.4 p-values 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 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 postestimation 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 linear-log functional form
4.3.2 Plotting the fitted linear-log model
4.3.3 Editing graphs
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 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 log-linear yield growth model
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
4.7 A log-log model
Key Terms
Chapter 4 Do-file
CHAPTER 5: The Multiple Regression Model
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 Two-sided tests
5.5.2 One-sided 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 Goodness-of-fit
Key Terms
Chapter 5 Do-file
CHAPTER 6: Further Inference in the Multiple Regression Model
6.1 The F-Test
6.1.1 Testing the significance of the model
6.1.2 Relationship between t- and F-tests
6.1.3 More general F-tests
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 Do-file
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 Further 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 log-linear models
7.3 The linear probability model
7.4 Treatment effects
7.5 Differences-in-differences estimation
Key Terms
Chapter 7 Do-file
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 Goldfeld-Quandt test
8.3 Heteroskedastic-consistent 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 Do-file
CHAPTER 9: Regression with Time-Series Data: Stationary Variables
9.1 Introduction
9.1.1 Defining time-series in Stata
9.1.2 Time-series 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 Durbin-Watson test
9.9.2 Prais-Winsten FGLS
Key Terms
Chapter 9 Do-file
CHAPTER 10: Random Regressors and Moment-Based Estimation
10.1 Least squares estimation of a wage equation
10.2 Two-stage 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 Cragg-Donald F-statistic
10.8 A simulation experiment
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
11.7 2SLS estimates of fish demand
11.8 2SLS alternatives
11.9 Monte Carlo simulation results
Key Terms
Chapter 11 Do-file
CHAPTER 12: Regression with Time-Series 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 Engle-Granger test
12.4.2 The Error-correction model
Key Terms
Chapter 12 Do-file
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 Do-file
CHAPTER 14: Time-Varying Volatility and ARCH Models
14.1 ARCH model and time-varying volatility
14.2 Estimating, testing, 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 A microeconometric panel
15.2 A pooled model
15.2.1 Cluster-robust 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 Breusch-Pagan test
15.4.3 The Hausman test
15.4.4 The Hausman-Taylor model
15.5 Sets of regression equations
15.5.1 Seemingly unrelated regresssions
15.5.2 SUR with wide data
15.6 Mixed models
Key Terms
Chapter 15 Do-file
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 Out-of-sample 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
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Tags: Lee Adkins, Carter Hill, Using Stata, Principles of Econometrics