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Click to enlarge See the back cover 
Using Stata for Principles of Econometrics, Fourth Edition 

$74.50 each 




Comment from the Stata technical groupUsing 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 contentsView 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
1.6 The variables window 1.5.2 Using the toolbar 1.5.3 Using files on the Internet 1.5.4 Locating book files on the Internet
1.6.1 Using the data editor for a single label
1.7 Describing data and obtaining summary statistics 1.6.2 Using the data utility for a single label 1.6.3 Using variables manager 1.8 The Stata help system
1.8.1 Using keyword search
1.9 Stata command syntax
1.8.2 Using command search 1.8.3 Opening a dialog box 1.8.4 Complete documentation in Stata manuals
1.9.1 Syntax of summarize
1.10 Saving your work
1.9.2 Learning syntax using the review window
1.10.1 Copying and pasting
1.11 Using the data browser 1.10.2 Using a log file 1.12 Using Stata graphics
1.12.1 Histograms
1.13 Using Stata dofiles 1.12.2 Scatter diagrams 1.14 Creating and managing variables
1.14.1 Creating (generating) new variables
1.15 Using Stata density functions 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
1.15.1 Cumulative distribution functions
1.16 Using and displaying scalars 1.15.2 Inverse cumulative distribution functions
1.16.1 Example of standard normal cdf
1.17 A scalar dialog box 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 1.18 Using factor variables
1.18.1 Creating indicator variables using a logical operator
Key terms 1.18.2 Creating indicator variables using tabulate Chapter 1 dofile Chapter 2 Simple linear regression
2.1 The food expenditure data
2.1.1 Starting a new problem
2.2 Computing summary statistics 2.1.2 Starting a log file 2.1.3 Opening a Stata data file 2.1.4 Browsing and listing the data 2.3 Creating a scatter diagram
2.3.1 Enhancing the plot
2.4 Regression
2.4.1 Fitted values and residuals
2.5 Using Stata to obtain predicted values 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
2.5.1 Saving the Stata data file
2.6 Estimating nonlinear relationships
2.6.1 A quadratic model
2.7 Regression with indicator variables2.6.2 A loglinear model Appendix 2A Average marginal effects
2A.1 Elasticity in a linear relationship
Appendix 2B A simulation experiment 2A.2 Elasticity in a quadratic relationship 2A.3 Slope in a loglinear model Key terms Chapter 2 dofile Chapter 3 Interval Estimation and Hypothesis Testing
3.1 Interval estimates
3.1.1 Critical values from the tdistribution
3.2 Hypothesis tests 3.1.2 Creating an interval estimate
3.2.1 Righttail test of significance
3.3 pvalues 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
3.3.1 pvalue of a righttail test
Appendix 3A Graphical tools 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 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
4.2 Measuring goodnessoffit
4.1.2 Estimate the regression and obtain postestimation results 4.1.3 Creating the prediction interval
4.2.1 Correlations and R^{2}
4.3 The effects of scaling and transforming the data
4.3.1 The linearlog functional form
4.4 Analyzing the residuals 4.3.2 Plotting the fitted linearlog model 4.3.3 Editing graphs
4.4.1 The JarqueBera test
4.5 Polynomial models
4.4.2 Chisquare distribution critical values 4.4.3 Chisquare distribution pvalues
4.5.1 Estimating and checking the linear relationship
4.6 Estimating a loglinear wage equation
4.5.2 Estimating and checking a cubic equation 4.5.3 Estimating a loglinear yield growth model
4.6.1 The loglinear model
4.7 A loglog 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 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
5.6 Polynomial equations 5.5.2 Onesided tests 5.5.3 Testing a linear combination
5.6.1 Optimal advertising: nonlinear combinations of parameters
5.7 Interactions 5.6.2 Using factor variables for 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
6.2 Nonsample information 6.1.2 Relationship between t and Ftests 6.1.3 More general Ftests 6.3 Model specification
6.3.1 Omitted variables
6.4 Poor data, collinearity, and insignificance 6.3.2 Irrelevant variables 6.3.3 Choosing the model Key terms Chapter 6 dofile Chapter 7 Using Indicator Variables
7.1 Indicator variables
7.1.1 Creating indicator variables
7.2 Applying 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
7.2.1 Interactions between qualitative factors
7.3 The linear probability model 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 7.4 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
8.3 Heteroskedasticconsistent standard errors 8.2.2 Lagrange multiplier tests 8.2.3 The GoldfeldQuandt test 8.4 The generalized least squares estimator
8.4.1 GLS using grouped data
8.5 Heteroskedasticity in the linear probability model 8.4.2 Feasible GLS–a more general case Key terms Chapter 8 dofile Chapter 9 Regression with TimeSeries Data: Stationary Variables
9.1 Introduction
9.1.1 Defining timeseries in Stata
9.2 Finite distributed lags 9.1.2 Timeseries plots 9.1.3 Stata's lag and difference operators 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
9.6 Autoregressive distributed lag models 9.5.2 Nonlinear least squares 9.5.3 A more general model
9.6.1 Phillips curve
9.7 Forecasting 9.6.2 Okun's law 9.6.3 Autoregressive models
9.7.1 Forecasting with an AR model
9.8 Multiplier analysis 9.7.2 Exponential smoothing 9.9 Appendix
9.9.1 DurbinWatson test
Key terms 9.9.2 PraisWinsten FGLS 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
12.2 Spurious regressions 12.1.2 Extracting dates 12.1.3 Graphing the data 12.3 Unit root tests for stationarity 12.4 Integration and cointegration
12.4.1 EngleGranger test
Key terms 12.4.2 Errorcorrection model 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
Key terms 14.3.2 TGARCH 14.3.3 GARCHinmean 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
15.4 Random effects estimation 15.3.2 The fixed effects estimator using xtreg 15.3.3 Fixed effects using the complete panel
15.4.1 The GLS transformation
15.5 Sets of regression equations 15.4.2 The BreuschPagan test 15.4.3 The Hausman test 15.4.4 The HausmanTaylor model
15.5.1 Seemingly unrelated regressions
15.6 Mixed models 15.5.2 SUR with wide data 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
16.2 The logit model for binary choice16.1.2 Probit marginal effects: details 16.1.3 Standard error of average marginal effect
16.2.1 Wald tests
16.3 Multinomial logit 16.2.2 Likelihood ratio tests 16.2.3 Logit estimation 16.2.4 Outofsample prediction 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
16.8 Selection bias 16.7.2 Mroz data example 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.4 Student's tdistribution B.3.2 Normal probability calculations
B.4.1 Plot of standard normal and t(3)
B.5 Fdistribution B.4.2 tdistribution probabilities B.4.3 Graphing tail probabilities
B.5.1 Plotting the Fdensity
B.6 Chisquare distribution B.5.2 Fdistribution probability calculations
B.6.1 Plotting the chisquare density
B.7 Random numbers B.6.2 Chisquare probability calculations
B.7.1 Using inversion method
Key terms B.7.2 Creating uniform random numbers Appendix B dofile Appendix C Review of Statistical Inference
C.1 Examining the hip data
C.1.1 Constructing a histogram
C.2 Using simulated data values C.1.2 Obtaining summary statistics C.1.3 Estimating the population mean C.3 The central limit theorem C.4 Interval estimation
C.4.1 Using simulated data
C.5 Testing the mean of a normal population C.4.2 Using the hip data
C.5.1 Righttail test
C.6 Testing the variance of a normal population C.5.2 Twotail test C.7 Testing the equality of two normal population means
C.7.1 Population variances are equal
C.8 Testing the equality of two normal population variances C.7.2 Population variances are unequal C.9 Testing normality C.10 Maximum likelihood estimation C.11 Kernel density estimator Key terms Appendix C dofile Index

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