Using Stata for Principles of Econometrics

By: Lee C. Adkins; R. Carter Hill

• Publisher: Wiley

• Print ISBN: 9781119502180, 1119502187

  Print ISBN: 9781119502180, 1119502187

• eText ISBN: 9781119463245, 1119463246

  eText ISBN: 9781119463245, 1119463246

• Edition: 5th
• Copyright year: 2018
• Format: Fixed

Table of Contents Title Page Copyright Contents Chapter 1 Introducing Stata Key Terms 1.1 Starting Stata 1.2 The opening display 1.3 Exiting Stata 1.4 Stata data files for POE5 1.4.1 A working directory 1.5 Opening Stata data files 1.5.1 Using the toolbar 1.5.2 The use command 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 utility for a single label 1.7 Describing data and obtaining summary statistics 1.8 The Stata help system 1.8.1 Using keyword search 1.8.2 Opening a dialog box 1.8.3 Complete documentation in Stata manuals 1.8.4 Advice 1.8.5 Stata videos on YouTube 1.8.6 Statalist 1.8.7 Not elsewhere classified 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 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 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 temporary scalars Chapter 1 Do-file Chapter 2 Simple Linear Regression Key Terms 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 Plotting the fitted regression line 2.5 Using Stata to obtain predicted values 2.5.1 Using saved coefficients 2.5.2 Using lincom 2.5.3 Using the margins command 2.5.4 Using incomplete observations 2.5.5 Computing an elasticity 2.6 OLS estimator variances and covariance 2.6.1 Estimating the variance of the error term 2.6.2 Viewing estimated variances and covariance 2.6.3 Saving the Stata data file 2.7 Estimating nonlinear relationships 2.7.1 A quadratic model 2.7.2 A log-linear model 2.8 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 log-linear model Appendix 2B Simulation experiments 2B.1 Fixed x’s 2B.2 Random x’s Chapter 2 Do-file Chapter 3 Interval Estimation and Hypothesis Testing Key Terms 3.1 Interval estimates 3.1.1 Critical values from the t-distribution 3.1.2 Creating an interval estimate 3.1.3 Creating an interval estimate using lincom 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 3.2.5 Two-tail test of significance 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 3B.1 Fixed x’s 3B.2 Random x’s Chapter 3 Do-file Chapter 4 Prediction, Goodness of Fit and Modeling Issues Key Terms 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 4.1.4 Using margins to create 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 Reporting regression results 4.3.2 The linear-log functional form 4.3.3 Plotting the fitted linear-log model 4.3.4 Editing graphs 4.4 Analyzing the residuals 4.4.1 Residual plots 4.4.2 The Jarque-Bera test 4.4.3 Chi-square distribution critical values 4.4.4 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 4.6.6 Prediction intervals in the log-linear model using margins 4.7 A log-log model Chapter 4 Do-file Chapter 5 Multiple Linear Regression Key Terms 5.1 The Hamburger Chain Model 5.2 Least Squares Estimation 5.2.1 Least squares procedure 5.2.2 Least squares prediction 5.2.3 Rescaling the variables 5.2.4 Estimating the error variance 5.2.5 Measuring the goodness-of-fit 5.2.6 Frisch-Waugh-Lovell 5.3 Least Squares Precision 5.4 Confidence Intervals 5.4.1 Changing the confidence level 5.4.2 Linear combination of parameters 5.5 Hypothesis Tests 5.5.1 Two-sided t-test 5.5.2 One-sided t-test 5.5.3 Testing a linear combination of parameters 5.6 Interaction Variables 5.6.1 Polynomial regressors 5.6.2 Using factor variables for interactions 5.6.3 Interactions with other variables 5.6.4 Log-wages and quadratic interactions 5.6.5 Optimal level of advertising 5.6.6 Maximizing wages via experience Appendix 5B.1 Nonlinear functions of a single parameter Appendix 5B.2 Nonlinear functions of two parameters Appendix 5C.1 Least squares estimation with chi-square errors Appendix 5.C.2 Monte Carlo simulation of the delta method Appendix 5D Bootstrapping Chapter 5 Do-file Chapter 6 Further Inference in the Multiple Regression Model Key Terms 6.1 Testing joint hypotheses: 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 6.1.4 Large sample tests 6.1.5 Nonlinear hypothesis tests 6.2 Stata programs 6.3 Nonsample information 6.4 Model specification 6.4.1 Omitted variables 6.4.2 Irrelevant variables 6.4.3 Choosing the model 6.4.4 RESET test for function form 6.4.5 RESET program 6.4.6 Control variables 6.4.7 Prediction-forecast error variance 6.4.8 Prediction-model selection and RMSE 6.5 Poor data, collinearity, and insignificance 6.5.1 Variance inflation factors 6.5.2 Influential observations 6.6 Nonlinear least squares Chapter 6 Do-file Chapter 7 Using Indicator Variables Key Terms 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 Chapter 7 Do-file Chapter 8 Heteroskedasticity Key Terms 8.1 The nature of heteroskedasticity 8.2 Heteroskedastic-consistent standard errors 8.3 The generalized least squares estimator 8.3.1 Feasible GLS-a more general case 8.3.2 Fesible GLS with a heteroskedastic partition 8.4 Detecting heteroskedasticity 8.4.1 The Goldfeld-Quandt test using partitioned data 8.4.2 The Goldfeld-Quandt test in the food expenditure model 8.4.3 Lagrange multiplier tests 8.5 Heteroskedasticity in the linear probability model Appendix 8D Alternative robust sandwich estimators Appendix 8E Monte Carlo evidence Chapter 8 Do-file Chapter 9 Regression with Time-Series Data: Stationary Variables Key Terms 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 Correlogram 9.3 The AR(2) model 9.4 Autoregressive distributed lag models 9.4.1 Forecasts and forecast intervals 9.4.2 Model selection 9.4.3 Granger causality 9.5 Serial correlation in residuals 9.5.1 Detecting autocorrelation in residuals 9.5.2 Okun’s Law 9.5.3 HAC standard errors 9.5.4 Nonlinear least squares 9.5.5 Feasible GLS 9.6 The consumption function 9.7 Multipliers for an IDL model 9.8 Durbin-Watson Test Chapter 9 Do-file Chapter 10 Endogenous Regressors and Moment Based Estimation Key Terms 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 Illustrations using simulated data 10.9 A simulation experiment Chapter 10 Do-file Chapter 11 Simultaneous Equations Models Key Terms 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 Chapter 11 Do-file Chapter 12 Regression with Time-Series Data: Nonstationary Variables Key Terms 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 12.1.4 Summary statistics using subsamples 12.1.5 Correlogram 12.2 Deterministic trends 12.3 Spurious regressions 12.4 Unit root tests for stationarity 12.4.1 Is GDP trend stationary? 12.4.2 Is wheat yield stationary? 12.5 Integration and cointegration 12.5.1 Order of integration 12.5.2 Engle-Granger test 12.5.3 The error correction model 12.5.4 Regression with no cointegration Chapter 12 Do-file Chapter 13 Vector Error Correction and Vector Autoregressive Models Key Terms 13.1 VEC and VAR models 13.2 Estimating a VEC model 13.3 Estimating a VAR 13.4 Impulse responses and variance decompositions Chapter 13 Do-file Chapter 14 Time-Varying Volatility and ARCH Models Key Terms 14.1 ARCH model and time-varying volatility 14.2 Simulating ARCH 14.3 Testing, estimating and forecasting 14.4 Extensions 14.4.1 GARCH 14.4.2 Threshold GARCH 14.4.3 GARCH-in-mean Chapter 14 Do-file Chapter 15 Panel Data Models Key Terms 15.1 A microeconomic panel 15.2 The fixed-effects estimator 15.2.1 The difference estimator: T = 2 15.2.2 The within estimator: T = 2 15.2.3 The within estimator: T = 3 15.2.4 The fixed-effects estimator: xtreg 15.2.5 The least squares dummy variable estimator 15.2.6 Testing for fixed effects 15.3 Panel data regression error assumptions 15.3.1 OLS estimation with cluster-robust standard errors 15.3.2 Fixed-effects estimation with cluster-robust standard errors 15.3.3 Random-effects estimation of a production function 15.3.4 Random-effects estimation of a wage equation 15.3.5 Testing for random-effects 15.3.6 The Hausman contast test for the production function 15.3.7 The Hausman contast test for the wage equation 15.3.8 A regression based Hausman test for the production function 15.3.9 A regression based Hausman test for the wage equation 15.3.10 The Hausman-Taylor estimator Chapter 15 Do-file Chapter 16 Qualitative and Limited Dependent Variable Models Key Terms 16.1 Models with binary dependent variables 16.1.1 The linear probability model 16.1.2 Probit: a small example 16.1.3 Probit: the transportation data 16.1.4 Marginal effects 16.1.5 Probit marginal effects: details 16.1.6 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 Binary choice models with a continuous endogenous variable 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 16.8 Selection bias Appendix 16D Tobit Monte Carlo experiment Chapter 16 Do-file Appendix A Review of Math Essentials Key Terms 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 Logarithms A.7 Numerical derivatives and integrals Appendix A Do-file Appendix B Review of Probability Concepts Key Terms B.1 Stata probability functions B.2 Binomial distribution B.3 Poisson distribution B.4 Normal distribution B.4.1 Normal density plots B.4.2 Normal probability calculations B.5 Chi-square distribution B.5.1 Plotting the chi-square density B.5.2 Chi-square probability calculations B.5.3 The non-centaral chi-square pdf B.6 Student’s t-distribution B.6.1 Plot of standard normal and t(3) B.6.2 t-distribution probabilities B.6.3 Graphing tail probabilities B.6.4 The non-central t-distribution B.7 F-distribution B.7.1 Plotting the F-density B.7.2 F-distribution probabililty calculations B.7.3 The non-central F-distribution B.8 The log-normal distribution B.9 Random numbers B.9.1 Using inversion method B.9.2 Creating uniform random numbers Appendix B Do-file Appendix C Review of Statistical Inference Key Terms 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 Estimating population moments C.5 Interval estimation C.5.1 Computing confidence intervals C.5.2 Using simulated data C.5.3 Using the hip data C.6 Testing the mean of a normal population C.6.1 Right-tail test C.6.2 Two-tail test C.7 Testing the variance of a normal population C.8 Testing the equality of two normal population means C.8.1 Population variances are equal C.8.2 Population variances are unequal C.9 Testing the equality of two normal population variances C.10 Testing normality C.11 Maximum likelihood estimation C.11.1 Testing a population proportion C.11.2 Likelihood ratio test C.11.3 Wald test C.11.4 Lagrange multiplier test C.12 Least squares C.13 Kernel density estimator Appendix C Do-file EULA

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