An Introduction to Modern Econometrics Using Stata

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ISBN: 9781597180139 | 1597180130
Cover: Nonspecific Binding
Copyright: 8/17/2006

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Integrating a contemporary approach to econometrics with the powerful computational tools offered by Stata, An Introduction to Modern Econometrics Using Stata places modern econometric theory in a practical context applied to real data sets using Stata software. The author emphasizes the role of method-of-moments estimators, hypothesis testing, and specification analysis and demonstrates how to apply theory to practice with numerous examples. He begins with several chapters that build familiarity with the basic skills needed to work with econometric data in Stata before delving into the core topics, which range from the multiple linear regression model to instrumental-variables estimation.

Illustrations

Preface

xvii

Notation and typography

xix

Introduction

(6)

An overview of Stata's distinctive features

(3)

Installing the necessary software

(1)

Installing the support materials

(2)

Working with economic and financial data in Stata

(36)

The basics

(15)

The use command

(1)

Variable types

(1)

_n and N

(1)

generate and replace

(1)

sort and gsort

(1)

if exp and in range

(2)

Using if exp with indicator variables

(2)

Using if exp versus by varlist: with statistical commands

(2)

Labels and notes

(3)

The varlist

(1)

drop and keep

(1)

rename and renvars

(1)

The save command

(1)

insheet and infile

(1)

Common data transformations

(21)

The cond() function

(1)

Recoding discrete and continuous variables

(1)

Handling missing data

(1)

mvdecode and mvencode

(1)

String-to-numeric conversion and vice versa

(1)

Handling dates

(2)

Some useful functions for generate or replace

(1)

The egen command

(1)

Official egen functions

(1)

egen functions from the user community

(2)

Computation for by-groups

(3)

Local macros

(1)

Looping over variables: forvalues and foreach

(2)

Scalars and matrices

(1)

Command syntax and return values

(4)

Organizing and handling economic data

(26)

Cross-sectional data and identifier variables

(1)

Time-series data

(1)

Time-series operators

(1)

Pooled cross-sectional time-series data

(1)

Panel data

(3)

Operating on panel data

(2)

Tools for manipulating panel data

(6)

Unbalanced panels and data screening

(3)

Other transforms of panel data

(1)

Moving-window summary statistics and correlations

(2)

Combining cross-sectional and time-series datasets

(1)

Creating long-format datasets with append

(2)

Using merge to add aggregate characteristics

(1)

The dangers of many-to-many merges

(1)

The reshape command

(4)

The xpose command

(1)

Using Stata for reproducible research

(7)

Using do-files

(1)

Data validation: assert and duplicates

(6)

Linear regression

(46)

Introduction

(1)

Computing linear regression estimates

(5)

Regression as a method-of-moments estimator

(1)

The sampling distribution of regression estimates

(1)

Efficiency of the regression estimator

(1)

Numerical identification of the regression estimates

(1)

Interpreting regression estimates

(12)

Research project: A study of single-family housing prices

(1)

The ANOVA table: ANOVA F and R-squared

(1)

Adjusted R-squared

(2)

The coefficient estimates and beta coefficients

(1)

Regression without a constant term

(1)

Recovering estimation results

(2)

Detecting collinearity in regression

(3)

Presenting regression estimates

(4)

Presenting summary statistics and correlations

(1)

Hypothesis tests, linear restrictions, and constrained least squares

(11)

Wald tests with test

(2)

Wald tests involving linear combinations of parameters

(2)

Joint hypothesis tests

(1)

Testing nonlinear restrictions and forming nonlinear combinations

(1)

Testing competing (nonnested) models

100

(2)

Computing residuals and predicted values

102

(5)

Computing interval predictions

103

(4)

Computing marginal effects

107

(5)

Appendix: Regression as a least-squares estimator

112

(1)

Appendix: The large-sample VCE for linear regression

113

(2)

Specifying the functional form

115

(18)

Introduction

115

(1)

Specification error

115

(17)

Omitting relevant variables from the model

116

(1)

Specifying dynamics in time-series regression models

117

(1)

Graphically analyzing regression data

117

(2)

Added-variable plots

119

(2)

Including irrelevant variables in the model

121

(1)

The asymmetry of specification error

121

(1)

Misspecification of the functional form

122

(1)

Ramsey's RESET

122

(2)

Specification plots

124

(1)

Specification and interaction terms

125

(1)

Outlier statistics and measures of leverage

126

(2)

The DFITS statistic

128

(2)

The DFBETA statistic

130

(2)

Endogeneity and measurement error

132

(1)

Regression with non-i.i.d. errors

133

(28)

The generalized linear regression model

134

(9)

Types of deviations from i.i.d. errors

134

(2)

The robust estimator of the VCE

136

(2)

The cluster estimator of the VCE

138

(1)

The Newey--West estimator of the VCE

139

(3)

The generalized least-squares estimator

142

(1)

The FGLS estimator

143

(1)

Heteroskedasticity in the error distribution

143

(11)

Heteroskedasticity related to scale

144

(1)

Testing for heteroskedasticity related to scale

145

(2)

FGLS estimation

147

(2)

Heteroskedasticity between groups of observations

149

(1)

Testing for heteroskedasticity between groups of observations

150

(1)

FGLS estimation

151

(1)

Heteroskedasticity in grouped data

152

(1)

FGLS estimation

153

(1)

Serial correlation in the error distribution

154

(7)

Testing for serial correlation

155

(4)

FGLS estimation with serial correlation

159

(2)

Regression with indicator variables

161

(24)

Testing for significance of a qualitative factor

161

(7)

Regression with one qualitative measure

162

(3)

Regression with two qualitative measures

165

(2)

Interaction effects

167

(1)

Regression with qualitative and quantitative factors

168

(6)

Testing for slope differences

170

(4)

Seasonal adjustment with indicator variables

174

(5)

Testing for structural stability and structural change

179

(6)

Constraints of continuity and differentiability

179

(4)

Structural change in a time-series model

183

(2)

Instrumental-variables estimators

185

(34)

Introduction

185

(1)

Endogeneity in economic relationships

185

(3)

2SLS

188

(1)

The ivreg command

189

(1)

Identification and tests of overidentifying restrictions

190

(2)

Computing IV estimates

192

(2)

ivreg2 and GMM estimation

194

(6)

The GMM estimator

195

(1)

GMM in a homoskedastic context

196

(1)

GMM and heteroskedasticity-consistent standard errors

197

(1)

GMM and clustering

198

(1)

GMM and HAC standard errors

199

(1)

Testing overidentifying restrictions in GMM

200

(5)

Testing a subset of the overidentifying restrictions in GMM

201

(4)

Testing for heteroskedasticity in the IV context

205

(2)

Testing the relevance of instruments

207

(4)

Durbin-Wu-Hausman tests for endogeneity in IV estimation

211

(5)

Appendix: Omitted-variables bias

216

(1)

Appendix: Measurement error

216

(3)

Solving errors-in-variables problems

218

(1)

Panel-data models

219

(28)

FE and RE models

220

(12)

One-way FE

221

(3)

Time effects and two-way FE

224

(2)

The between estimator

226

(1)

One-way RE

227

(3)

Testing the appropriateness of RE

230

(1)

Prediction from one-way FE and RE

231

(1)

IV models for panel data

232

(1)

Dynamic panel-data models

232

(4)

Seemingly unrelated regression models

236

(6)

SUR with identical regressors

241

(1)

Moving-window regression estimates

242

(5)

Models of discrete and limited dependent variables

247

(30)

Binomial logit and probit models

247

(9)

The latent-variable approach

248

(2)

Marginal effects and predictions

250

(1)

Binomial probit

251

(2)

Binomial logit and grouped logit

253

(1)

Evaluating specification and goodness of fit

254

(2)

Ordered logit and probit models

256

(3)

Truncated regression and tobit models

259

(7)

Truncation

259

(3)

Censoring

262

(4)

Incidental truncation and sample-selection models

266

(5)

Bivariate probit and probit with selection

271

(6)

Binomial probit with selection

272

(5)

Getting the data into Stata

277

(12)

Inputting data from ASCII text files and spreadsheets

277

(9)

Handling text files

278

(1)

Free format versus fixed format

278

(2)

The insheet command

280

(1)

Accessing data stored in spreadsheets

281

(1)

Fixed-format data files

281

(5)

Importing data from other package formats

286

(3)

The basics of Stata programming

289

(32)

Local and global macros

290

(4)

Global macros

293

(1)

Extended macro functions and list functions

293

(1)

Scalars

294

(1)

Loop constructs

295

(4)

foreach

297

(2)

Matrices

299

(2)

return and ereturn

301

(6)

ereturn list

305

(2)

The program and syntax statements

307

(6)

Using Mata functions in Stata programs

313

(8)

References

321

(8)

Author index

329

(4)

Subject index

333

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An Introduction to Modern Econometrics Using Stata

Summary

Table of Contents

Supplemental Materials