9781597180139

An Introduction to Modern Econometrics Using Stata

by ;
  • ISBN13:

    9781597180139

  • ISBN10:

    1597180130

  • Edition: 1st
  • Format: Nonspecific Binding
  • Copyright: 8/17/2006
  • Publisher: Stata Press

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Summary

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.

Table of Contents

Illustrations
xv
Preface xvii
Notation and typography xix
Introduction
1(6)
An overview of Stata's distinctive features
1(3)
Installing the necessary software
4(1)
Installing the support materials
5(2)
Working with economic and financial data in Stata
7(36)
The basics
7(15)
The use command
7(1)
Variable types
8(1)
_n and N
9(1)
generate and replace
10(1)
sort and gsort
10(1)
if exp and in range
11(2)
Using if exp with indicator variables
13(2)
Using if exp versus by varlist: with statistical commands
15(2)
Labels and notes
17(3)
The varlist
20(1)
drop and keep
20(1)
rename and renvars
21(1)
The save command
21(1)
insheet and infile
21(1)
Common data transformations
22(21)
The cond() function
22(1)
Recoding discrete and continuous variables
23(1)
Handling missing data
24(1)
mvdecode and mvencode
25(1)
String-to-numeric conversion and vice versa
26(1)
Handling dates
27(2)
Some useful functions for generate or replace
29(1)
The egen command
30(1)
Official egen functions
30(1)
egen functions from the user community
31(2)
Computation for by-groups
33(3)
Local macros
36(1)
Looping over variables: forvalues and foreach
37(2)
Scalars and matrices
39(1)
Command syntax and return values
39(4)
Organizing and handling economic data
43(26)
Cross-sectional data and identifier variables
43(1)
Time-series data
44(1)
Time-series operators
45(1)
Pooled cross-sectional time-series data
45(1)
Panel data
46(3)
Operating on panel data
47(2)
Tools for manipulating panel data
49(6)
Unbalanced panels and data screening
50(3)
Other transforms of panel data
53(1)
Moving-window summary statistics and correlations
53(2)
Combining cross-sectional and time-series datasets
55(1)
Creating long-format datasets with append
56(2)
Using merge to add aggregate characteristics
57(1)
The dangers of many-to-many merges
58(1)
The reshape command
58(4)
The xpose command
62(1)
Using Stata for reproducible research
62(7)
Using do-files
62(1)
Data validation: assert and duplicates
63(6)
Linear regression
69(46)
Introduction
69(1)
Computing linear regression estimates
70(5)
Regression as a method-of-moments estimator
72(1)
The sampling distribution of regression estimates
73(1)
Efficiency of the regression estimator
74(1)
Numerical identification of the regression estimates
75(1)
Interpreting regression estimates
75(12)
Research project: A study of single-family housing prices
76(1)
The ANOVA table: ANOVA F and R-squared
77(1)
Adjusted R-squared
78(2)
The coefficient estimates and beta coefficients
80(1)
Regression without a constant term
81(1)
Recovering estimation results
82(2)
Detecting collinearity in regression
84(3)
Presenting regression estimates
87(4)
Presenting summary statistics and correlations
90(1)
Hypothesis tests, linear restrictions, and constrained least squares
91(11)
Wald tests with test
94(2)
Wald tests involving linear combinations of parameters
96(2)
Joint hypothesis tests
98(1)
Testing nonlinear restrictions and forming nonlinear combinations
99(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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