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Microeconometrics Using Stata, Second Edition, Volumes I and II 2nd edition [Multiple-component retail product]

(University of California, Davis, California, USA University of California, Davis, California, USA University of California, California, USA),
  • Formaat: Multiple-component retail product, 1675 pages, kõrgus x laius: 246x189 mm, kaal: 3380 g, Contains 2 paperbacks
  • Ilmumisaeg: 21-Jul-2022
  • Kirjastus: Stata Press
  • ISBN-10: 1597183598
  • ISBN-13: 9781597183598
Teised raamatud teemal:
Microeconometrics Using Stata, Second Edition, Volumes I and II 2nd editionSuurem pilt
  • Formaat: Multiple-component retail product, 1675 pages, kõrgus x laius: 246x189 mm, kaal: 3380 g, Contains 2 paperbacks
  • Ilmumisaeg: 21-Jul-2022
  • Kirjastus: Stata Press
  • ISBN-10: 1597183598
  • ISBN-13: 9781597183598
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781597183598.html
Märksõnad:
Microeconometrics Using Stata, Second Edition is an invaluable reference for researchers and students interested in applied microeconometric methods.

Like previous editions, this text covers all the classic microeconometric techniques ranging from linear models to instrumental-variables regression to panel-data estimation to nonlinear models such as probit, tobit, Poisson, and choice models. Each of these discussions has been updated to show the most modern implementation in Stata, and many include additional explanation of the underlying methods. In addition, the authors introduce readers to performing simulations in Stata and then use simulations to illustrate methods in other parts of the book. They even teach you how to code your own estimators in Stata.

The second edition is greatly expandedthe new material is so extensive that the text now comprises two volumes. In addition to the classics, the book now teaches recently developed econometric methods and the methods newly added to Stata. Specifically, the book includes entirely new chapters on





duration models randomized control trials and exogenous treatment effects endogenous treatment effects models for endogeneity and heterogeneity, including finite mixture models, structural equation models, and nonlinear mixed-effects models spatial autoregressive models semiparametric regression lasso for prediction and inference Bayesian analysis

Anyone interested in learning classic and modern econometric methods will find this the perfect companion. And those who apply these methods to their own data will return to this reference over and over as they need to implement the various techniques described in this book.
Volume I Cross-Sectional and Panel Regression Methods
List of tables
xiii
List of figures
xv
Preface to the Second Edition xvii
Preface to the First Edition xix
1 Stata basics
1(32)
1.1 Interactive use
1(1)
1.2 Documentation
2(3)
1.3 Command syntax and operators
5(9)
1.4 Do-files and log files
14(5)
1.5 Scalars and matrices
19(1)
1.6 Using results from Stata commands
20(3)
1.7 Global and local macros
23(3)
1.8 Looping commands
26(3)
1.9 Mata and Python in Stata
29(1)
1.10 Some useful commands
29(1)
1.11 Template do-file
30(1)
1.12 Community-contributed commands
30(1)
1.13 Additional resources
31(1)
1.14 Exercises
31(2)
2 Data management and graphics
33(52)
2.1 Introduction
33(1)
2.2 Types of data
33(3)
2.3 Inputting data
36(7)
2.4 Data management
43(17)
2.5 Manipulating datasets
60(7)
2.6 Graphical display of data
67(16)
2.7 Additional resources
83(1)
2.8 Exercises
83(2)
3 Linear regression basics
85(64)
3.1 Introduction
85(1)
3.2 Data and data summary
85(9)
3.3 Transformation of data before regression
94(2)
3.4 Linear regression
96(6)
3.5 Basic regression analysis
102(21)
3.6 Specification analysis
123(9)
3.7 Specification tests
132(8)
3.8 Sampling weights
140(5)
3.9 OLS using Mata
145(2)
3.10 Additional resources
147(1)
3.11 Exercises
147(2)
4 Linear regression extensions
149(58)
4.1 Introduction
149(1)
4.2 In-sample prediction
149(8)
4.3 Out-of-sample prediction
157(4)
4.4 Predictive margins
161(14)
4.5 Marginal effects
175(11)
4.6 Regression decomposition analysis
186(7)
4.7 Shapley decomposition of relative regressor importance
193(2)
4.8 Difference-in-differences estimators
195(9)
4.9 Additional resources
204(1)
4.10 Exercises
204(3)
5 Simulation
207(38)
5.1 Introduction
207(1)
5.2 Pseudorandom-number generators
208(6)
5.3 Distribution of the sample mean
214(6)
5.4 Pseudorandom-number generators: Further details
220(7)
5.5 Computing integrals
227(5)
5.6 Simulation for regression: Introduction
232(10)
5.7 Additional resources
242(1)
5.8 Exercises
242(3)
6 Linear regression with correlated errors
245(60)
6.1 Introduction
245(1)
6.2 Generalized least-squares and FGLS regression
246(4)
6.3 Modeling heteroskedastic data
250(6)
6.4 OLS for clustered data
256(9)
6.5 FGLS estimators for clustered data
265(4)
6.6 Fixed-effects estimator for clustered data
269(8)
6.7 Linear mixed models for clustered data
277(9)
6.8 Systems of linear regressions
286(9)
6.9 Survey data: Weighting, clustering, and stratification
295(6)
6.10 Additional resources
301(1)
6.11 Exercises
302(3)
7 Linear instrumental-variables regression
305(68)
7.1 Introduction
305(1)
7.2 Simultaneous equations model
306(4)
7.3 Instrumental-variables regression
310(6)
7.4 Instrumental-variables example
316(14)
7.5 Weak instruments
330(9)
7.6 Diagnostics and tests for weak instruments
339(14)
7.7 Inference with weak instruments
353(9)
7.8 Finite sample inference with weak instruments
362(1)
7.9 Other estimators
363(4)
7.10 Three-stage least-squares systems estimation
367(1)
7.11 Additional resources
368(1)
7.12 Exercises
369(4)
8 Linear panel-data models: Basics
373(48)
8.1 Introduction
373(1)
8.2 Panel-data methods overview
373(6)
8.3 Summary of panel data
379(15)
8.4 Pooled or population-averaged estimators
394(3)
8.5 Fixed-effects or within estimator
397(4)
8.6 Between estimator
401(1)
8.7 Random-effects estimator
402(4)
8.8 Comparison of estimators
406(6)
8.9 First-difference estimator
412(2)
8.10 Panel-data management
414(4)
8.11 Additional resources
418(1)
8.12 Exercises
419(2)
9 Linear panel-data models: Extensions
421(38)
9.1 Introduction
421(1)
9.2 Panel instrumental-variables estimation
421(4)
9.3 Hausman-Taylor estimator
425(3)
9.4 Arellano-Bond estimator
428(17)
9.5 Long panels
445(11)
9.6 Additional resources
456(1)
9.7 Exercises
456(3)
10 Introduction to nonlinear regression
459(20)
10.1 Introduction
459(1)
10.2 Binary outcome models
459(3)
10.3 Probit model
462(4)
10.4 MEs and coefficient interpretation
466(6)
10.5 Logit model
472(2)
10.6 Nonlinear least squares
474(2)
10.7 Other nonlinear estimators
476(1)
10.8 Additional resources
477(1)
10.9 Exercises
477(2)
11 Tests of hypotheses and model specification
479(58)
11.1 Introduction
479(1)
11.2 Critical values and p-values
480(5)
11.3 Wald tests and confidence intervals
485(13)
11.4 Likelihood-ratio tests
498(4)
11.5 Lagrange multiplier test (or score test)
502(3)
11.6 Multiple testing
505(7)
11.7 Test size and power
512(7)
11.8 The power onemean command for multiple regression
519(10)
11.9 Specification tests
529(3)
11.10 Permutation tests and randomization tests
532(2)
11.11 Additional resources
534(1)
11.12 Exercises
534(3)
12 Bootstrap methods
537(42)
12.1 Introduction
537(1)
12.2 Bootstrap methods
537(2)
12.3 Bootstrap pairs using the vce(bootstrap) option
539(8)
12.4 Bootstrap pairs using the bootstrap command
547(8)
12.5 Percentile-t bootstraps with asymptotic refinement
555(5)
12.6 Wild bootstrap with asymptotic refinement
560(9)
12.7 Bootstrap pairs using bsample and simulate
569(1)
12.8 Alternative resampling schemes
570(5)
12.9 The jackknife
575(1)
12.10 Additional resources
576(1)
12.11 Exercises
577(2)
13 Nonlinear regression methods
579(64)
13.1 Introduction
579(1)
13.2 Nonlinear example: Doctor visits
580(2)
13.3 Nonlinear regression methods
582(15)
13.4 Different estimates of the VCE
597(7)
13.5 Prediction
604(5)
13.6 Predictive margins
609(3)
13.7 Marginal effects
612(17)
13.8 Model diagnostics
629(3)
13.9 Clustered data
632(8)
13.10 Additional resources
640(1)
13.11 Exercises
640(3)
14 Flexible regression: Finite mixtures and nonparametric
643(40)
14.1 Introduction
643(1)
14.2 Models based on finite mixtures
644(6)
14.3 FMM example: Earnings of doctors
650(15)
14.4 Global polynomials
665(3)
14.5 Regression splines
668(7)
14.6 Nonparametric regression
675(5)
14.7 Partially parametric regression
680(1)
14.8 Additional resources
681(1)
14.9 Exercises
681(2)
15 Quantile regression
683(26)
15.1 Introduction
683(1)
15.2 Conditional quantile regression
684(4)
15.3 CQR for medical expenditures data
688(11)
15.4 CQR for generated heteroskedastic data
699(4)
15.5 Quantile treatment effects for a binary treatment
703(3)
15.6 Additional resources
706(1)
15.7 Exercises
707(2)
A Programming in Stata
709(18)
A.1 Stata matrix commands
709(7)
A.2 Programs
716(6)
A.3 Program debugging
722(3)
A.4 Additional resources
725(2)
B Mata
727(14)
B.1 How to run Mata
727(2)
B.2 Mata matrix commands
729(9)
B.3 Programming in Mata
738(2)
B.4 Additional resources
740(1)
C Optimization in Mata
741(78)
C.1 Mata moptimize() function
741(10)
C.2 Mata optimize() function
751(3)
C.3 Additional resources
754(65)
Glossary of abbreviations 755(6)
References 761(16)
Author Index 777(6)
Subject Index 783(828)
Volume II Nonlinear Models and Causal Inference Methods
List of tables
xiii
List of figures
xv
16 Nonlinear optimization methods
819(38)
16.1 Introduction
819(1)
16.2 Newton-Raphson method
819(5)
16.3 Gradient methods
824(5)
16.4 Overview of ml, moptimize(), and optimize()
829(2)
16.5 The ml command: If method
831(6)
16.6 Checking the program
837(7)
16.7 The ml command: lf0--lf2, d0---d2, and gf0 methods
844(7)
16.8 Nonlinear instrumental-variables (GMM) example
851(3)
16.9 Additional resources
854(1)
16.10 Exercises
854(3)
17 Binary outcome models
857(44)
17.1 Introduction
857(1)
17.2 Some parametric models
858(2)
17.3 Estimation
860(2)
17.4 Example
862(7)
17.5 Goodness of fit and prediction
869(8)
17.6 Marginal effects
877(3)
17.7 Clustered data
880(1)
17.8 Additional models
881(6)
17.9 Endogenous regressors
887(8)
17.10 Grouped and fractional data
895(3)
17.11 Additional resources
898(1)
17.12 Exercises
898(3)
18 Multinomial models
901(48)
18.1 Introduction
901(1)
18.2 Multinomial models overview
901(4)
18.3 Multinomial example: Choice of fishing mode
905(3)
18.4 Multinomial logit model
908(6)
18.5 Alternative-specific conditional logit model
914(8)
18.6 Nested logit model
922(7)
18.7 Multinomial probit model
929(5)
18.8 Alternative-specific random-parameters logit
934(4)
18.9 Ordered outcome models
938(4)
18.10 Clustered data
942(1)
18.11 Multivariate outcomes
943(3)
18.12 Additional resources
946(1)
18.13 Exercises
946(3)
19 Tobit and selection models
949(72)
19.1 Introduction
949(1)
19.2 Tobit model
950(3)
19.3 Tobit model example
953(8)
19.4 Tobit for lognormal data
961(9)
19.5 Two-part model in logs
970(4)
19.6 Selection models
974(8)
19.7 Nonnormal models of selection
982(4)
19.8 Prediction from models with outcome in logs
986(3)
19.9 Endogenous regressors
989(2)
19.10 Missing data
991(4)
19.11 Panel attrition
995(24)
19.12 Additional resources
1019(1)
19.13 Exercises
1019(2)
20 Count-data models
1021(78)
20.1 Introduction
1021(1)
20.2 Modeling strategies for count data
1022(4)
20.3 Poisson and negative binomial models
1026(18)
20.4 Hurdle model
1044(6)
20.5 Finite-mixture models
1050(19)
20.6 Zero-inflated models
1069(10)
20.7 Endogenous regressors
1079(10)
20.8 Clustered data
1089(1)
20.9 Quantile regression for count data
1090(6)
20.10 Additional resources
1096(1)
20.11 Exercises
1096(3)
21 Survival analysis for duration data
1099(40)
21.1 Introduction
1099(1)
21.2 Data and data summary
1100(4)
21.3 Survivor and hazard functions
1104(5)
21.4 Semiparametric regression model
1109(9)
21.5 Fully parametric regression models
1118(11)
21.6 Multiple-records data
1129(3)
21.7 Discrete-time hazards logit model
1132(3)
21.8 Time-varying regressors
1135(1)
21.9 Clustered data
1136(1)
21.10 Additional resources
1137(1)
21.11 Exercises
1137(2)
22 Nonlinear panel models
1139(52)
22.1 Introduction
1139(1)
22.2 Nonlinear panel-data overview
1139(6)
22.3 Nonlinear panel-data example
1145(3)
22.4 Binary outcome and ordered outcome models
1148(19)
22.5 Tobit and interval-data models
1167(5)
22.6 Count-data models
1172(12)
22.7 Panel quantile regression
1184(3)
22.8 Endogenous regressors in nonlinear panel models
1187(1)
22.9 Additional resources
1188(1)
22.10 Exercises
1188(3)
23 Parametric models for heterogeneity and endogeneity
1191(78)
23.1 Introduction
1191(1)
23.2 Finite mixtures and unobserved heterogeneity
1192(3)
23.3 Empirical examples of FMMs
1195(29)
23.4 Nonlinear mixed-effects models
1224(7)
23.5 Linear structural equation models
1231(20)
23.6 Generalized structural equation models
1251(10)
23.7 ERM commands for endogeneity and selection
1261(5)
23.8 Additional resources
1266(1)
23.9 Exercises
1266(3)
24 Randomized control trials and exogenous treatment effects
1269(68)
24.1 Introduction
1269(2)
24.2 Potential outcomes
1271(1)
24.3 Randomized control trials
1272(10)
24.4 Regression in an RCT
1282(8)
24.5 Treatment evaluation with exogenous treatment
1290(2)
24.6 Treatment evaluation methods and estimators
1292(10)
24.7 Stata commands for treatment evaluation
1302(3)
24.8 Oregon Health Insurance Experiment example
1305(7)
24.9 Treatment-effect estimates using the OHIE data
1312(11)
24.10 Multilevel treatment effects
1323(9)
24.11 Conditional quantile TEs
1332(2)
24.12 Additional resources
1334(1)
24.13 Exercises
1335(2)
25 Endogenous treatment effects
1337(68)
25.1 Introduction
1337(1)
25.2 Parametric methods for endogenous treatment
1338(3)
25.3 ERM commands for endogenous treatment
1341(7)
25.4 ET commands for binary endogenous treatment
1348(8)
25.5 The LATE estimator for heterogeneous effects
1356(7)
25.6 Difference-in-differences and synthetic control
1363(6)
25.7 Regression discontinuity design
1369(19)
25.8 Conditional quantile regression with endogenous regressors
1388(6)
25.9 Unconditional quantiles
1394(7)
25.10 Additional resources
1401(1)
25.11 Exercises
1402(3)
26 Spatial regression
1405(28)
26.1 Introduction
1405(1)
26.2 Overview of spatial regression models
1406(1)
26.3 Geospatial data
1407(4)
26.4 The spatial weighting matrix
1411(2)
26.5 OLS regression and test for spatial correlation
1413(1)
26.6 Spatial dependence in the error
1414(3)
26.7 Spatial autocorrelation regression models
1417(10)
26.8 Spatial instrumental variables
1427(1)
26.9 Spatial panel-data models
1428(1)
26.10 Additional resources
1429(1)
26.11 Exercises
1430(3)
27 Semiparametric regression
1433(32)
27.1 Introduction
1433(1)
27.2 Kernel regression
1434(4)
27.3 Series regression
1438(2)
27.4 Nonparametric single regressor example
1440(10)
27.5 Nonparametric multiple regressor example
1450(3)
27.6 Partial linear model
1453(3)
27.7 Single-Index Model
1456(2)
27.8 Generalized additive models
1458(3)
27.9 Additional resources
1461(1)
27.10 Exercises
1462(3)
28 Machine learning for prediction and inference
1465(62)
28.1 Introduction
1465(1)
28.2 Measuring the predictive ability of a model
1466(11)
28.3 Shrinkage estimators
1477(5)
28.4 Prediction using lasso, ridge, and elasticnet
1482(11)
28.5 Dimension reduction
1493(3)
28.6 Machine learning methods for prediction
1496(5)
28.7 Prediction application
1501(6)
28.8 Machine learning for inference in partial linear model
1507(9)
28.9 Machine learning for inference in other models
1516(7)
28.10 Additional resources
1523(1)
28.11 Exercises
1524(3)
29 Bayesian methods: Basics
1527(52)
29.1 Introduction
1527(1)
29.2 Bayesian introductory example
1528(4)
29.3 Bayesian methods overview
1532(6)
29.4 An i.i.d. example
1538(11)
29.5 Linear regression
1549(3)
29.6 A linear regression example
1552(8)
29.7 Modifying the MH algorithm
1560(2)
29.8 RE model
1562(5)
29.9 Bayesian model selection
1567(2)
29.10 Bayesian prediction
1569(3)
29.11 Probit example
1572(4)
29.12 Additional resources
1576(1)
29.13 Exercises
1576(3)
30 Bayesian methods: Markov chain Monte Carlo algorithms
1579(32)
30.1 Introduction
1579(1)
30.2 User-provided log likelihood
1579(5)
30.3 MH algorithm in Mata
1584(5)
30.4 Data augmentation and the Gibbs sampler in Mata
1589(6)
30.5 Multiple imputation
1595(4)
30.6 Multiple-imputation example
1599(9)
30.7 Additional resources
1608(1)
30.8 Exercises
1608(3)
Glossary of abbreviations 1611(6)
References 1617(18)
Author Index 1635(6)
Subject Index 1641
Colin Cameron is a professor of economics at the University of CaliforniaDavis, where he teaches econometrics at undergraduate and graduate levels, as well as an undergraduate course in health economics. He has given short courses in Europe, Australia, Asia, and South America. His research interests are in microeconometrics, especially in robust inference for regression with clustered errors. He is currently an associate editor of the Stata Journal.

Pravin K. Trivedi is a Distinguished Professor Emeritus at Indiana UniversityBloomington and an honorary professor in the School of Economics at the University of Queensland. During his academic career, he has taught undergraduate- and graduate-level econometrics in the United States, England, Europe, and Australia. His research interests include microeconometrics and health economics. He served as coeditor of the Econometrics Journal from 20002007 and associate editor of the Journal of Applied Econometrics from 19862015. He has coauthored (with David Zimmer) Copula Modeling in Econometrics: An Introduction for Practitioners (2007).

Cameron and Trivedis joint work includes research articles on econometric models and tests for count data, the Econometric Society monograph Regression Analysis of Count Data, and the graduate-level text Microeconometrics: Methods and Applications.