Microeconometrics Using Stata

1198 Pages·2022·53.28 MB·English

by  A. COLIN CAMERON, PRAVIN K. TRIVEDI

Microeconometrics Using Stata
Volume II: Nonlinear Models and Causal
Inference Methods
Second Edition
A. COLIN CAMERON
Department of Economics
University of California, Davis, CA
and
School of Economics
University of Sydney, Sydney, Australia
PRAVIN K. TRIVEDI
School of Economics
University of Queensland, Brisbane, Australia
and
Department of Economics
Indiana University, Bloomington, IN
®
A Stata Press Publication
StataCorp LLC
College Station, Texas
®
Copyright © 2009, 2010, 2022 by StataCorp LLC
All rights reserved. First edition 2009
Revised edition 2010
Second edition 2022
Published by Stata Press, 4905 Lakeway Drive, College Station, Texas
77845
Typeset in LaTeX2e
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1
Print ISBN-10: 1-59718-359-8 (volumes I and II)
Print ISBN-10: 1-59718-361-X (volume I)
Print ISBN-10: 1-59718-362-8 (volume II)
Print ISBN-13: 978-1-59718-359-8 (volumes I and II)
Print ISBN-13: 978-1-59718-361-1 (volume I)
Print ISBN-13: 978-1-59718-362-8 (volume II)
ePub ISBN-10: 1-59718-360-1 (volumes I and II)
ePub ISBN-10: 1-59718-363-6 (volumes I)
ePub ISBN-10: 1-59718-364-4 (volumes II)
ePub ISBN-13: 978-1-59718-360-4 (volumes I and II)
ePub ISBN-13: 978-1-59718-363-5 (volumes I)
ePub ISBN-13: 978-1-59718-364-2 (volumes II)
Library of Congress Control Number: 2022938057
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Contents
16 Nonlinear optimization methods
16.1 Introduction
16.2 Newton–Raphson method
16.3 Gradient methods
16.4 Overview of ml, moptimize(), and optimize()
16.5 The ml command: lf method
16.6 Checking the program
16.7 The ml command: lf0–lf2, d0–d2, and gf0 methods
16.8 Nonlinear instrumental-variables (GMM) example
16.9 Additional resources
16.10 Exercises
17 Binary outcome models
17.1 Introduction
17.2 Some parametric models
17.3 Estimation
17.4 Example
17.5 Goodness of fit and prediction
17.6 Marginal effects
17.7 Clustered data
17.8 Additional models
17.9 Endogenous regressors
17.10 Grouped and fractional data
17.11 Additional resources
17.12 Exercises
18 Multinomial models
18.1 Introduction
18.2 Multinomial models overview
18.3 Multinomial example: Choice of fishing mode
18.4 Multinomial logit model
18.5 Alternative-specific conditional logit model
18.6 Nested logit model
18.7 Multinomial probit model
18.8 Alternative-specific random-parameters logit
18.9 Ordered outcome models
18.10 Clustered data
18.11 Multivariate outcomes
18.12 Additional resources
18.13 Exercises
19 Tobit and selection models
19.1 Introduction
19.2 Tobit model
19.3 Tobit model example
19.4 Tobit for lognormal data
19.5 Two-part model in logs
19.6 Selection models
19.7 Nonnormal models of selection
19.8 Prediction from models with outcome in logs
19.9 Endogenous regressors
19.10 Missing data
19.11 Panel attrition
19.12 Additional resources
19.13 Exercises
20 Count-data models
20.1 Introduction
20.2 Modeling strategies for count data
20.3 Poisson and negative binomial models
20.4 Hurdle model
20.5 Finite-mixture models
20.6 Zero-inflated models
20.7 Endogenous regressors
20.8 Clustered data
20.9 Quantile regression for count data
20.10 Additional resources
20.11 Exercises
21 Survival analysis for duration data
21.1 Introduction
21.2 Data and data summary
21.3 Survivor and hazard functions
21.4 Semiparametric regression model
21.5 Fully parametric regression models
21.6 Multiple-records data
21.7 Discrete-time hazards logit model
21.8 Time-varying regressors
21.9 Clustered data
21.10 Additional resources
21.11 Exercises
22 Nonlinear panel models
22.1 Introduction
22.2 Nonlinear panel-data overview
22.3 Nonlinear panel-data example
22.4 Binary outcome and ordered outcome models
22.5 Tobit and interval-data models
22.6 Count-data models
22.7 Panel quantile regression
22.8 Endogenous regressors in nonlinear panel models
22.9 Additional resources
22.10 Exercises
23 Parametric models for heterogeneity and endogeneity
23.1 Introduction
23.2 Finite mixtures and unobserved heterogeneity
23.3 Empirical examples of FMMs
23.4 Nonlinear mixed-effects models
23.5 Linear structural equation models
23.6 Generalized structural equation models
23.7 ERM commands for endogeneity and selection
23.8 Additional resources
23.9 Exercises
24 Randomized control trials and exogenous treatment effects
24.1 Introduction
24.2 Potential outcomes
24.3 Randomized control trials
24.4 Regression in an RCT
24.5 Treatment evaluation with exogenous treatment
24.6 Treatment evaluation methods and estimators
24.7 Stata commands for treatment evaluation
24.8 Oregon Health Insurance Experiment example
24.9 Treatment-effect estimates using the OHIE data
24.10 Multilevel treatment effects
24.11 Conditional quantile TEs
24.12 Additional resources
24.13 Exercises
25 Endogenous treatment effects
25.1 Introduction
25.2 Parametric methods for endogenous treatment
25.3 ERM commands for endogenous treatment
25.4 ET commands for binary endogenous treatment
25.5 The LATE estimator for heterogeneous effects
25.6 Difference-in-differences and synthetic control
25.7 Regression discontinuity design
25.8 Conditional quantile regression with endogenous regressors
25.9 Unconditional quantiles
25.10 Additional resources
25.11 Exercises
26 Spatial regression
26.1 Introduction
26.2 Overview of spatial regression models
26.3 Geospatial data
26.4 The spatial weighting matrix
26.5 OLS regression and test for spatial correlation
26.6 Spatial dependence in the error
26.7 Spatial autocorrelation regression models
26.8 Spatial instrumental variables
26.9 Spatial panel-data models
26.10 Additional resources
26.11 Exercises
27 Semiparametric regression
27.1 Introduction
27.2 Kernel regression
27.3 Series regression
27.4 Nonparametric single regressor example
27.5 Nonparametric multiple regressor example
27.6 Partial linear model
27.7 Single-index model
27.8 Generalized additive models
27.9 Additional resources
27.10 Exercises
28 Machine learning for prediction and inference
28.1 Introduction
28.2 Measuring the predictive ability of a model
28.3 Shrinkage estimators
28.4 Prediction using lasso, ridge, and elasticnet
28.5 Dimension reduction
28.6 Machine learning methods for prediction
28.7 Prediction application
28.8 Machine learning for inference in partial linear model
28.9 Machine learning for inference in other models
28.10 Additional resources
28.11 Exercises
29 Bayesian methods: Basics
29.1 Introduction
29.2 Bayesian introductory example
29.3 Bayesian methods overview
29.4 An i.i.d. example
29.5 Linear regression
29.6 A linear regression example
29.7 Modifying the MH algorithm
29.8 RE model
29.9 Bayesian model selection
29.10 Bayesian prediction
29.11 Probit example
29.12 Additional resources
29.13 Exercises