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E-raamat: Micro-Econometrics: Methods of Moments and Limited Dependent Variables

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  • Ilmumisaeg: 28-Sep-2009
  • Kirjastus: Springer-Verlag New York Inc.
  • Keel: eng
  • ISBN-13: 9780387688411
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Micro-Econometrics: Methods of Moments and Limited Dependent VariablesSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 28-Sep-2009
  • Kirjastus: Springer-Verlag New York Inc.
  • Keel: eng
  • ISBN-13: 9780387688411
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WhenIwrotethebookMethodsofMomentsandSemiparametricEco- metrics for Limited Dependent Variable Models published from Springer in 1996, my motivation was clear: there was no book available to convey the latest messages in micro-econometrics. The messages were that most eco- metric estimators can be viewed as method-of-moment estimators and that inferences for models with limited dependent variables (LDV) can be done without going fully parametric. Time has passed and there are now several books available for the same purpose. These days, methods of moments are the mainstay in econometrics, not just in micro-, but also in macro-econometrics. Many papers have been published for semiparametric methods and LDV models. I, myself, learned much over the years since 1996, so much so that my own view on what should be taught, and how, has changed much. Particularly, my exposure to the sample selection and treatment e ect literature has changed the way I look at econometrics now. When I set out to write the second edition of the 1996 book, these changes prompted me to re-title, reorganize, and re-focus the book.

Arvustused

From the reviews of the second edition:

This is a book on microeconometric methods. book is particularly useful both for advanced graduate students and for researchers, whether they are theoretically or empirically oriented. an excellent basis for an advanced course on semi- and non-parametric econometrics, or simply as a valuable reference book. I have found this book to be extremely useful for my own work and I believe that many other readers, either students or researchers, will share that positive experience. (Myoung-jae Lee, The Econometrics Journal, May, 2010)

The book is voluminous at 759 pages. The author discusses various methods of testing and estimation in different models with illustrative empirical examples. The author hopes that theoretically oriented readers will find useful an overview on micro-econometrics and applied researchers will find helpful information on how to apply micro-econometric techniques. This reviewer agrees that the author has succeeded mostly in his aim. book is a valuable addition to the literature on micro-econometrics. (B. L. S. Prakasa Rao, Mathematical Reviews, Issue 2011 c)

Methods of Moments for Single Linear Equation Models
1(52)
Least Squares Estimator (LSE)
1(16)
LSE as a Method of Moment (MOM)
1(1)
Linear Model
1(1)
LSE and Moment Conditions
2(1)
Zero Moments and Independence
3(1)
Asymptotic Properties of LSE
4(1)
LLN and LSE Consistency
5(1)
CLT and N-Consistency
6(1)
LSE Asymptotic Distribution
7(1)
Matrices and Linear Projection
8(2)
R2 and Two Examples
10(3)
Partial Regression
13(2)
Omitted Variable Bias
15(2)
Heteroskedasticity and Homoskedasticity
17(8)
Heteroskedasticity Sources
18(1)
Forms of Heteroskedasticity
18(1)
Heteroskedasticity due to Aggregation
19(1)
Variance Decomposition
20(1)
Analysis of Variance (Anova)
21(2)
Weighted LSE (WLS)
23(1)
Heteroskedasticity Examples
24(1)
Testing Linear Hypotheses
25(6)
Wald Test
25(2)
Remarks
27(1)
Empirical Examples
28(3)
Instrumental Variable Estimator (IVE)
31(11)
IVE Basics
31(1)
IVE in Narrow Sense
31(1)
Instrumental Variable (IV) qualifications
32(2)
Further Remarks
34(1)
IVE Examples
35(4)
IVE with More than Enough Instruments
39(1)
IVE in Wide Sense
39(1)
Various Interpretations of IVE
40(1)
Further Remarks
41(1)
Generalized Method-of-Moment Estimator (GMM)
42(6)
GMM Basics
43(1)
GMM Remarks
44(2)
GMM Examples
46(2)
Generalized Least Squares Estimator (GLS)
48(5)
GLS Basics
48(1)
GLS Remarks
49(1)
Efficiency of LSE, GLS, and GMM
50(3)
Methods of Moments for Multiple Linear Equation Systems
53(38)
System LSE, IVE, and GMM
53(13)
System LSE
53(1)
Multiple Linear Equations
53(1)
System LSE and Motivation
54(1)
Asymptotic Variance
55(1)
System IVE and Rank Condition
56(1)
Moment Conditions
56(1)
System IVE and Separate IVE
57(2)
Identification Conditions
59(1)
System GMM and Link to Panel Data
60(1)
System GMM
60(2)
System GMM and Panel Data
62(4)
Simultaneous Equations and Identification
66(9)
Relationship Between Endogenous Variables
66(2)
Conventional Approach to Rank Condition
68(1)
Simpler Approach to Rank Condition
69(2)
Avoiding Arbitrary Exclusion Restrictions
71(1)
Grouping and Assigning
71(1)
Patterns in Reduced-Form Ratios
72(2)
Meaning of Singular Systems
74(1)
Methods of Moments for Panel Data
75(16)
Panel Linear Model
76(1)
Typical Panel Data Layout
76(2)
Panel Model with a Cross-Section Look
78(1)
Remarks
79(2)
Panel GMM and Constructing Instruments
81(1)
Panel IVE and GMM
81(1)
Instrument Construction
82(1)
Specific Examples of Instruments
82(2)
Within-Group and Between-Group Estimators
84(1)
Within Group Estimator (WIT)
84(2)
Between Group Estimator (BET) and Panel LSE and GLS
86(1)
WIT as Fixed-Effect Estimator
87(4)
M-Estimator and Maximum Likelihood Estimator (MLE)
91(42)
M-Estimator
91(5)
Four Issues and Main Points
91(1)
Remarks for Asymptotic Distribution
92(3)
Computation
95(1)
Maximum Likelihood Estimator (MLE)
96(6)
MLE Basics
97(2)
MLE Identification
99(1)
Asymptotic Variance Relative to M-estimator
100(2)
M-Estimator with Nuisance Parameters
102(6)
Two-Stage M-Estimator Basics
102(1)
Influence Function and Correction Term
103(2)
Various Forms of Asymptotic Variances
105(1)
Examples of Two-Stage M-Estimators
106(1)
No First-Stage Effect
106(2)
First-Stage Effect
108(1)
Method-of-Moment Tests (MMT)
108(4)
Basics
108(1)
Examples
109(3)
Conditional Moment Tests
112(1)
Tests Comparing Two Estimators
112(5)
Two Estimators for the Same Parameter
113(2)
Two Estimators for the Same Variance
115(2)
Three Tests for MLE
117(10)
Wald Test and Nonlinear Hypotheses
118(1)
Likelihood Ratio (LR) Test
119(1)
Restricted LSE
119(2)
Restricted MLE and LR Test
121(1)
Score (LM) Test and Effective Score Test
122(2)
Further Remarks and an Empirical Example
124(3)
Numerical Optimization and One-Step Efficient Estimation
127(6)
Newton--Raphson Algorithm
127(2)
Newton--Raphson Variants and Other Methods
129(2)
One-Step Efficient Estimation
131(2)
Nonlinear Models and Estimators
133(44)
Nonlinear Least Squares Estimator (NLS)
133(12)
Various Nonlinear Models
134(1)
Index Models
134(1)
Transformation-of-Variable Models
135(1)
Mean, Median, and More Nonlinear Models
136(2)
NLS and Its Asymptotic Properties
138(3)
Three Tests for NLS
141(2)
Gauss--Newton Algorithm
143(1)
NLS-LM Test for Linear Models
144(1)
Quantile and Mode Regression
145(11)
Median Regression
146(1)
Quantile Regression
147(1)
Asymmetric Absolute Loss and Quantile Function
147(3)
Quantile Regression Estimator
150(1)
Empirical Examples
151(2)
Mode Regression
153(1)
Treatment Effects
154(2)
GMM for Nonlinear Models
156(12)
GMM for Single Nonlinear Equation
157(2)
Implementation and Examples
159(4)
Three Tests in GMM
163(1)
Efficiency of GMM
164(1)
Weighting Matrices for Dependent Data
165(1)
GMM for Multiple Nonlinear Equations
166(2)
Minimum Distance Estimation (MDE)
168(9)
MDE Basics
169(2)
Various MDE Cases
171(3)
An Empirical Example from Panel Data
174(3)
Parametric Methods for Single Equation LDV Models
177(52)
Binary Response
177(12)
Basics
177(2)
Logit and Probit
179(4)
Marginal Effects
183(2)
Willingness to Pay and Treatment Effect
185(1)
Willingness to Pay (WTP)
185(2)
Remarks for WTP Estimation
187(1)
Comparison to Treatment Effect
188(1)
Ordered Discrete Response
189(9)
Basics
189(2)
Digression on Re-parametrization in MLE
191(1)
Ordered Probit
192(2)
An Empirical Example: Contingent Valuation
194(4)
Count Response
198(8)
Basics and Poisson MLE
198(2)
Poisson Over-dispersion Problem and Other Estimators
200(1)
Negative Binomial (NB) MLE
200(2)
Zero-Inflated Count Responses
202(1)
Methods of Moments
202(1)
An Empirical Example: Inequality Effect on Crime
203(1)
IVE for Count or Positive Responses
204(2)
Censored Response and Related LDV Models
206(10)
Censored Models
206(2)
Censored-Model MLE
208(2)
Truncated Regression and Fractional Response
210(1)
Marginal Effects for Censored/Selection Models
211(2)
Empirical Examples
213(3)
Parametric Estimators for Duration
216(13)
Basics
216(1)
Survival and Hazard Functions
216(2)
Log-Likelihood Functions
218(1)
Exponential Distribution for Duration
219(2)
Weibull Distribution for Duration
221(2)
Unobserved Heterogeneity and Other Parametric Hazards
223(2)
Invariances and Extreme Value Distributions
225(4)
Parametric Methods for Multiple Equation LDV Models
229(74)
Multinomial Choice Models
229(15)
Basics
230(1)
Multinomial Probit (MNP)
231(1)
Choice Probabilities and Identified Parameters
231(2)
Log-Likelihood Function and MOM
233(1)
Implementation
234(1)
Multinomial Logit (MNL)
235(1)
Choice Probabilities and Implications
235(2)
Further Remarks
237(1)
Marginal Effects
238(1)
An Empirical Example: Presidential Election
239(3)
Nested Logit (NES)
242(2)
Methods of Simulated Moments (MSM)
244(8)
Basic Idea with Frequency Simulator
244(3)
GHK Smooth Simulator
247(3)
Methods of Simulated Likelihood (MSL)
250(2)
Sample-Selection Models
252(17)
Various Selection Models
253(2)
Selection Addition, Bias, and Correction Terms
255(2)
MLE
257(1)
Two-Stage Estimator
258(4)
Selection Models for Some LDV's
262(1)
Binary-Response Selection MLE
262(3)
Count-Response Zero-Inflated MLE
265(1)
Count-Response Selection MOM
266(1)
Double and Multiple Hurdle Models
267(2)
LDV's with Endogenous Regressors
269(16)
Five Ways to Deal with Endogenous LDV's
270(3)
A Recursive System
273(2)
Simultaneous Systems in LDV's and Coherency Conditions
275(1)
Incoherent System in Binary Responses
275(1)
Coherent System in Censored Responses
275(2)
Control Function Approach with a Censored Response
277(1)
Simultaneous Systems in Latent Continuous Variables
278(1)
Motivations and Justifications
278(2)
Individual RF-Based Approach with MDE
280(3)
An Empirical Example
283(2)
Panel-Data Binary-Response Models
285(11)
Panel Conditional Logit
285(1)
Two Periods with Time-Varying Intercept
286(2)
Three or More Periods
288(1)
Digression on Sufficiency
289(2)
Unrelated-Effect Panel Probit
291(2)
Dynamic Panel Probit
293(3)
Competing Risks
296(7)
Observed Causes and Durations
296(2)
Latent Causes and Durations
298(2)
Dependent Latent Durations and Identification
300(3)
Kernel Nonparametric Estimation
303(60)
Kernel Density Estimator
303(10)
Density Estimators
303(4)
Density-Derivative Estimators
307(2)
Further Remarks
309(3)
Adaptive Kernel Estimator
312(1)
Consistency and Bandwidth Choice
313(9)
Bias and Order of Kernel
313(3)
Variance and Consistency
316(2)
Choosing Bandwidth with MSE
318(2)
Choosing Bandwidth with Cross-Validation
320(2)
Asymptotic Distribution
322(7)
Lindeberg CLT
323(1)
Confidence Intervals
324(2)
Confidence Bands
326(1)
An Empirical Example of Confidence Bands
327(2)
Finding Modes
329(4)
Graphical Detection
329(1)
A Multimodality Test
330(2)
An Empirical Example: World Income Distribution
332(1)
Survival and Hazard Under Random Right-Censoring
333(11)
Nelson--Aalen Cumulative-Hazard Estimator
333(3)
Survival-Function Estimators
336(1)
Cumulative-Hazard-Based Estimator
336(2)
Kaplan--Meier Product Limit Estimator
338(2)
Density and Hazard Estimators
340(1)
Kernel Density Estimator
340(2)
Kernel Hazard Estimator
342(2)
Kernel Nonparametric Regression
344(9)
Overview
344(3)
Consistency
347(1)
Asymptotic Distribution
348(2)
Choosing Smoothing Parameter and Kernel
350(3)
Topics in Kernel Nonparametric Regression
353(10)
Mixed Regressors and Structural Breaks
353(3)
Estimating Derivatives
356(3)
Nonparameric MLE and Quantile Regression
359(1)
Local Linear Regression
360(3)
Bandwidth-Free Semiparametric Methods
363(78)
Quantile Regression for LDV models
363(20)
Binary and Multinomial Responses
364(3)
Ordered Discrete Responses
367(2)
Count Responses
369(1)
Main Idea
369(2)
Quantile Regression of a Transformed Variable
371(1)
Further Remarks
372(1)
Censored Responses
373(1)
Censored Quantile Estimators
373(2)
Two-Stage Procedures and Unobserved Censoring Point
375(4)
An Empirical Example
379(2)
Median Rational Expectation
381(2)
Methods Based on Modality and Symmetry
383(14)
Mode Regression for Truncated Model and Robustness
384(2)
Symmetrized LSE for Truncated and Censored Models
386(1)
Symmetrically Trimmed LSE
386(2)
Symmetrically Censored LSE
388(1)
Partial-Symmetry-Based Estimators
389(1)
Quadratic Mode Regression Estimator (QME)
389(2)
Remarks for QME
391(2)
Winsorized Mean Estimator (WME)
393(3)
Estimators for Censored-Selection Models
396(1)
Rank-Based Methods
397(18)
Single Index Models (SIM)
398(1)
Single Index and Transformation of Variables
398(1)
Simple Single-Index Model Estimator
399(1)
Double or Multiple Indices
400(1)
Kendall Rank Correlation Estimator (KRE)
401(1)
Estimator and Identification
402(2)
Asymptotic Distribution
404(2)
Randomly Censored Duration with Unknown Transformation
406(2)
Spearman Rank Correlation Estimator (SRE)
408(2)
Pairwise-Difference Rank for Response Transformations
410(1)
Main Idea and Estimator
410(1)
Remarks
411(1)
An Empirical Example
412(1)
Rank-Based Estimation of Transformation Function
413(2)
Differencing-Based Estimators
415(6)
Pairwise-Difference for Censored and Truncated Models
415(1)
Differencing Idea
415(1)
Censored Regression
416(1)
Truncated Regression
417(1)
Differencing Estimator for Semi-linear Models
418(3)
Estimators for Duration Models
421(10)
Discrete Durations
421(3)
Piecewise Constant Hazard
424(1)
Discrete-Time-Varying Regressors
424(1)
Ordered Discrete Response Model for Time-Constant Regressors
425(3)
An Empirical Example
428(1)
Partial Likelihood Estimator (PLE)
429(2)
Integrated-Moment Specification Tests
431(10)
Integrated Moment Tests (IMT)
432(2)
Integrated Regression Function Specification Test
434(1)
Main Idea
434(1)
Bootstrap Inference
435(2)
Further Remarks
437(1)
Conditional Kolmogorov Test
437(4)
Bandwidth-Dependent Semiparametric Methods
441(90)
Two-Stage Estimator with Nonparametric First-Stage
441(8)
Density or Conditional Mean for First Stage
442(2)
Other Nonparametric Nuisance Parameters
444(2)
Examples
446(1)
Moments with Nonparametric Nuisance Parameters
446(1)
Nonparametric WLS
446(1)
Nonparametric Heteroskedasticity Test
447(2)
Nonparametric TSE for Endogenous Regressors
449(7)
Linear Model and Nonparametric 2SLSE
449(2)
Smooth Nonlinear Models and Nonparametric SUB
451(1)
Non-smooth Models and Nonparametric SUB
452(2)
Nonparametric Second-Stage and Integral Equation
454(2)
Control-Function (CF) Approaches
456(8)
Linear Models
456(2)
Nonlinear Models
458(2)
Average Structural Function (ASF)
460(1)
Pairwise Differencing for Nonparametric CF
461(1)
Nonparametric Second-Stage
462(2)
Single Index Models
464(15)
Density-Weighted Average Derivative (WADE)
464(3)
Average Derivative Estimators (ADE)
467(1)
Motivations
467(1)
Estimators
468(1)
Remarks
469(2)
Nonparametric LSE
471(2)
Quasi-MLE for Binary Response
473(2)
Discrete Regressors
475(1)
Extensions to Multiple Index Models
476(3)
Semi-linear Models
479(9)
Two-Stage Procedure
479(4)
Empirical Application: Hedonic Price Indices
483(2)
Pair-wise Differencing for Semi-linear LDV Models
485(3)
Additive Models
488(9)
Backfitting
488(1)
Smoothed Backfitting
489(2)
Marginal Integration
491(3)
Further Generalizations
494(3)
Transformation of Response Variables
497(13)
Density-Weighted Response Approach
497(1)
Main Idea
497(2)
Asymptotic Distribution
499(1)
Further Remarks
500(1)
Extensions of Density-Weighted Response Approach
501(1)
Endogenous Regressors
501(3)
Ordered Discrete Response
504(2)
Panel Binary Response
506(2)
Unknown Transformation of Response
508(2)
Nonparametric Specification and Significance Tests
510(21)
Omitted-Variable-Based LM-Type Tests
510(3)
Wald-Type Tests with Parametric and Nonparametric Fits
513(3)
LR-Type Tests
516(2)
Model-Selection-Based Tests
518(2)
Single-Index Model Fitness Tests
520(4)
Nonparametric Significance Tests
524(1)
Two-bandwidth Tests
524(2)
One-Bandwidth Test
526(1)
Cross-Validation Approach for Mixed Regressors
527(1)
Non-nested Model Tests and Multi-sample Tests
527(1)
LM-Type Tests for Non-nested Models
527(1)
LR-Type Test for Non-nested Models
528(1)
Multi-sample Tests for Multiple Treatments
529(2)
Appendix I: Mathematical Backgrounds and
Chapter Appendices
531(84)
Mathematical and Statistical Backgrounds
531(18)
Bounds, Limits, and Functions
531(3)
Continuity and Differentiability of Functions
534(2)
Probability Space and Random Variables
536(2)
Integrals
538(3)
Density and Conditional Mean
541(3)
Dominated and Monotone Convergences
544(1)
Convergence of Random Variables and Laws
545(2)
LLN and CLT
547(2)
Appendix for
Chapter 2
549(11)
Seemingly Unrelated Regression (SUR)
549(1)
Two-Equation SUR
549(1)
Asymptotic Distribution
550(1)
Efficiency Gain
551(2)
On System GMM Efficiency Gain
553(1)
Classical Simultaneous Equation Estimators
554(1)
Full-Information MLE (FIML)
554(3)
Limited-Information MLE (LIML)
557(1)
Three-Stage LSE (3SLSE)
558(2)
Appendix for
Chapter 3
560(6)
Details on Four Issues for M-Estimator
560(3)
MLE with LSE First-Stage and Control Function
563(3)
Appendix for
Chapter 4
566(6)
LR and LM tests in NLS
566(2)
Topics for GMM
568(1)
LR and LM tests
568(2)
Optimal Weighting Matrix
570(1)
Over-Identification Test
570(2)
Appendix for
Chapter 5
572(3)
Proportional Hazard and Accelerated Failure Time
572(1)
Proportional Hazard
572(1)
Accelerated Failure Time (AFT)
573(1)
Further Remarks
574(1)
Appendix for
Chapter 6
575(9)
Type-I Extreme Errors to Multinomial Logit
575(2)
Two-Level Nested Logit
577(1)
Lower-Level MNL
577(2)
Upper-Level MNL
579(1)
Final-Stage MLE and Remarks
580(2)
Asymptotic Distribution of MSM estimators
582(2)
Appendix for
Chapter 7
584(12)
Other Density Estimation Ideas
584(1)
Nearest-Neighbor Method
584(1)
Maximum Penalized Likelihood Estimator
585(1)
Series Approximation
585(2)
Asymptotic Distribution for Kernel Regression Estimator
587(2)
Other Nonparametric Regression Methods
589(1)
Nearest-Neighbor Estimator
589(1)
Spline Smoothing
590(1)
Series Approximation
591(3)
Asymptotic Normality of Series Estimators
594(2)
Appendix for
Chapter 8
596(14)
U-Statistics
596(1)
Motivations
596(1)
Symmetrization
597(2)
Examples
599(1)
GMM with Integrated Squared Moments
600(3)
Goodness-of-Fit Tests for Distribution Functions
603(1)
Brownian Motion and Brownian Bridge
603(2)
Kolmogorov--Smirnov (KS) test
605(1)
Cramer--von-Mises (CM) and Anderson--Darling (AD) tests
606(1)
Joint Test for All Quantiles
607(3)
Appendix for
Chapter 9
610(5)
Asymptotic Variance of Marginal Integration
610(3)
CLT for Degenerate U-Statistics
613(2)
Appendix II: Supplementary Topics
615(90)
Appendix for Hypothesis Test
615(17)
Basics
615(2)
Comparison of Tests and Local Alternatives
617(1)
Efficacy and Relative Efficiency
617(3)
Finding Distribution Under Alternatives
620(2)
Wald Test Under Local Alternatives to Linear Hypotheses
622(1)
Non-nested Hypothesis Testing
623(1)
Terminologies
623(2)
LR Test for Strictly Non-nested Hypotheses
625(2)
Centered LR Test and Encompassing
627(1)
J-Test and Score Test Under Artificial Nesting
627(1)
Pearson Chi-Square Goodness-of-Fit Test
628(4)
Stratified Sampling and Weighted M-Estimator
632(14)
Three Stratified Sampling Methods
633(1)
Standard Stratified Sampling (SSS)
633(1)
Variable Probability Sampling (VPS)
634(1)
Multinomial Sampling (MNS)
635(1)
Infeasible MLE
635(2)
Weighted M-Estimator
637(1)
Consistency
638(1)
Asymptotic Distribution
638(2)
An Example: Weighted M-Estimator for Mean
640(2)
Logit Slope Consistency in Response-Based Samples
642(2)
Truncated Samples with Zero Cell Probability
644(1)
Truncated Count Response Under On-Site Sampling
645(1)
Empirical Likelihood Estimator
646(8)
Empirical Likelihood (EL) Method
647(3)
Exponential Tilting Estimator
650(2)
Minimum Discrepancy Estimator
652(2)
Stochastic-Process Convergence and Applications
654(11)
Motivations
654(2)
Stochastic Process and Weak Convergence
656(1)
Stochastic Process
656(3)
Weak Convergence
659(2)
Stochastically Equicontinuous Empirical Processes
661(2)
Applications
663(2)
Goodness-of-Fit Tests with Nuisance Parameters
665(10)
Some Stochastic Integrals
665(3)
Weak Limit of GOF tests with Nuisance Parameters
668(3)
Asymptotically Distribution-Free (ADF) Transformation
671(4)
Bootstrap
675(30)
Review on Asymptotic Statistical Inference
675(2)
Bootstrap for Distribution Functions
677(6)
Main Idea
677(3)
Percentile-t, Centered-Percentile, and Percentile
680(2)
Transformation and Percentile Method Invariance
682(1)
Bootstrap Consistency and Confidence Intervals
683(4)
Defining Bootstrap Consistency
683(1)
Bootstrap Consistency with Empirical Processes
684(1)
Confidence Intervals with Bootstrap Quantiles
685(2)
High-Order Improvement for Asymptotic Normality
687(3)
Edgeworth Expansion
690(7)
Cumulant Generating Function
690(2)
Density of Normalized Sums
692(2)
Distribution Function of Normalized Sums
694(1)
Moments
695(2)
Other Bootstrap Topics
697(8)
Bootstrap Test
697(2)
Bootstrap Bias-Correction
699(3)
Estimating Asymptotic Variance with Bootstrap Quantiles
702(1)
Bootstrap Iteration and Pre-pivoting
702(3)
Appendix III: Select Gauss Programs
705(22)
LSE, IVE, GMM and Wald Test
705(2)
System LSE
707(1)
Method-of-Moment Test for Symmetry
708(1)
Quantile Regression
709(1)
Univariate Parametric LDV Models
710(4)
Probit
710(1)
Ordered Probit
711(1)
Tobit
712(1)
Weibull MLE under Random Censoring
713(1)
Multivariate Parametric LDV Models
714(2)
Multinomial Logit (MNL)
714(1)
Two-Stage Estimator for Sample Selection
715(1)
Nonparametric Regression and Hazard
716(4)
Univariate Density
716(1)
Bivariate Regression Function
717(1)
Regression Derivative and Confidence Interval
718(2)
Bandwidth-Free Semiparametric Methods
720(3)
Winsorized Mean Estimator (WME) for Censored Model
720(2)
Differencing for Semi-Linear Model
722(1)
Bandwidth-Dependent Semiparametric Methods
723(4)
Two-Stage Estimator for Semi-Linear Model
723(2)
Quasi-MLE for Single-Index Binary Response
725(2)
References 727(32)
Index 759
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