Generalized Method of Moments Estimation,9780521660136

Generalized Method of Moments Estimation

by
ISBN13: 9780521660136
ISBN10: 0521660130
Format: Hardcover
Pub. Date: 1999-04-13
Publisher(s): Cambridge University Press
List Price: $139.65

Buy New

Usually Ships in 8 - 10 Business Days.
$133.00
Add to Cart

Rent Textbook

Select for Price
Add to Cart
There was a problem. Please try again later.

Rent Digital

Rent Digital Options
Online:180 Days access
Downloadable:180 Days
$48.00
Online:1825 Days access
Downloadable:Lifetime Access
$59.99
*To support the delivery of the digital material to you, a digital delivery fee of $3.99 will be charged on each digital item.
$48.00*
Add to Cart

Used Textbook

We're Sorry
Sold Out

Buy from our Marketplace starting at $90.43

How Marketplace Works:

  • This item is offered by an independent seller and not shipped from our warehouse
  • Item details like edition and cover design may differ from our description; see seller's comments before ordering.
  • Sellers much confirm and ship within two business days; otherwise, the order will be cancelled and refunded.
  • Marketplace purchases cannot be returned to eCampus.com. Contact the seller directly for inquiries; if no response within two days, contact customer service.
  • Additional shipping costs apply to Marketplace purchases. Review shipping costs at checkout.

Summary

The generalized method of moments (GMM) estimation has emerged over the last decade as providing a ready to use, flexible tool of application to a large number of econometric and economic models by relying on mild, plausible assumptions. The principal objective of this volume, the first devoted entirely to the GMM methodology, is to offer a complete and up to date presentation of the theory of GMM estimation as well as insights into the use of these methods in empirical studies. It is also designed to serve as a unified framework for teaching estimation theory in econometrics. Contributors to the volume include well-known authorities in the field based in North America, the UK/Europe, and Australia. The work is likely to become a standard reference for graduate students and professionals in economics, statistics, financial modeling, and applied mathematics.

Table of Contents

Preface 1(2)
Introduction to the Generalized Method of Moments Estimation
3(28)
David Harris
Laszlo Matyas
The Method of Moments
4(5)
Moment Conditions
4(3)
Method of Moments Estimation
7(2)
Generalized Method of Moments (GMM) Estimation
9(2)
Asymptotic Properties of the GMM Estimator
11(18)
Consistency
12(5)
Asymptotic Normality
17(4)
Asymptotic Efficiency
21(1)
Illustrations
22(1)
GMM With i.i.d. Observations
23(1)
Regression With Instrumental Variables
24(1)
Choice of Moment Conditions
25(4)
Conclusion
29(2)
References
29(2)
GMM Estimation Techniques
31(32)
Masao Ogaki
GMM Estimation
31(4)
GMM Estimator
31(3)
Important Assumptions
34(1)
Nonlinear Instrumental Variable Estimation
35(1)
GMM Applications with Stationary Variables
36(5)
Euler Equation Approach
36(1)
Habit Formation
37(2)
Linear Rational Expectations Models
39(2)
GMM in the Presence of Nonstationary Variables
41(14)
The Cointegration--Euler Equation Approach
42(2)
Structural Error Correction Models
44(1)
An Exchange Rate Model with Sticky Prices
45(6)
The Instrumental Variables Methods
51(4)
Some Aspects of GMM Estimation
55(8)
Numerical Optimization
55(1)
The Choice of Instrumental Variables
56(1)
Normalization
57(2)
References
59(4)
Covariance Matrix Estimation
63(33)
Matthew J. Cushing
Mary G. McGarvey
Preliminary Results
63(1)
The Estimated
64(3)
Kernel Estimator of VT
67(10)
Weighted Autocovariance Estimators
68(1)
Weighted Periodogram Estimators
69(2)
Computational Considerations
71(1)
Spectral Interpretation
72(1)
Asymptotic Properties of Scale Parameter Estimators
73(4)
Optimal Choice of Covariance Estimator
77(5)
Optimal Choice of Scale Parameter Window
77(1)
Optimal Choice of Scale Parameter
78(2)
Other Estimation Strategies
80(2)
Finite Sample Properties of HAC Estimators
82(14)
Monte Carlo Evidence
82(2)
Regression Model
84(2)
Heteroskedastic but Serially Uncorrelated Errors
86(2)
Autocorrelated but Homoskedastic Errors
88(2)
Autocorrelated and Heteroskedastic Errors
90(3)
Summary
93(1)
References
94(2)
Hypothesis Testing in Models Estimated by GMM
96(32)
Alastair R. Hall
Identifying and Overidentifying Restrictions
99(2)
Testing Hypotheses About E[f(xt, O0)]
101(3)
Testing Hypotheses About Subsets of E[f (xt, θ0)]
104(4)
Testing Hypotheses About the Parameter Vector
108(3)
Testing Hypotheses About Structural Stability
111(7)
Testing Non--nested Hypotheses
118(4)
Conditional Moment and Hausman Tests
122(6)
Conditional Moment Tests
123(1)
Hausman Tests
124(1)
References
125(3)
Finite Sample Properties of GMM estimators and Tests
128(21)
Jan M. Podivinsky
Related Theoretical Literature
129(2)
Simulation Evidence
131(10)
Asset Pricing Models
132(2)
Business Cycle Models
134(1)
Stochastic Volatility Models
135(3)
Inventory Models
138(1)
Models of Covariance Structures
139(1)
Other Applications of GMM
140(1)
Extensions of Standard GMM
141(4)
Estimation
141(3)
Testing
144(1)
Concluding Comments
145(4)
References
145(4)
GMM Estimation of Time Series Models
149(22)
David Harris
Estimation of Moving Average Models
150(7)
A Simple Estimator of an MA(1) Model
150(3)
Estimation by Autoregressive Approximation
153(1)
The Durbin Estimator
154(1)
The Galbraith and Zinde-Walsh Estimator
155(1)
A Finite Order Autoregressive Approximation
155(1)
Higher Order MA Models
156(1)
Estimation of ARMA Models
157(5)
IV Estimation of AR Coefficients
157(1)
The ARMA(1,1) Model
158(1)
A Simple IV Estimator
159(1)
Optimal IV Estimation
160(2)
Applications to Unit Root Testing
162(9)
Testing for an Autoregressive Unit Root
162(3)
Testing for a Moving Average Unit Root
165(2)
Appendix: Proof of Theorem 6.1
167(2)
References
169(2)
Reduced Rank Regression Using GMM
171(40)
Frank Kleibergen
GMM--2SLS Estimators in Reduced Rank Models
173(11)
Reduced Rank Regression Models
173(1)
GMM--2SLS Estimators
174(4)
Limiting Distributions for the GMM-2SLS Cointegration Estimators
178(6)
Testing Cointegration Using GMM-2SLS Estimators
184(3)
Cointegration in a Model with Heteroskedasticity
187(4)
Generalized Least Squares Cointegration Estimators
187(4)
Cointegration with Structural Breaks
191(5)
Conclusion
196(15)
Appendix
197(12)
References
209(2)
Estimation of Linear Panel Data Models Using GMM
211(37)
Seung C. Ahn
Peter Schmidt
Preliminaries
212(6)
General Model
213(1)
GMM and Instrumental Variables
214(3)
Strictly Exogenous Instruments and Random effects
217(1)
Strictly Exogenous Instruments and Fixed Effects
217(1)
Models with Weakly Exogenous Instruments
218(4)
The Forward Filter Estimator
219(1)
Irrelevance of Forward Filtering
220(1)
Semiparametric Efficiency Bound
221(1)
Models with Strictly Exogenous Regressors
222(8)
The Hausman and Taylor, Amemiya and MaCurdy and Breusch, Mizon and Schmidt Estimators
223(3)
Efficient GMM Estimation
226(3)
GMM with Unrestricted Σ
229(1)
Simultaneous Equations
230(5)
Estimation of a Single Equation
231(3)
System of Equations Estimation
234(1)
Dynamic Panel Data Models
235(10)
Moment Conditions Under Standard Assumptions
236(2)
Some Alternative Assumptions
238(3)
Estimation
241(1)
Notation and General Results
241(2)
Linear Moment Conditions and Instrumental Variables
243(1)
Linearized GMM
244(1)
Conclusion
245(3)
References
246(2)
Alternative GMM Methods for Nonlinear Panel Data Models
248(27)
Jorg Breitung
Michael Lechner
A Class of Nonlinear Panel Data Models
250(3)
GMM Estimators for the Conditional Mean
253(2)
Higher Order Moment Conditions
255(1)
Selecting Moment Conditions: The Gallant--Tauchen Approach
256(2)
A Minimum Distance Approach
258(1)
Finite Sample Properties
259(4)
Data Generating Process
259(1)
Estimators
260(3)
Results
263(2)
An Application
265(2)
Concluding Remarks
267(8)
References
273(2)
Simulation Based Method of Moments
275(26)
Roman Liesenfeld
Jorg Breitung
General Setup and Applications
277(4)
The Method of Simulated Moments (MSM)
281(5)
Indirect Inference Estimator
286(4)
The SNP Approach
290(2)
Some Practical Issues
292(4)
Drawing Random Numbers and Variance Reduction
292(2)
The Selection of the Auxiliary Model
294(1)
Small Sample Properties of the Indirect Inference
295(1)
Conclusion
296(5)
References
297(4)
Logically Inconsistent Limited Dependent Variables Models
301(12)
J. S. Butter
Gabriel Picone
Logical Inconsistency
302(3)
Identification
305(4)
Estimation
309(1)
Conclusion and Extensions
310(3)
References
311(2)
Index 313

An electronic version of this book is available through VitalSource.

This book is viewable on PC, Mac, iPhone, iPad, iPod Touch, and most smartphones.

By purchasing, you will be able to view this book online, as well as download it, for the chosen number of days.

Digital License

You are licensing a digital product for a set duration. Durations are set forth in the product description, with "Lifetime" typically meaning five (5) years of online access and permanent download to a supported device. All licenses are non-transferable.

More details can be found here.

A downloadable version of this book is available through the eCampus Reader or compatible Adobe readers.

Applications are available on iOS, Android, PC, Mac, and Windows Mobile platforms.

Please view the compatibility matrix prior to purchase.