Microeconometrics: Methods and Applications A. Colin Cameron and Pravin K. Trivedi
MICROECONOMETRICS: Methods and Applications

Cambridge University Press, New York
May 2005

PART 2  (chapters 4-10)

Part 2 presents the core methods – least squares, method of moments, and maximum likelihood --  of estimation and inference in nonlinear regression models that are central in microeconometrics.  Both the traditional topics as well as more modern topics like quantile regression, sequential estimation, empirical likelihood, bootstrap, and semi- and nonparametric regression are covered.  In general the discussion is at a level intended to provide enough background and detail to enable the practitioner to read and comprehend articles in the leading econometrics journals.  We presume prior familiarity with linear regression analysis.  

Chapter 4 begins with the linear regression model. It then covers at an introductory level quantile regression, which models distributional features other than the conditional mean. It provides a lengthy expository treatment of instrumental variables estimation, a major semiparametric method of causal inference. Chapter 5 presents the most commonly-used estimation methods for nonlinear models, beginning  with the quite general topic of m-estimation, before specialization to maximum likelihood and nonlinear least squares regression. Chapter 6 provides a comprehensive treatment of generalized method of moments, which is a quite general estimation framework, applicable both in linear and nonlinear, and single- and multi-equation settings. The chapter emphasizes  the special case of instrumental variables estimation.
Chapter 7 covers both the classical and bootstrap approaches to hypothesis testing, while Chapter 8 presents relatively more modern methods of model selection and specification analysis. .Because of their importance the bootstrap methods  also get a more detailed stand-alone treatment  in Chapter 11. As much as possible testing methods are presented in a unified manner in these chapters, but specific applications occur throughout the book

Chapter 9 is a stand-alone chapter that presents nonparametric and semiparametric estimation methods that place a flexible structure on the econometric model. Chapter 10 presents the computational methods used to compute the nonlinear estimators presented in chapters 5 and 6. This material becomes especially relevant to the practitioner if an estimator is not automatically computed by an econometrics package.

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