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Econometric analysis of Cross section and panel data
This book is intended primarily for use in a second-semester course in graduate econometrics, after a first course at the level of Goldberger (1991) or Greene (1997). Parts of the book can be used for special-topics courses, and it should serve as a general reference. My focus on cross section and panel data methods?in particular, what is often dubbed microeconometrics?is novel, and it recognizes that, after coverage of the basic linear model in a first-semester course, an increasingly popular approach is to treat advanced cross section and panel data methods in one semester and time series methods in a separate semester. This division reflects the current state of econometric practice. Modern empirical research that can be fitted into the classical linear model paradigm is becoming increasingly rare. For instance, it is now widely recognized that a student doing research in applied time series analysis cannot get very far by ignoring recent advances in estimation and testing in models with trending and strongly dependent processes. This theory takes a very di?erent direction from the classical linear model than does cross section or panel data analysis. Hamilton?s (1994) time series text demonstrates this di?erence unequivocally. Books intended to cover an econometric sequence of a year or more, beginning with the classical linear model, tend to treat advanced topics in cross section and panel data analysis as direct applications or minor extensions of the classical linear model (if they are treated at all). Such treatment needlessly limits the scope of applications and can result in poor econometric practice. The focus in such books on the algebra and geometry of econometrics is appropriate for a first-semester course, but it results in oversimplification or sloppiness in stating assumptions. Approaches to estimation that are acceptable under the fixed regressor paradigm so prominent in the classical linear model can lead one badly astray under practically important departures from the fixed regressor assumption. Books on ??advanced?? econometrics tend to be high-level treatments that focus on general approaches to estimation, thereby attempting to cover all data configurations? including cross section, panel data, and time series?in one framework, without giving special attention to any. A hallmark of such books is that detailed regularity conditions are treated on par with the practically more important assumptions that have economic content. This is a burden for students learning about cross section and panel data methods, especially those who are empirically oriented: definitions and limit theorems about dependent processes need to be included among the regularity conditions in order to cover time series applications. In this book I have attempted to find a middle ground between more traditional approaches and the more recent, very unified approaches. I present each model and method with a careful discussion of assumptions of the underlying population model. These assumptions, couched in terms of correlations, conditional expectations, conditional variances and covariances, or conditional distributions, usually can be given behavioral content. Except for the three more technical chapters in Part III, regularity conditions?for example, the existence of moments needed to ensure that the central limit theorem holds?are not discussed explicitly, as these have little bearing on applied work. This approach makes the assumptions relatively easy to understand, while at the same time emphasizing that assumptions concerning the underlying population and the method of sampling need to be carefully considered in applying any econometric method. A unifying theme in this book is the analogy approach to estimation, as exposited by Goldberger (1991) and Manski (1988). [For nonlinear estimation methods with cross section data, Manski (1988) covers several of the topics included here in a more compact format.] Loosely, the analogy principle states that an estimator is chosen to solve the sample counterpart of a problem solved by the population parameter. The analogy approach is complemented nicely by asymptotic analysis, and that is the focus here. By focusing on asymptotic properties I do not mean to imply that small-sample properties of estimators and test statistics are unimportant. However, one typically first applies the analogy principle to devise a sensible estimator and then derives its asymptotic properties. This approach serves as a relatively simple guide to doing inference, and it works well in large samples (and often in samples that are not so large). Small-sample adjustments may improve performance, but such considerations almost always come after a large-sample analysis and are often done on a case-bycase basis. The book contains proofs or outlines the proofs of many assertions, focusing on the role played by the assumptions with economic content while downplaying or ignoring regularity conditions. The book is primarily written to give applied researchers a very firm understanding of why certain methods work and to give students the background for developing new methods. But many of the arguments used throughout the book are representative of those made in modern econometric research (sometimes without the technical details). Students interested in doing research in cross section or panel data methodology will find much here that is not available in other graduate texts. I have also included several empirical examples with included data sets. Most of the data sets come from published work or are intended to mimic data sets used in modern empirical analysis. To save space I illustrate only the most commonly used methods on the most common data structures. Not surprisingly, these overlap conxviii Preface siderably with methods that are packaged in econometric software programs. Other examples are of models where, given access to the appropriate data set, one could undertake an empirical analysis. The numerous end-of-chapter problems are an important component of the book. Some problems contain important points that are not fully described in the text; others cover new ideas that can be analyzed using the tools presented in the current and previous chapters. Several of the problems require using the data sets that are included with the book. As with any book, the topics here are selective and reflect what I believe to be the methods needed most often by applied researchers. I also give coverage to topics that have recently become important but are not adequately treated in other texts. Part I of the book reviews some tools that are elusive in mainstream econometrics books? in particular, the notion of conditional expectations, linear projections, and various convergence results. Part II begins by applying these tools to the analysis of singleequation linear models using cross section data. In principle, much of this material should be review for students having taken a first-semester course. But starting with single-equation linear models provides a bridge from the classical analysis of linear models to a more modern treatment, and it is the simplest vehicle to illustrate the application of the tools in Part I. In addition, several methods that are used often in applications?but rarely covered adequately in texts?can be covered in a single framework. I approach estimation of linear systems of equations with endogenous variables from a di?erent perspective than traditional treatments. Rather than begin with simultaneous equations models, we study estimation of a general linear system by instrumental variables. This approach allows us to later apply these results to models with the same statistical structure as simultaneous equations models, including panel data models. Importantly, we can study the generalized method of moments estimator from the beginning and easily relate it to the more traditional three-stage least squares estimator. The analysis of general estimation methods for nonlinear models in Part III begins with a general treatment of asymptotic theory of estimators obtained from nonlinear optimization problems. Maximum likelihood, partial maximum likelihood, and generalized method of moments estimation are shown to be generally applicable estimation approaches. The method of nonlinear least squares is also covered as a method for estimating models of conditional means. Part IV covers several nonlinear models used by modern applied researchers. Chapters 15 and 16 treat limited dependent variable models, with attention given to Preface xix handling certain endogeneity problems in such models. Panel data methods for binary response and censored variables, including some new estimation approaches, are also covered in these chapters. Chapter 17 contains a treatment of sample selection problems for both cross section and panel data, including some recent advances. The focus is on the case where the population model is linear, but some results are given for nonlinear models as well. Attrition in panel data models is also covered, as are methods for dealing with stratified samples. Recent approaches to estimating average treatment e?ects are treated in Chapter 18. Poisson and related regression models, both for cross section and panel data, are treated in Chapter 19. These rely heavily on the method of quasi-maximum likelihood estimation. A brief but modern treatment of duration models is provided in Chapter 20. I have given short shrift to some important, albeit more advanced, topics. The setting here is, at least in modern parlance, essentially parametric. I have not included detailed treatment of recent advances in semiparametric or nonparametric analysis. In many cases these topics are not conceptually di?cult. In fact, many semiparametric methods focus primarily on estimating a finite dimensional parameter in the presence of an infinite dimensional nuisance parameter?a feature shared by traditional parametric methods, such as nonlinear least squares and partial maximum likelihood. It is estimating infinite dimensional parameters that is conceptually and technically challenging. At the appropriate point, in lieu of treating semiparametric and nonparametric methods, I mention when such extensions are possible, and I provide references. A benefit of a modern approach to parametric models is that it provides a seamless transition to semiparametric and nonparametric methods. General surveys of semiparametric and nonparametric methods are available in Volume 4 of the Handbook of Econometrics?see Powell (1994) and Ha?rdle and Linton (1994)?as well as in Volume 11 of the Handbook of Statistics?see Horowitz (1993) and Ullah and Vinod (1993). I only briefly treat simulation-based methods of estimation and inference. Computer simulations can be used to estimate complicated nonlinear models when traditional optimization methods are ine?ective. The bootstrap method of inference and confidence interval construction can improve on asymptotic analysis. Volume 4 of the Handbook of Econometrics and Volume 11 of the Handbook of Statistics contain nice surveys of these topics (Hajivassilou and Ruud, 1994; Hall, 1994; Hajivassilou, 1993; and Keane, 1993). xx Preface On an organizational note, I refer to sections throughout the book first by chapter number followed by section number and, sometimes, subsection number. Therefore, Section 6.3 refers to Section 3 in Chapter 6, and Section 13.8.3 refers to Subsection 3 of Section 8 in Chapter 13. By always including the chapter number, I hope to minimize confusion..NULL.NULL
Ketersediaan
| Call Number | Location | Available |
|---|---|---|
| Tan 330. 015 195 Woo e | PSB lt.dasar - Pascasarjana | 1 |
| Penerbit | Cambridge The MIT Press., 2002 |
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| Edisi | - |
| Subjek | - |
| ISBN/ISSN | - |
| Klasifikasi | - |
| Deskripsi Fisik | - |
| Info Detail Spesifik | - |
| Other Version/Related | Tidak tersedia versi lain |
| Lampiran Berkas | Tidak Ada Data |