Handbook of Econometrics, Volume 1
Опубликовано на портале: 02-03-2004
New York: North-Holland, 1983, V. 1
Volume One of Elsevier's Handbook of Econometrics covers mathematical and statistical methods in econometrics, econometric models, and estimation and computation. The Handbook of Econometrics aims to serve as a source, reference, and teaching supplement for the field of econometrics, the branch of economics concerned with the empirical estimation of economic relationships. Econometrics is conceived broadly to include not only econometric models and estimation theory but also econometric data analysis, econometric applications in various substantive fields, and the uses of estimated econometric models. Our purpose has been to provide reasonably comprehensive and up-to-date surveys of recent developments and the state of various aspects of econometrics as of the early 1980s, written at a level intended for professional use by economists, econometricians, and statisticians and for use in advanced graduate econometrics courses.
Econometrics is the application of mathematics and statistical methods to the analysis of economic data. Mathematical models help us to structure our perceptions about the forces generating the data we want to analyze, while statistical methods help us to summarize the data, estimate the parameters of our models, and interpret the strength of the evidence for the various hypotheses that we wish to examine. The evidence provided by the data affects our ideas about the appropriateness of the original model and may result in significant revisions of such models. There is, thus, a continuous interplay in econometrics between mathematical-theoretical modeling of economic behavior, data collection, data summarizing, model fitting, and model evaluation. Theory suggests data to be sought and examined; data availability suggests new theoretical questions and stimulates the development of new statistical methods. The examination of theories in light of data leads to their revision. The examination of data in the light of theory leads often to new interpretations and sometimes to questions about its quality or relevance and to attempts to collect new and different data.
In this volume we review only a subset of what might be called "econometrics". The mathematical-theoretical tools required for model building are discussed primarily in the Handbook of Mathematical Economics. Issues of sampling theory, survey design, data collection and editing, and computer programming, all important aspects of the daily life of a practicing econometrician, had, by and large, to be left out of the scope of this Handbook. We concentrate, instead, on statistical problems and economic interpretation issues associated with the modeling and estimation of economic behavioral relationships from already assembled and often badly collected data. If economists had access to good experimental data, or were able to design and to perform the relevant economic experiments, the topics to be covered in such a Handbook would be quite different. The fact that the generation and collection of economic data is mostly outside the hands of the econometrician is the cause of many of the inferential problems which are discussed in this Handbook. The organization of the Handbook follows in relatively systematic fashion the way an econometric study would proceed, starting from basic mathematical and statistical methods and econometric models, proceeding to estimation and computation, through testing, and ultimately to applications and uses. The Handbook also includes a fairly detailed development of time series topics and many other special topics. Part 1 summarizes some basic tools used repeatedly in econometrics, including linear algebra, matrix methods, and statistical theory.
- Linear Algebra and Matrix Methods in Econometrics - Henri Theil
Preface - Zvi Griliches and Michael D. Intriligator
Part 1 - Mathematical and Statistical Methods in Econometrics
Part 2 - Econometric Models
Part 3 - Estimation and Computation
bayesian analysis biased estimation dirty data flawed models linear algebra matrix methods nonlinear regression models simultaneous equations model specification analysis линейная алгебра математическое моделирование матричный подход нелинейная регрессионная модель регрессионная модель численные методы
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