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1
Online Resource
Online Resource
Handbook of econometrics : volume 1 (1983)
Amsterdam : North-Holland Pub. Co
Details
UID:
gbv_789694654
Format: Online Ressource (xxvii, 771 pages) , illustrations.
Edition: Online-Ausg.
ISBN: 0444861858 , 9780444861856
Series Statement: Handbooks in economics bk. 2
Content: The Handbook is a definitive reference source and teaching aid for econometricians. It examines models, estimation theory, data analysis and field applications in econometrics. Comprehensive surveys, written by experts, discuss recent developments at a level suitable for professional use by economists, econometricians, statisticians, and in advanced graduate econometrics courses
Content: The Handbook is a definitive reference source and teaching aid for econometricians. It examines models, estimation theory, data analysis and field applications in econometrics. Comprehensive surveys, written by experts, discuss recent developments at a level suitable for professional use by economists, econometricians, statisticians, and in advanced graduate econometrics courses
Note: Includes bibliographical references and indexes , Mathematical and Statistical Methods in Econometrics. Linear algebra and matrix methods in econometrics (H. Theil)Statistical theory and econometrics (A. Zellner) -- Econometric Models. Economic and econometric models (M.D. Intriligator) -- Identification (C. Hsiao) -- Model choice and specification analysis (E.E. Leamer) -- Estimation and Computation. Non-linear regression models (T. Amemiya) -- Specification and estimation of simultaneous equation models (J.A. Hausman) -- Exact small sample theory in the simultaneous equations model (P.C.B. Philipps) -- Bayesian analysis of simultaneous equation systems (J.H. Drèze, J.-F. Richard) -- Biased estimation (G.G. Judge, M.E. Bock) -- Estimation for dirty data and flawed models (W.S. Krasker, E. Kuh and R.E. Welsch) -- Compuational problems and methods (R.E. Quandt).
Language: English
Keywords: Electronic books
URL: Volltext  (Deutschlandweit zugänglich)
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Associated Volumes
  • 2
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    Chapter 1 Linear algebra and matrix methods in econometrics (1983)
    Details
    UID:
    gbv_1831646390
    ISBN: 0444861858
    Content: Vectors and matrices played a minor role in the econometric literature published before Second World War, but they have become an indispensable tool in the past several decades. Part of this development results from the importance of matrix tools for the statistical component of econometrics; another reason is the increased use of matrix algebra in the economic theory underlying econometric relations. This chapter presents a selective survey of both areas. It reviews the concepts of linear dependence and orthogonality of vectors and the rank of a matrix. A major reason related to the usefulness of matrix methods is that many topics in econometrics have a multivariate character. The chapter illustrates the convenience of matrices for linear systems. The expression linear algebra should not be interpreted in the sense that matrices are useful for linear systems only. Vectors and matrices are important in the statistical component of econometrics. A general method of estimation is maximum likelihood (ML) that can be shown to have certain optimal properties for large samples under relatively weak conditions. The derivation of the ML estimates and their large sample covariance matrix involves the information matrix, which is (apart from sign) the expectation of the matrix of second-order derivatives of the log-likelihood function with respect to the parameters. The prominence of ML estimation in recent years has greatly contributed to the increased use of matrix methods in econometrics.
    In: Handbook of econometrics, Amsterdam : North-Holland Pub. Co, 1983, (1983), Seite 3-65, 0444861858
    In: 9780444861856
    In: year:1983
    In: pages:3-65
    Language: English
    DOI: 10.1016/S1573-4412(83)01005-3
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  • 3
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    Chapter 10 Biased estimation (1983)
    Details
    UID:
    gbv_1831646307
    ISBN: 0444861858
    Content: This chapter presents the conventional linear-statistical models, estimators, and hypothesis testing framework. Sampling theory and Bayes estimators that permit sample information and various types of nonsample information are specified and evaluated. In the chapter, testing frameworks are specified for evaluating the compatibility of the sample information. The various types of nonsample information and the corresponding pretest estimators are derived, compared, and evaluated. The chapter discusses inadmissibility of the least-squares estimator and a range of Stein-rule estimators for alternative loss functions and design matrices. Alternatives to least squares are considered for the stochastic regressor case. The chapter also discusses the problem of collinear data and the ridge-type and general minimax estimators. The statistical implications of these biased alternatives for econometric theory and practice are discussed in the chapter.
    In: Handbook of econometrics, Amsterdam : North-Holland Pub. Co, 1983, (1983), Seite 599-649, 0444861858
    In: 9780444861856
    In: year:1983
    In: pages:599-649
    Language: English
    DOI: 10.1016/S1573-4412(83)01014-4
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  • 4
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    Chapter 11 Estimation for dirty data and flawed models (1983)
    Details
    UID:
    gbv_1831646293
    ISBN: 0444861858
    Content: This chapter focuses on resistant estimation procedures and methods for evaluating the impact of particular data elements on regression estimates. Model builders using macroeconomic time series are often plagued by occasional unusual events, leading them to decrease the weights to be attached to these data in the spirit of resistant estimation. Even when there are good data and theory that correspond reasonably well to the process being modeled, there are episodic model failures. The chapter discusses some model failures that can arise in practice. It describes recent developments in methods for the detection of influential data in regression and discusses several issues about inference in the resistant case and the main theoretical foundations of robust and bounded-influence (BIF) estimation. The chapter presents an example of BIF applied to the HarrisonRubinfeld large cross-section hedonic price index. The chapter also presents some recent results on instrumental-variables bounded-influence estimation, and discusses resistant estimation for time-series models.
    In: Handbook of econometrics, Amsterdam : North-Holland Pub. Co, 1983, (1983), Seite 651-698, 0444861858
    In: 9780444861856
    In: year:1983
    In: pages:651-698
    Language: English
    DOI: 10.1016/S1573-4412(83)01015-6
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  • 5
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    Chapter 12 Computational problems and methods (1983)
    Details
    UID:
    gbv_1831646285
    ISBN: 0444861858
    Content: This chapter discusses some of the important computational methods and problems. The emphasis is on algorithms and general procedures for solving problems. The chapter discusses matrix methods involved in estimating the parameters of single and simultaneous equation models. It discusses various aspects of numerical optimization. These methods become relevant whenever the first-order conditions for a maximum are not linear in the parameters to be estimated. The chapter presents a survey of the typical functions that are optimized and discusses the basic theory of optimization. Special purpose algorithms and simplifications useful in econometrics; and some further aspects of algorithms are discussed. The chapter discusses particular difficulties encountered only in problems of certain types and focuses on numerical integration and random number generation.
    In: Handbook of econometrics, Amsterdam : North-Holland Pub. Co, 1983, (1983), Seite 699-764, 0444861858
    In: 9780444861856
    In: year:1983
    In: pages:699-764
    Language: English
    DOI: 10.1016/S1573-4412(83)01016-8
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  • 6
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    Chapter 2 Statistical theory and econometrics (1983)
    Details
    UID:
    gbv_1831646382
    ISBN: 0444861858
    Content: Research in statistical theory has yielded useful procedures for learning from data one of the principal objectives of econometrics and science. This research has produced a large number of probability models for observations that are widely utilized in econometrics and other sciences, which are reviewed in the chapter. This chapter also reviews techniques for estimation, prediction, and testing that enable investigators to solve inference problems in a scientific manner. The importance of utilizing sound, scientific methods in analyzing data, and drawing conclusions from them is obvious, because such conclusions often have crucial implications for economic policy making and the progress of economic science. On the other hand, it is a fact that statistical and econometric analysis frequently is a mixture of science and art. A challenge for statistical theory is to provide fruitful, formal procedures that are helpful in solving model formulation problems. The two major approaches to statistical inference discussed in this chapter are the sampling theory approach and the Bayesian approach. Among several controversies, there is controversy regarding the implications of the likelihood principle for econometric and statistical practice.
    In: Handbook of econometrics, Amsterdam : North-Holland Pub. Co, 1983, (1983), Seite 67-178, 0444861858
    In: 9780444861856
    In: year:1983
    In: pages:67-178
    Language: English
    DOI: 10.1016/S1573-4412(83)01006-5
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  • 7
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    Chapter 3 Economic and econometric models (1983)
    Details
    UID:
    gbv_1831646374
    ISBN: 0444861858
    Content: This chapter discusses models used in econometrics. Models play a major role in all econometric studies, whether theoretical or applied. Defining econometrics as the branch of economics concerned with the empirical estimation of economic relationships, models, together with data, represent the basic ingredients of any econometric study. The theory of the phenomena under investigation is developed into a model that is further refined into an econometric model. This model is then estimated on the basis of data pertaining to the phenomena under investigation using econometric techniques. The investigation of economic and econometric models indicates that there is a wide range of models and applications. There are many approaches to modeling, and even in the standard linear stochastic algebraic model of econometrics there are many alternative specifications available. These models have been applied in many different areasin fact, in virtually all areas of economics and in some related social sciences. The models have been used for various purposes, including structural analysis, forecasting, and policy evaluation.
    In: Handbook of econometrics, Amsterdam : North-Holland Pub. Co, 1983, (1983), Seite 181-221, 0444861858
    In: 9780444861856
    In: year:1983
    In: pages:181-221
    Language: English
    DOI: 10.1016/S1573-4412(83)01007-7
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  • 8
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    Chapter 4 Identification (1983)
    Details
    UID:
    gbv_1831646366
    ISBN: 0444861858
    Content: The study of identification has been clearly linked to the design of experiments. In a well-designed experiment the treatment group and the control group are similar in every aspect, except for the treatment. The difference in response may therefore be attributed to the treatment and the parameters of interest are identified. An extensive study of the identifiability conditions for the simultaneous equations models under various assumptions about the underlying structures was provided by Fisher. This chapter discusses the development of the subject since the publication of Fisher's book. It discusses the basic concepts of identification and some identifiability criteria for contemporaneoussimultaneous equation models under linear constraints. The chapter discusses criteria for models subject to nonlinear continuous differentiable constraints and covariance restrictions with special emphasis on the applications to errors in variables and variance components models. The Bayesian view on identification is also discussed in the chapter.
    In: Handbook of econometrics, Amsterdam : North-Holland Pub. Co, 1983, (1983), Seite 223-283, 0444861858
    In: 9780444861856
    In: year:1983
    In: pages:223-283
    Language: English
    DOI: 10.1016/S1573-4412(83)01008-9
    URL: Volltext  (Deutschlandweit zugänglich)
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  • 9
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    Chapter 5 Model choice and specification analysis (1983)
    Details
    UID:
    gbv_1831646358
    ISBN: 0444861858
    Content: This chapter discusses statistical theories of model selection and traditional problems. One important source of model-selection problems is the existence of a priori opinion that constraints are likely. Statistical testing is then designed either to determine if a set of constraints is true or to determine if a set of constraints is approximately true. The solution to these two problems that might be supposed to be essentially the same, in fact diverge in two important respects: (1) the first problem leads clearly to a significance levelthat is a decreasing function of sample size, whereas (2) the second problem selects a relatively constant significance level. The problem has a set of alternative modelsthat is determined entirely from a priori knowledge, whereas the second problem can have a data-dependent set of hypotheses. Quadratic loss functions are discussed in the chapter both with and without fixed costs. Quadratic loss does not imply a model-selection problem. The chapter also discusses problems that are not well known.
    In: Handbook of econometrics, Amsterdam : North-Holland Pub. Co, 1983, (1983), Seite 285-330, 0444861858
    In: 9780444861856
    In: year:1983
    In: pages:285-330
    Language: English
    DOI: 10.1016/S1573-4412(83)01009-0
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  • 10
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    Chapter 6 Non-linear regression models (1983)
    Details
    UID:
    gbv_183164634X
    ISBN: 0444861858
    Content: This chapter discusses a survey of nonlinear regression models, with an emphasis on the theory of estimation and hypothesis testing rather than on computation and applications. The advent of advanced computer technology has made it possible for the econometrician to estimate an increasing number of nonlinear regression models. Nonlinearity arises in diverse ways in econometric applications. Perhaps the simplest and best-known case of nonlinearity in econometrics arises as the observed variables in linear-regression models. Another well-known case is the distributed-lag model in which the coefficients on the lagged exogenous variables are specified to decrease with lags in certain nonlinear fashion, such as geometrically declining coefficients. In both of these cases, nonlinearity appears only in parameters but not in variables. More general nonlinear models are used in the estimation of production functions and demand functions. Even a simple CobbDouglas production function cannot be transformed into linearity if the error term is added rather than multiplied.
    In: Handbook of econometrics, Amsterdam : North-Holland Pub. Co, 1983, (1983), Seite 333-389, 0444861858
    In: 9780444861856
    In: year:1983
    In: pages:333-389
    Language: English
    DOI: 10.1016/S1573-4412(83)01010-7
    URL: Volltext  (Deutschlandweit zugänglich)
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