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1
Online Resource
Online Resource
Handbook of econometrics : volume 2 (1984)
Amsterdam [u.a.] : Elsevier
Details
UID:
gbv_789695405
Format: Online-Ressource (xxvi, 775-1461 p)
ISBN: 9780444861863 , 0444861866
Series Statement: Handbooks in economics 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 bibliographies and indexes , Testing. Wald, likelihood ratio and Lagrange multiplier tests in econometrics (R.F. Engle)Multiple hypothesis testing (N.E. Savin) -- Approximating the distributions of economic estimators and test statistics (T. Rothenberg) -- Monte Carlo experimental in econometrics (D.F. Hendry) -- Time Series Topics. Time series and spectral methods in econometrics (C.W.J. Granger, M.W. Watson) -- Dynamic specification (D.F. Hendry, A.R. Pagan and J. Denis Sargan) -- Inference and causality in economic time series models (J. Geweke) -- Continuous time stochastic models and issues of aggregation over time (A.R. Bergstrom) -- Random and changing coefficient models (G.C. Chow) -- Panel data (G. Chamberlain). Special Topics in Econometrics - 1. Latent variable models in econometrics (D.J. Aigner et al.) -- Econometric analysis of qualitative response models (D. McFadden).
Language: English
Keywords: Electronic books ; Electronic books
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  • 2
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    Chapter 13 Wald, likelihood ratio, and Lagrange multiplier tests in econometrics (1984)
    Details
    UID:
    gbv_1831646226
    ISBN: 9780444861863
    Content: The use of increasingly complex statistical models has led to heavy reliance on maximum likelihood methods for both estimation and testing. In such a setting, only asymptotic properties can be expected for estimators or tests. Often there are asymptotically equivalent procedures that differ substantially in computational difficulty and finite sample performance. In a maximum likelihood framework, the Wald, Likelihood Ratio, and Lagrange Multiplier (LM) tests are a natural trio. They all share the property of being asymptotically locally the most powerful invariant tests, and in fact all are asymptotically equivalent. However, in practice, there are substantial differences in the way the tests look at particular models. Frequently when one is very complex, another will be much simpler. Furthermore, this formulation guides the intuition as to what is testable and how best to formulate a model to test it. In terms of forming diagnostic tests, the LM test is frequently computationally convenient as many of the test statistics are already available from the estimation of the null. The application of these tests principles and particularly the LM principle to a wide range of econometric problems is a natural development of the field, and it is a development that is proceeding at a very rapid pace.
    In: Handbook of econometrics, Amsterdam [u.a.] : Elsevier, 1984, (1984), Seite 775-826, 9780444861863
    In: 0444861866
    In: year:1984
    In: pages:775-826
    Language: English
    DOI: 10.1016/S1573-4412(84)02005-5
    URL: Volltext  (Deutschlandweit zugänglich)
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  • 3
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    Chapter 14 Multiple hypothesis testing (1984)
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    UID:
    gbv_1831646218
    ISBN: 9780444861863
    Content: This chapter presents a survey of multiple hypothesis testing procedures with an emphasis on those procedures that can be applied in the context of the classical linear regression model. Multiple hypothesis testing is the testing of two or more separate hypotheses simultaneously. The t and F tests are the most frequently used tests in econometrics. In regression analysis, there are two different procedures that can be used to test the hypothesis that all the coefficients are zero. One procedure is to test each coefficient separately with a t test, and the other is to test all coefficients jointly using an F test. The investigator usually performs both procedures when analyzing the sample data. It has been proved that the F test is equivalent to carrying out a set of simultaneous t tests. The chapter also discusses an induced test, which is either finite or infinite depending on whether there are a finite or infinite number of separate hypotheses. In the case of finite induced tests, the exact sampling distributions of the test statistics can be complicated, so that, in practice, the critical regions of the tests are based on probability inequalities. On the other hand, infinite induced tests are commonly constructed such that the correct critical value can be readily calculated.
    In: Handbook of econometrics, Amsterdam [u.a.] : Elsevier, 1984, (1984), Seite 827-879, 9780444861863
    In: 0444861866
    In: year:1984
    In: pages:827-879
    Language: English
    DOI: 10.1016/S1573-4412(84)02006-7
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  • 4
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    Chapter 15 Approximating the distributions of econometric estimators and test statistics (1984)
    Details
    UID:
    gbv_183164620X
    ISBN: 9780444861863
    Content: Approximate distribution theory derives results from assumptions on the stochastic process generating the data. The quality of the approximation is not better than the quality of the specifications on which it is based. The models used by econometricians are, at best, crude and rather arbitrary. As most of the approximation methods employ information on the first four moments of the data whereas the usual asymptotic theory typically requires information only on the first two moments, some loss in robustness must be expected. However, if a rough idea about the degree of skewness and kurtosis is available, that information can be often exploited to obtain considerably improved approximations to sample statistics. The chapter discusses that sophisticated approximation theory is most appropriate in situations where the econometrician is able to make correct and detailed assumptions about the process being studied. In current practice, applied econometricians occasionally draw incorrect conclusions on the basis of alleged asymptotic properties of their procedures. In recent years, an extraordinary fondness for asymptotic theory has developed among econometricians. Considerable effort is devoted to showing that some new estimator or test is asymptotically normal and efficient. The assertion that a given estimator is approximately normal suggests that the speaker believes that it would be sensible to treat the estimator as though it were really normal. Accurate and convenient approximations for the distributions of econometric estimators and test statistics are of great value.
    In: Handbook of econometrics, Amsterdam [u.a.] : Elsevier, 1984, (1984), Seite 881-935, 9780444861863
    In: 0444861866
    In: year:1984
    In: pages:881-935
    Language: English
    DOI: 10.1016/S1573-4412(84)02007-9
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  • 5
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    Chapter 16 Monte carlo experimentation in econometrics (1984)
    Details
    UID:
    gbv_1831646196
    ISBN: 9780444861863
    Content: At the outset, it is useful to distinguish Monte Carlo methods from distribution sampling even though their application in econometrics may seem rather similar. The former is a general approach whereby mathematical problems of an analytical nature, which prove technically intractable, can be solved by substituting an equivalent stochastic problem and solving the latter. In contrast, distribution sampling is used to evaluate features of a statistical distribution by representing it numerically and drawing observations from that numerical distribution. The chapter investigates the distribution of the mean of random samples of T observations from a distribution that was uniform between zero and unity, one could simply draw a large number of samples of that size from a set of one million evenly spaced numbers in the interval and plot the resulting distribution. Such a procedure is invariably part of a Monte Carlo experiment.
    In: Handbook of econometrics, Amsterdam [u.a.] : Elsevier, 1984, (1984), Seite 937-976, 9780444861863
    In: 0444861866
    In: year:1984
    In: pages:937-976
    Language: English
    DOI: 10.1016/S1573-4412(84)02008-0
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  • 6
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    Chapter 17 Time series and spectral methods in econometrics (1984)
    Details
    UID:
    gbv_1831646188
    ISBN: 9780444861863
    Content: This chapter discusses two alternative approaches to the analysis of economic data, which is time series and the classical econometric approaches. The time series approach is based on experience from many fields, but that of the econometrician has been viewed as applicable only to economic data that have displayed a great deal of simultaneous or contemporaneous interrelationships. Some influences from the time series domain penetrated that of the classical econometrician, such as how to deal with trends and seasonal components, DurbinWatson statistics, and first-order serial correlation, but there was little influence in the other direction. In the past 10 years, this state of affairs has changed dramatically, with time series ideas becoming more mainstream and the procedures developed by econometricians being considered more carefully by the time series analysts. The building of large-scale models, worries about efficient estimation, the growing popularity of rational expectations theory and the consequent interest in optimum forecasts, and the discussion of causality testing have greatly helped in bringing the two approaches together, with benefits to both sides. The chapter briefly discusses the question of differencing of data, as an illustration of the alternative approaches taken in the past. It also discusses some applications of time series methods to economic data.
    In: Handbook of econometrics, Amsterdam [u.a.] : Elsevier, 1984, (1984), Seite 979-1022, 9780444861863
    In: 0444861866
    In: year:1984
    In: pages:979-1022
    Language: English
    DOI: 10.1016/S1573-4412(84)02009-2
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  • 7
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    Chapter 18 Dynamic specification (1984)
    Details
    UID:
    gbv_183164617X
    ISBN: 9780444861863
    Content: Dynamic specification denotes the problem of appropriately matching the lag reactions of a postulated theoretical model to the autocorrelation structure of the associated observed time-series data. As such, the issue is inseparable from that of stochastic specification if the finally chosen model is to have a purely random error process as its basic innovation. The subject-matter has advanced rapidly and offers an opportunity for critically examining the main themes and integrating previously disparate developments. A statistical-theory based model considers the joint density of the observables and seeks to characterize the processes whereby the data were generated. Thus, the focus is on means of simplifying the analysis to allow valid inference from submodels. This chapter also discusses that given the paucity of dynamic theory and the small sample sizes currently available for most time series of interest, as against the manifest complexity of the data processes, all sources of information have to be utilized. Attempt to resolve the issue of dynamic specification first involves developing the relevant concepts, models, and methods that is the deductive aspect of statistical analysis, prior to formulating inference techniques. An alternative interpretation is that by emphasizing the econometric aspect of time-series modeling, the analysis applies howsoever the model is obtained and seeks to be relatively neutral as to the economic theory content.
    In: Handbook of econometrics, Amsterdam [u.a.] : Elsevier, 1984, (1984), Seite 1023-1100, 9780444861863
    In: 0444861866
    In: year:1984
    In: pages:1023-1100
    Language: English
    DOI: 10.1016/S1573-4412(84)02010-9
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  • 8
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    Chapter 19 Inference and causality in economic time series models (1984)
    Details
    UID:
    gbv_1831646161
    ISBN: 9780444861863
    Content: This chapter is concerned with a particular formalization that has proved useful in empirical work, hence the juxtaposition of causality and inference. It also bears close relation to notions of strictly exogenous and predetermined variables, which have considerable operational significance in statistical inference, and to the concepts of causal orderings and reliability which are important in model construction in econometrics and engineering. Causality is defined in terms of predictability; it cannot be an acceptable definition of causation for most philosophers of science. The chapter focuses on the operational usefulness of the definition in the construction, estimation, and application of econometric models. It considers the logical relationships among WienerGranger causality, Simon's definition of causal ordering, the engineer's criterion of reliability, and the concept of structure. It also discusses that unidirectional causality from X to Y is not equivalent to the assertion that X is predetermined in a particular behavioral relationship whose parameters are to be estimated. It further focuses on parameterization problems, processes that are nonautoregressive or have deterministic components or are nonstationary, and inference about many variables.
    In: Handbook of econometrics, Amsterdam [u.a.] : Elsevier, 1984, (1984), Seite 1101-1144, 9780444861863
    In: 0444861866
    In: year:1984
    In: pages:1101-1144
    Language: English
    DOI: 10.1016/S1573-4412(84)02011-0
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  • 9
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    Chapter 20 Continuous time stochastic models and issues of aggregation over time (1984)
    Details
    UID:
    gbv_1831646153
    ISBN: 9780444861863
    Content: This chapter describes statistical methods that are applicable to a class of continuous time stochastic models and discusses the theoretical foundations of these methods. An important feature of the class of models is that such models allow for the incorporation of a priori restrictions, such as those derived from economic theory, through the structural parameters of the continuous time system. They are used to represent a dynamic system of causal relations in which each variable adjusts continuously in response to the stimulus provided by other variables, and the adjustment relations involve the basic structural parameters in some optimization theory. These structural parameters are estimated from a sample comprising a sequence of discrete observations of the variables that are a mixture of stock variables and flow variables. In this way, it is possible to take advantage of the a priori restrictions derived from economic theory without making the unrealistic assumption that the economy moves in discrete jumps among successive positions of temporary equilibrium. On the theoretical side an important and relatively unexplored field of research is in the development of methods of estimation for systems of nonlinear stochastic differential equations. In some cases, it is possible to derive the exact likelihood function in terms of the discrete observations generated by a system of nonlinear stochastic differential equations. But, more generally, approximate methods are relied on, possibly involving the use of numerical solutions to the nonlinear differential equations system.
    In: Handbook of econometrics, Amsterdam [u.a.] : Elsevier, 1984, (1984), Seite 1145-1212, 9780444861863
    In: 0444861866
    In: year:1984
    In: pages:1145-1212
    Language: English
    DOI: 10.1016/S1573-4412(84)02012-2
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  • 10
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    Chapter 21 Random and changing coefficient models (1984)
    Details
    UID:
    gbv_1831646145
    ISBN: 9780444861863
    Content: This chapter discusses that the standard linear regression model has been an attractive model to use in econometrics. If econometricians can uncover stable economic relations that satisfy at least approximately the assumptions of this model, they deserve the credit and the convenience of using it. Sometimes, however, econometricians are not lucky or ingenious enough to specify a stable regression relationship, and the relationship being studied gradually changes. Under such circumstances, an option is to specify a linear regression model with stochastically evolving coefficients. The chapter also reviews that for the purpose of parameter estimation, this model takes into account the possibility that the coefficients may be time dependent and provides estimates of these coefficients at different points of time. For the purpose of forecasting, this model has an advantage over the standard regression model in utilizing the estimates of the most up-to-date coefficients. From the viewpoint of hypothesis testing, this model serves as a viable alternative to the standard regression model for the purpose of checking the constancy of the coefficients of the latter model.
    In: Handbook of econometrics, Amsterdam [u.a.] : Elsevier, 1984, (1984), Seite 1213-1245, 9780444861863
    In: 0444861866
    In: year:1984
    In: pages:1213-1245
    Language: English
    DOI: 10.1016/S1573-4412(84)02013-4
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