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Titlebook: Bayesian Full Information Analysis of Simultaneous Equation Models Using Integration by Monte Carlo; Luc Bauwens Book 1984 Springer-Verlag

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期刊全稱(chēng)Bayesian Full Information Analysis of Simultaneous Equation Models Using Integration by Monte Carlo
影響因子2023Luc Bauwens
視頻videohttp://file.papertrans.cn/182/181843/181843.mp4
學(xué)科分類(lèi)Lecture Notes in Economics and Mathematical Systems
圖書(shū)封面Titlebook: Bayesian Full Information Analysis of Simultaneous Equation Models Using Integration by Monte Carlo;  Luc Bauwens Book 1984 Springer-Verlag
影響因子In their review of the "Bayesian analysis of simultaneous equation systems", Dr~ze and Richard (1983) - hereafter DR - express the following viewpoint about the present state of development of the Bayesian full information analysis of such sys- tems i) the method allows "a flexible specification of the prior density, including well defined noninformative prior measures"; ii) it yields "exact finite sample posterior and predictive densities". However, they call for further developments so that these densities can be eval- uated through ‘numerical methods, using an integrated software packa~e. To that end, they recommend the use of a Monte Carlo technique, since van Dijk and Kloek (1980) have demonstrated that "the integrations can be done and how they are done". In this monograph, we explain how we contribute to achieve the developments suggested by Dr~ze and Richard. A basic idea is to use known properties of the porterior density of the param- eters of the structural form to design the importance functions, i. e. approximations of the posterior density, that are needed for organizing the integrations.
Pindex Book 1984
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Spoken Language Component of the MASK Kiosk,ions and affect the exponential argument of (1.7) whose dependence on 6 is made clear. The first one is named: AI(Σ) (for analytical integration of Σ), and the second one: AI(γ) (for analytical integration of γ, the subvector of δ regrouping the coefficients of the predetermined variables in the equ
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Bayesian Inference: The Extended Natural-Conjugate Approach,ions and affect the exponential argument of (1.7) whose dependence on 6 is made clear. The first one is named: AI(Σ) (for analytical integration of Σ), and the second one: AI(γ) (for analytical integration of γ, the subvector of δ regrouping the coefficients of the predetermined variables in the equations (1.3)).
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Understanding Industry in Transition,As indicated at the end of section II.5, the choice of the functional form and of the parameters of an importance function f(θ) for a density p(θ) should be guided by the requirement that f be a good approximation of p. The following criteria are in our opinion necessary but by no means sufficient conditions to attain this goal.
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Karlheinz Sonntag,Michael FreibothWe have used the importance functions proposed in section III.2 with 5 models covering a variety of situations, as summarized in the following table:
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Understanding Changing Competence Demands,We have considered the case of the (truncated) extended natural-conjugate prior. Its drawbacks are well known — see DR and Richard (1973). In particular, one must elicit an informative prior on Σ, a difficult task; in addition, the prior information on Σ has a strong influence on the results on δ, and especially on β.
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