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Structural vector autoregressive ana...

Kilian, Lutz.

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Structural vector autoregressive analysis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Structural vector autoregressive analysis/ Lutz Kilian, Helmut Lutkepohl.
作者:	Kilian, Lutz.
其他作者:	Lutkepohl, Helmut.
出版者:	Cambridge :Cambridge University Press, : 2017.,
面頁冊數:	xx, 734 p. :ill., digital ;24 cm.
附註:	Title from publisher's bibliographic system (viewed on 17 Nov 2017).
標題:	Econometric models. -
電子資源:	https://doi.org/10.1017/9781108164818
ISBN:	9781108164818

Structural vector autoregressive analysis
Kilian, Lutz.

Structural vector autoregressive analysis[electronic resource] /Lutz Kilian, Helmut Lutkepohl. - Cambridge :Cambridge University Press,2017. - xx, 734 p. :ill., digital ;24 cm. - Themes in modern econometrics. - Themes in modern econometrics..

Title from publisher's bibliographic system (viewed on 17 Nov 2017).

Structural vector autoregressive (VAR) models are important tools for empirical work in macroeconomics, finance, and related fields. This book not only reviews the many alternative structural VAR approaches discussed in the literature, but also highlights their pros and cons in practice. It provides guidance to empirical researchers as to the most appropriate modeling choices, methods of estimating, and evaluating structural VAR models. The book traces the evolution of the structural VAR methodology and contrasts it with other common methodologies, including dynamic stochastic general equilibrium (DSGE) models. It is intended as a bridge between the often quite technical econometric literature on structural VAR modeling and the needs of empirical researchers. The focus is not on providing the most rigorous theoretical arguments, but on enhancing the reader's understanding of the methods in question and their assumptions. Empirical examples are provided for illustration.

ISBN: 9781108164818Subjects--Topical Terms:

542933
Econometric models.

LC Class. No.: HB141 / .K55 2017

Dewey Class. No.: 330.01519536

Structural vector autoregressive analysis
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