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＜E-Book＞
Model Reduction Methods for Vector Autoregressive Processes / by Ralf Brüggemann
(Lecture Notes in Economics and Mathematical Systems. ISSN:21969957 ; 536)

Edition	1st ed. 2004.
Publisher	(Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer)
Year	2004
Size	X, 218 p. 4 illus : online resource
Authors	*Brüggemann, Ralf author SpringerLink (Online service)
Subjects	LCSH:Probabilities LCSH:Econometrics LCSH:Statistics  FREE:Probability Theory FREE:Econometrics FREE:Statistics in Business, Management, Economics, Finance, Insurance
Notes	1 Introduction -- 1.1 Objective of the Study -- 1.2 Outline of the Study -- 2 Model Reduction in VAR Models -- 2.1 The VAR Modeling Framework -- 2.2 Specification of Subset VAR Models -- 2.3 Monte Carlo Comparison -- 2.4 Summary -- 3 Model Reduction in Cointegrated VAR Models -- 3.1 The Cointegrated VAR Modeling Framework -- 3.2 Modeling Cointegrated VAR Processes -- 3.3 Data Based Model Reduction -- 3.4 Evaluation of Model Reduction Method -- 3.5 Summary -- 3.A DOP Parameters and Properties -- 4 Model Reduction and Structural Analysis -- 4.1 The Structural VAR Modeling Framework -- 4.2 Estimation of Structural VAR Models -- 4.3 Monte Carlo Experiments -- 4.4 Summary -- 4.A Time Series Plots -- 4.B DGP Parameters -- 5 Empirical Applications -- 5.1 The Effects of Monetary Policy Shocks -- 5.2 Sources of German Unemployment -- 5.3 Summary -- 5.A Data Sources -- 5.B Two Cointegrating Vectors -- 5.C VECM Estimates -- 6 Concluding Remarks and Outlook -- 6.1 Summary -- 6.2 Extensions -- Index of Notation -- List of Figures -- List of Tables 1. 1 Objective of the Study Vector autoregressive (VAR) models have become one of the dominant research tools in the analysis of macroeconomic time series during the last two decades. The great success of this modeling class started with Sims' (1980) critique of the traditional simultaneous equation models (SEM). Sims criticized the use of 'too many incredible restrictions' based on 'supposed a priori knowledge' in large scale macroeconometric models which were popular at that time. Therefore, he advo­ cated largely unrestricted reduced form multivariate time series models, unrestricted VAR models in particular. Ever since his influential paper these models have been employed extensively to characterize the underlying dynamics in systems of time series. In particular, tools to summarize the dynamic interaction between the system variables, such as impulse response analysis or forecast error variance decompo­ sitions, have been developed over the years. The econometrics of VAR models and related quantities is now well established and has found its way into various textbooks including inter alia Llitkepohl (1991), Hamilton (1994), Enders (1995), Hendry (1995) and Greene (2002). The unrestricted VAR model provides a general and very flexible framework that proved to be useful to summarize the data characteristics of economic time series. Unfortunately, the flexibility of these models causes severe problems: In an unrestricted VAR model, each variable is expressed as a linear function of lagged values of itself and all other variables in the system HTTP:URL=https://doi.org/10.1007/978-3-642-17029-4

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