Office of Academic Resources
Chulalongkorn University
Chulalongkorn University

Home / Help

AuthorBrรผggemann, Ralf. author
TitleModel Reduction Methods for Vector Autoregressive Processes [electronic resource] / by Ralf Brรผggemann
ImprintBerlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004
Connect to
Descript X, 218 p. 4 illus. online resource


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 advoยญ 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 decompoยญ 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


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

Mathematics Probabilities Statistics Econometrics Mathematics Probability Theory and Stochastic Processes Econometrics Statistics for Business/Economics/Mathematical Finance/Insurance


Office of Academic Resources, Chulalongkorn University, Phayathai Rd. Pathumwan Bangkok 10330 Thailand

Contact Us

Tel. 0-2218-2929,
0-2218-2927 (Library Service)
0-2218-2903 (Administrative Division)
Fax. 0-2215-3617, 0-2218-2907

Social Network


facebook   instragram