Ebook: Model Reduction Methods for Vector Autoregressive Processes

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.

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Vector Autoregressive (VAR) models have become one of the dominant tools for the empirical analysis of macroeconomic time series. Sometimes the flexibility of VAR models leads to overparameterized models, making accurate estimates of impulse responses and forecasts difficult. This book introduces a variety of data-based model reduction methods and provides a detailed investigation of different reduction strategies in the context of popular VAR modelling classes, including stationary, cointegrated and structural VAR models. VAR practitioners benefit from guidelines being developed for using model reduction in applied work. The use of different reduction techniques is illustrated by means of empirical models for US monetary policy shocks and a structural vector error correction model of the German labor market.

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Vector Autoregressive (VAR) models have become one of the dominant tools for the empirical analysis of macroeconomic time series. Sometimes the flexibility of VAR models leads to overparameterized models, making accurate estimates of impulse responses and forecasts difficult. This book introduces a variety of data-based model reduction methods and provides a detailed investigation of different reduction strategies in the context of popular VAR modelling classes, including stationary, cointegrated and structural VAR models. VAR practitioners benefit from guidelines being developed for using model reduction in applied work. The use of different reduction techniques is illustrated by means of empirical models for US monetary policy shocks and a structural vector error correction model of the German labor market.

Content:
Front Matter....Pages I-X
Introduction....Pages 1-4
Model Reduction in VAR Models....Pages 5-57
Model Reduction in Cointegrated VAR Models....Pages 59-104
Model Reduction and Structural Analysis....Pages 105-146
Empirical Applications....Pages 147-196
Concluding Remarks and Outlook....Pages 197-201
Back Matter....Pages 203-220

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Vector Autoregressive (VAR) models have become one of the dominant tools for the empirical analysis of macroeconomic time series. Sometimes the flexibility of VAR models leads to overparameterized models, making accurate estimates of impulse responses and forecasts difficult. This book introduces a variety of data-based model reduction methods and provides a detailed investigation of different reduction strategies in the context of popular VAR modelling classes, including stationary, cointegrated and structural VAR models. VAR practitioners benefit from guidelines being developed for using model reduction in applied work. The use of different reduction techniques is illustrated by means of empirical models for US monetary policy shocks and a structural vector error correction model of the German labor market.

Content:
Front Matter....Pages I-X
Introduction....Pages 1-4
Model Reduction in VAR Models....Pages 5-57
Model Reduction in Cointegrated VAR Models....Pages 59-104
Model Reduction and Structural Analysis....Pages 105-146
Empirical Applications....Pages 147-196
Concluding Remarks and Outlook....Pages 197-201
Back Matter....Pages 203-220
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