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 equ...
| Main Author: | |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2004, 2004
|
| Edition: | 1st ed. 2004 |
| Series: | Lecture Notes in Economics and Mathematical Systems
|
| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 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