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.
