Research Discussion Papers, Bank of Finland
No 33/2012:
Forecasting with a noncausal VAR model
Henri Nyberg ()
and Pentti Saikkonen ()
Abstract: We propose simulation-based forecasting methods for the
noncausal vector autoregressive model proposed by Lanne and Saikkonen
(2012). Simulation or numerical methods are required because the prediction
problem is generally nonlinear and, therefore, its analytical solution is
not available. It turns out that different special cases of the model call
for different simulation procedures. Simulation experiments demonstrate
that gains in forecasting accuracy are achieved by using the correct
noncausal VAR model instead of its conventional causal counterpart. In an
empirical application, a noncausal VAR model comprised of U.S. inflation
and marginal cost turns out superior to the best-fitting conventional
causal VAR model in forecasting inflation.
Keywords: noncausal vector autoregression; forecasting; simulation; importance sampling; inflation; (follow links to similar papers)
JEL-Codes: C32; C53; E31; (follow links to similar papers)
38 pages, November 9, 2012
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