Forecasting with a noncausal VAR model

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Title: Forecasting with a noncausal VAR model
Author: Nyberg, Henri ; Saikkonen, Pentti
Organization: Bank of Finland
Series: Bank of Finland Research Discussion Papers
ISSN: 1456-6184
Series year: 2012
Series number: 33/2012
Year of publication: 2012
Publication date: 9.11.2012
Published in: Published in Computational Statistics & Data Analysis, Volume 76, August 2014, Pages 536-555
DOI: %2010.1016/j.csda.2013.10.014
Pages: 43 s.
Keywords: mallit; ennusteet; USA; inflaatio; VAR; rajakustannukset
JEL: C32; C53
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 bestfitting conventional causal VAR model in forecasting inflation. Keywords: Noncausal vector autoregression, forecasting, simulation, importance sampling, inflation. JEL codes: C32, C53, E3l.AC
Note: Ilmestynyt myös Computational Statistics & Data Analysis 2013.

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