Forecasting with a noncausal VAR model

Henri Nyberg, Pentti Saikkonen

Research output: Working paperDiscussion paperScientific


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.
Original languageEnglish
Place of PublicationHelsinki
PublisherBank of Finland
Number of pages37
ISBN (Electronic)978-952-462-828-0
Publication statusPublished - Nov 2012
MoE publication typeD4 Published development or research report or study

Fields of Science

  • 112 Statistics and probability
  • 511 Economics

Cite this

Nyberg, H., & Saikkonen, P. (2012). Forecasting with a noncausal VAR model. (Bank of Finland Research Discussion Papers; No. 33/2012). Helsinki: Bank of Finland.