Forecasting with a noncausal VAR model
AbstractWe 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.
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Bibliographic InfoPaper provided by Bank of Finland in its series Research Discussion Papers with number 33/2012.
Length: 38 pages
Date of creation: 09 Nov 2012
Date of revision:
noncausal vector autoregression; forecasting; simulation; importance sampling; inflation;
Other versions of this item:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-12-06 (All new papers)
- NEP-ECM-2012-12-06 (Econometrics)
- NEP-ETS-2012-12-06 (Econometric Time Series)
- NEP-FOR-2012-12-06 (Forecasting)
- NEP-ORE-2012-12-06 (Operations Research)
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