Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes
David Hendry () and
Guillaume Chevillon ()
No 196, Economics Series Working Papers from University of Oxford, Department of Economics
We evaluate the asymptotic and finite-sample properties of direct multi-step estimation (DMS) for forecasting at several horizons. For forecast accuracy gains from DMS in finite samples, mis-specification and non-stationarity of the DGP are necessary, but when a model is well-specified, iterating the one-step ahead froecasts may not be asymptotically preferable. If a model is mis-specified for a non-stationary DGP, in particular omitting either negative residual serial correlation or regime shifts, DMS can forecast more accurately. Monte Carlo simulations clarify the non-linear dependence of the estimation and forecast biases on the parameters of the DGP, and explain existing results.
Keywords: Adaptive Estimation; Multi-Step Estimation; Dynamic Forecasts; Model Mis-Specification (search for similar items in EconPapers)
JEL-codes: C32 C51 C53 (search for similar items in EconPapers)
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Journal Article: Non-parametric direct multi-step estimation for forecasting economic processes (2005)
Working Paper: Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:oxf:wpaper:196
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