Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes
Guillaume Chevillon () and
David Hendry ()
No 2004-W12, Economics Papers from Economics Group, Nuffield College, University of Oxford
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 forecasts may not be asymptotically preferable. If a model is mis-specified for a non-stationary DGP, 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)
Pages: 27 pages
New Economics Papers: this item is included in nep-ecm and nep-ets
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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:nuf:econwp:0412
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