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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

Abstract: 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
Date: 2004-05-24
New Economics Papers: this item is included in nep-ecm and nep-ets
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Related works:
Journal Article: Non-parametric direct multi-step estimation for forecasting economic processes (2005) Downloads
Working Paper: Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes (2004) Downloads
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