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Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes Author info | Abstract | Publisher info | Download info | Related research | Statistics Guillaume Chevillon
David Hendry
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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.
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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number
196.
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Date of creation: 2004Date of revision:
Handle: RePEc:oxf:wpaper:196Contact details of provider: Postal: Manor Rd. Building, Oxford, OX1 3UQ Email: Web page: http://www.economics.ox.ac.uk/ More information through EDIRC
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Keywords: Adaptive Estimation Multi-Step Estimation Dynamic Forecasts Model Mis-Specification Other versions of this item:
Find related papers by JEL classification: C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
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