"Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes"
	
David F. Hendry
Nuffield College, Oxford OX1 1NF, UK.

and

Guillaume Chevillon
OFCE, Paris and Economics Department, 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. 
JEL Classification: C32, C51, C53.