J.G. de Gooijer
D. Zerom Godefay
- Multi-stage kernel-based conditional quantile prediction in time series
- Communications in Statistics: Theory and Methods
- Pages (from-to)
- Document type
- Faculty of Economics and Business (FEB)
- Amsterdam School of Economics Research Institute (ASE-RI)
We present a multi-stage conditional quantile predictor for time series of Markovian structure. It is proved that at any quantile level p \in (0,1), the asymptotic mean squared error (MSE) of the new predictor is smaller than the single-stage conditional quantile predictor. A simulation study confirm this result in a small sample situation. Because the improvement by the proposed predictor increases for quantiles at the tails of the conditional distribution function, the multi-stage predictor can be used to compute better predictive intervals with smaller variability. Applying this predictor to thechanges in the U.S. short-term interest rate, rather smooth out-of-sample predictive intervals are obtained.
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