Intervalos de predicción para pronósticos no paramétricos de la inflación colombiana (2010)
This paper contains one of the Kernel smoothing applications. It shows Colombian total inﬂation’s smoothing results; multi-steps-ahead forecast, based on Mean and Median conditional predictors, the forecast generated are compared with an ARIMA and STAR models and with neuronal networks; ﬁnally the intervals of prediction of ARIMA model using nonparametric methods are compared. It found that the intervals using the second method are better in the period of evaluated time.