Fitting of an appropriate model to an observed time series data for the purpose of predicting the future values efficiently is always a challenging task. The practitioners of statistics in their first attempt always try to fit parametric regression model to the data. For all parametric models to be fitted, it is assumed that the model errors follow independent normal distributions. If that assumption on error distribution is not satisfied, then we should search for an alternative procedure. Here, we propose the nonparametric regression procedure as the alternative procedure and study its performance. In the present investigation the secondary data on production of rice crop forthe Kharif season and production of wheat for Rabi season for India as a whole for 51 years (1962-63 to 2012-13) have been used. It has been observed that the variable, production of rice,does not satisfy the assumption of normal distribution of errors but the variable, production of wheat satisfies the assumption of normality of error distribution. Here we have applied Parametric and nonparametric regression approaches to both the data sets. It has been found that there is a great reduction in the value of Mean Absolute Percentage Error (MAPE) of prediction for the dependent variable production of rice when nonparametric regression is used. It is concluded that the nonparametric regression works well for the data set for which the normality assumption of the error distribution does not hold and gives better prediction than the usual parametric regression.

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