Jordan neural network for inflation forecasting

Abstract

In times of pronounced nonlinearity of macroeconomic variables and in situations when variables are not normally distributed, i.e. when the assumption of i.i.d. is not fulfilled, neural networks (NNs) should be used for forecasting. In this paper, Jordan neural network (JNN), a special type of NNs is examined, because of its advantages in time series forecasting suitable for in ation forecasting. The variables used as inputs include labour market variable,financial variable, external factor and lagged in ation, i.e. the most commonly used variables in previous researches. The research is conducted at the aggregate level of euro area countries in the period from January 1999 to January 2017. Based on 250 estimated JNNs, which differ in selected variables, sample breaking point and varying parameters (number of hidden neurons, weight value of the context unit), the model adequacy indicators for each JNN are calculated for two periods: in-the-sample and out-of-sample. Finally, the optimal JNN for in ation forecasting is obtained as the best compromise solution between low mean squared error inthe- sample and out-of-sample and low number of parameters to estimate. This paper contributes to existing literature in using JNN for in ation forecasting since it is rarely used for macroeconomic time series prediction in general. Moreover, this paper defines which set of variables contributes to the best in ation forecast. Additionally, JNN is examined thoroughly byfixing certain parameters of the model and alternating other parameters to contribute to the JNN literature, i.e.finding the optimal JNN.