Can Recurrent Neural Networks Predict Inflation in Euro Zone as Good as Professional Forecasters?

Abstract

This paper investigates whether a specific type of a recurrent neural network, in particular Jordan neural network (JNN), captures the expected inflation better than commonly used feedforward neural networks and traditional parametric time-series models. It also considers competing survey-based and model-based expected inflation towards ex-post actual inflation to find whose predictions are more accurate; predictions from survey respondents or forecasting modelers. Further, it proposes neural network modelling strategy when dealing with nonstationary time-series which exhibit long-memory property and nonlinear dependence with respect to lagged inputs and exogenous inputs as well. Following this strategy, overfitting problem was reduced until no improvement in forecasting accuracy of expected inflation is achieved. The main finding is that JNN predicts inflation in euro zone quite accurately within forecasting horizon of 2 years. Regarding rational expectation principle we have found a set of demand-pull and cost-push inflation characteristics as exogenous inputs which helps in reducing overfitting problem of recurrent neural network even more. The sample includes euro zone aggregated monthly observations from January 2000 to December 2019. The results also confirm that inflation expectations obtained from JNN are consistent with Survey of professional forecasters (SPF), and thus, monetary policy makers can use JNN as a complementary tool in shortcomings of other inflation expectations measures.

Forecasting of Power Output of a PVPS Based on Meteorological Data Using RNN Approaches

“…According to Haykin [14], single-layer feed-forward networks, multilayer networks, and recurrent networks (RNNs) are the main categories of ANNs. A comparative study was conducted by Šestanović et al [22] to assess the capability of the Jordan neural network (JNN), a specific type of RNN and feed-forward network, to predict inflation in the Euro zone. The researchers reported that JNN showed a better ability to predict inflation, and the prospects given by JNN were consistent with the survey of professional forecasters.…”

Double integral‐enhanced Zeroing neural network with linear noise rejection for time‐varying matrix inverse

“…So they have been extensively studied and proposed for mathematical applications [17][18][19], such as static matrix inversion problems [20]. A non-linear predictive control scheme based on a self-organising recurrent neural network (RNN) was proposed, and the theoretical feasibility was proved by Lyapunov function [21]. Then, the gradient neural network (GNN) was proposed.…”