Forecasting Inflation in Russia Using Neural Networks

Forecasting Russian inflation is an important practical task. This paper applies two benchmark machine learning models to this task. Although machine learning in general has been an active area of research for the past 20 years, those methods began gaining popularity in the literature on inflation forecasting only recently. In this paper, I employ neural networks and support-vector machines to forecast inflation in Russia. I also apply Shapley decomposition to obtain economic interpretation of inflation forecasts. The performance of these two models is then compared with the performance of more conventional approaches that serve as benchmark forecasts. These are an autoregression and a linear regression with regularisation (a.k.a. ridge regression). My empirical findings suggest that both machine learning models forecast inflation no worse than the conventional benchmarks and that the Shapley decomposition is a suitable framework that yields a meaningful interpretation to the neural network forecast. I conclude that machine learning methods offer a promising tool of inflation forecasting.