Penalised regressions vs. autoregressive moving average models for forecasting inflation

Resumen

This paper relates seasonal autoregressive moving average (SARMA) models with linear regression. Based on this relation, the paper shows that penalised linear models may surpass the out-of-sample forecasting accuracy of the best SARMA models when forecasting inflation based on past values, due to penalisation and cross-validation. The paper constructs a minimal working example using ridge regression to compare both of the competing approaches when forecasting the monthly inflation in 35 selected countries of the Organisation for Economic Co-operation and Development and in three groups of countries. The results empirically verify the hypothesis that penalised linear regression, and ridge regression in particular, can outperform the best standard SARMA models computed through a grid search when forecasting inflation. Thus, a new and effective technique for forecasting inflation based on past values is provided for use by financial analysts and investors. The results indicate that more attention should be given to machine learning techniques for time series forecasting of inflation, even as basic as penalised linear regressions, due to their superior empirical performance.