Predicting inflation dynamics with singular spectrum analysis

We use univariate and multivariate singular spectrum analyses to predict the rate of inflation as well as changes in the direction of inflation time series for the USA. We use consumer price indices and realtime chain-weighted gross domestic product price index series in these prediction exercises. Moreover, we compare our out-of-sample, h-step-ahead moving prediction results with other prediction results based on methods such as the activity-based non-accelerating inflation rate of unemployment Phillips curve, auto-regressive AR(ρ) model, the dynamic factors model and random-walk models with the last as a naive forecasting method. We use short-run (quarterly) and long-run (1-6 years) time windows for predictions and find that multivariate singular spectrum analysis outperforms all other competing prediction methods. Also, we confirm the results of earlier studies that prediction of the rate of inflation in the USA during the period of the 'Great Moderation' is less challenging compared with the more volatile inflationary period of 1970-1985.