Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques

We show, how the forecasting performance of models varies, when certain inaccuracies in the pseudo real-time experiment take place. We consider the case of Russian CPI forecasting and estimate several models on not seasonally adjusted data vintages. Particular attention is paid to the availability of the variables at the moment of forecast: we take into account the release timing of the series and the corresponding release delays, in order to reconstruct the forecasting in real-time. In the series of experiments, we quantify how each of these issues affect the out-of-sample error. We illustrate, that the neglect of the release timing generally lowers the errors. The same is true for the use of seasonally adjusted data. The impact of the data vintages depends on the model and forecasting period. The overall effect of all three inaccuracies varies from 8% to 17% depending on the forecasting horizon. This means, that the actual forecasting error can be significantly underestimated, when inaccurate pseudo real-time experiment is run. We underline the need to take these aspects into account, when the real-time forecasting is considered.