Inflation Forecasting in Time Series Models Using High Frequency Data

The article examines ways to improve inflation forecasting by using high frequency consumer price data in time series models. The purpose of increasing the number of observations available at a higher frequency is to increase the accuracy of inflation forecasts. The theoretical part of the paper considers the advantages and disadvantages of using high frequency price data in ADL, VAR and MIDAS inflation models with both single and mixed data frequency. The empirical section traces out the effects of including an online price index available at a daily or weekly frequency during the period from 2020 to 2023 in the forecast model for the consumer price index. The article compares the forecasts of consumer prices by applying the VAR, MFVAR and MIDAS models which include data from a high frequency regressor with the forecasts obtained through auto-ARIMA models. The conclusion about the difference in the quality of the short-term forecast of consumer price dynamics in these models is based on the difference of the forecast error indicator of the models. The results provide some evidence that short-term out-of-sample CPI dynamic forecasting becomes more accurate when online price data is included (namely in the class of multidimensional time series models when data is included in the model at a higher frequency). However, the advantage derived from including high frequency online price data in models decreases as the forecast horizon is extended. The results show the importance of including online price data in inflation models in a disaggregated form while forecasting price trends of the nearest future.