INFLATION TARGETING AND FORECASTING: EVIDENCE FROM EURO AREA

Abstract

There are different structures that enable a central bank to strengthen the performance of a monetary policy. Price stability is essential to central banks in order to promote a successful monetary policy and they have unequivocal authority in determining the way on how to achieve it. Controlling price movements between a particular bandwidth through inflation targeting, is one way that can contribute in addressing and achieving the specific goals of a monetary policy. There are different structures that enable a central bank to strengthen the performance of a monetary policy. Price stability is essential to central banks in order to promote a successful monetary policy and they have unequivocal authority in determining the way on how to achieve it. Controlling price movements between a particular bandwidth through inflation targeting, is one way that can contribute in addressing and achieving the specifics goals of a monetary policy. This paper will start by comparing the actual and forecasted inflation, based on quarterly data from OECD for the period 2010 – 2026. Price data is derived from Harmonized core inflation for the 17 member of the Euro area (EA17). For the most part of the period, inflation stud firmly below the 2% level i.e. in accordance with the objectives of the ECB, before spiking to historic levels during the COVID 19 pandemic, peaking around 5.5 percent, and stabilizing afterword’s at the 2 percent level, until the end of 2026. Evaluating the forecast error, three metrics will be employed, based on the work of Czekaj et al. (2024). These methods are: mean absolute forecast error (MAE), root mean squared forecast errors (RMSE) and mean absolute percentage error (MAPE). In the case of MAE for yearly data inflation forecast was off by only 0.83 percent, meaning that inflation targeting by the ECB was in line with the expectations, because predictive inflation error was inferior of 2 percent, lower than the target set by the central bank. The same thing cannot be said for the quarterly data, where mean absolute error is 2.89 percent, considerably greater than the ECB objective. MAPE, method is used to calculate the average forecast error, with yearly and quarterly data, the results show that on average the prediction is off by 0.54 percent and 0.58 percent respectively. Considering the RMSE method, it produces on average a prediction for yearly and quarterly data, off by 1.27 and 1.34 respectively. In the case when forecast accuracy measures are considered separately, q3 and q4 quarters are the quarters where prediction accuracy is the lowest for the three methods. Examining the accuracy of forecasted measure for inflation targeting economies, the results are in line with Czekaj et al. (2024) and Schnabel (2024). Inflation targeting has contributed in stabilizing price increases with one exception, during a sudden economic shock, i.e. COVID 19 crises. Moreover, through forecasted accuracy methods this study showed that in q3 and q4 quarters the errors where the highest and volatility of errors in predicting inflation was the greatest during the shock of COVID 19 crises.