## 5 Iterative Forecasting Experiment  
To further explore the potential of the proposed approach for short-term economic forecasting, we designed an iterative out-of-sample forecasting experiment in which we re-ran the evolutionary process for each period of the out-of-sample subset using a rolling estimation window. We compared the obtained results with autoregressive moving average (ARIMA) forecasts used as a benchmark. The selected models are displayed in Table 14 in the Appendix C.  
In order to determine the number of lags that should be included in the model, we have selected the model with the lowest value of the Akaike information criterion (AIC), considering models with a minimum number of 1 lag up to a maximum of 4, including all the intermediate lags. In Table 4, we present the results of comparing the out-of-sample forecasting performance of the proposed approach to rolling ARIMA forecasts used as a benchmark for two different forecast horizons (\(h\)). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{\(h=1\)} & \multicolumn{3}{c}{\(h=4\)} \\ \hline  & **Evo.Exp** & **ARIMA** & **HLN** & **Evo.Exp** & **ARIMA** & **HLN** \\ \hline Austria & 0.474 & 0.883 & \(-\)0.835 & 0.475 & 1.590 & \(-\)2.103 \\ Belgium & 0.421 & 1.093 & \(-\)2.729 & 0.555 & 1.139 & \(-\)2.384 \\ Bulgaria & 0.650 & 0.316 & 2.190 & 0.928 & 0.688 & 0.668 \\ Czech Republic & 0.629 & 1.264 & \(-\)1.493 & 0.929 & 2.578 & \(-\)4.068 \\ Denmark & 0.599 & 1.410 & \(-\)3.084 & 0.624 & 2.195 & \(-\)4.073 \\ Finland & 0.657 & 0.832 & \(-\)1.413 & 0.719 & 1.181 & \(-\)1.193 \\ France & 0.908 & 1.541 & \(-\)1.108 & 1.047 & 2.275 & \(-\)1.599 \\ Germany & 0.530 & 0.905 & \(-\)1.203 & 0.711 & 1.594 & \(-\)2.581 \\ Greece & 0.523 & 0.952 & \(-\)2.280 & 0.535 & 1.827 & \(-\)3.175 \\ Hungary & 0.528 & 2.866 & \(-\)4.146 & 1.829 & 2.845 & \(-\)0.860 \\ Italy & 0.649 & 1.493 & \(-\)1.447 & 1.055 & 1.849 & \(-\)2.957 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Forecast accuracy (RMSFE): Iteratively evolved expectations (Evo.Exp) vs. ARIMA. We find that in all countries except Bulgaria, iterative sentiment indicators (Evo.Exp) produce lower RMSFE values than ARIMA models, regardless of the forecast horizon. This gain in forecast accuracy is significant in ten of the countries for one-quarter-ahead predictions (\(h=1\)), and in nine economies for four-quarter-ahead forecasts (\(h=4\)). Consequently, the iterative approach allows for the refining of the accuracy of the estimations obtained in the nowcasting exercise (Table 3). Compared to ARIMA predictions, the relative improvement of the proposed methodology increases along with the predictive horizon. Proof of this is that the RMSFE obtained for one- and four-quarter-ahead predictions is practically identical in most countries. The explanation lies fundamentally in the fact that the generated indicators tend to show stable behaviour over long periods. These results show the predictive potential of the proposed procedure, and provide evidence regarding the ability of GP to solve optimization problems related to economic modelling and forecasting. In this sense, our study connects with previous research by [30], who incorporated GP in a vector error correction framework and obtained better forecasts of US imports than with ARIMA models. Using information from the ifo's WES, in [65], the authors implemented GP to construct a leading indicator and a coincident indicator, obtaining more accurate forecasts with the latter. Similarly, in [66], the authors applied GP to develop a set of empirical models to forecast GDP, investment and loan rates in Poland, and found that the proposed approach outperformed artificial neural network models. Focusing on the EA, in [28], the usefulness of genetic algorithms to forecast quarterly GDP growth and monthly inflation was empirically demonstrated. Previous applications of evolutionary computing in finance have also shown the potential of GP for the prediction of exchange rates [7; 8; 39], and for stock price forecasting [35; 36; 37; 38].