A comparison of using MIDAS and LSTM models for GDP nowcasting

The paper elaborates on machine and deep learning methods, as well as mixed data sampling regression models, used for GDP nowcasting. The aim is to select an adequate model that shows better performance on the data used. The paper provides an answer to the question of whether the use of deep learning methods can improve GDP nowcasting compared to traditional econometric methods, as well as whether the use of specific high-frequency indicators improves the quality of the models used. The paper examines the selection of adequate indicators – both official and those from alternative sources, presents the framework of mixed data sampling regression models and deep learning models used for nowcasting, and gives an assessment of two such models on the example of Serbian GDP. Serbia’s GDP was modelled for the period Q1 2016 – Q2 2023 and the end of the observed period (six quarters) was used for the forecast. Finally, two assessed models were compared – the mixed data sampling regression model and the LSTM neural network. A special focus is placed on ways to improve both models. The LSTM recurrent neural network model had a smaller forecast error, with the use of a combination of official and alternative (high-frequency) indicators, but the mixed data sampling regression model also proved to be a good tool for decision-makers, since its structure allows insight into the ongoing movements impacting GDP dynamics. The use of alternative indicators in nowcasting improved the projections through both presented models.