Methodological Aspects on Choosing the Right Data and Its Impact on Forecasting Accuracy – A Case Study with MIDAS
Sukhbir Kaur, Sukhbir Singh, Kanchan Jain, Pooja Soni
Abstract
Mixed data sampling (MIDAS) regression models have gained popularity for forecasting macroeconomic variables using high-frequency indicators. However, macroeconomic data is often subject to revisions error, which can affect the robustness of these models. This paper aims to investigate the performance of MIDAS models specifically focusing on forecasting based on data contaminated with revisions. It is examined how well MIDAS regression models perform in terms of predictions when applied to such data. Also, the impact of using the latest available information versus the first announced information for model building is studied for examining the influence of timing of data availability on the model’s performance and on nowcasting and forecasting accuracy.
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