Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product

Abstract

As an important indicator that can reflect a country’s macroeconomic situation and future trend, experts and scholars have long focused on analyses and predictions of gross domestic product (GDP). Combining principal component analysis (PCA), the mixed-frequency data sampling (MIDAS) model and the error correction model (ECM), this investigation constructs the principal-component-based ECM-MIDAS and co-integration MIDAS (CoMIDAS) models, respectively. After that, this investigation uses the monthly consumption, investment and trade data to build a mixed-frequency model to predict quarterly GDP. The empirical results can be summarized as follows: First, the predictive effectiveness of the mixed-frequency model is better than that of the same-frequency model. Second, the three variables have a strong correlation, and applying the principal component idea when modelling the same and mixed frequencies can lead to more favourable predictive effectiveness. Third, adding an error correction term to the principal-component-based mixed-frequency model has a significant coefficient and a higher predictive accuracy. Based on the above, it can be concluded that combining the MIDAS model with error correction and a principal component is effective; thus, this combination may be applied to support real-time and accurate macroeconomic prediction.