Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia

Abstract

Aggregate demand forecasting, also known as nowcasting when it applies to current quarter assessment, is of notable interest to policy makers. This paper concentrates on the empirical methods dealing with mixed-frequency data. In particular, it focuses on the MIDAS approach and its later extension, the Bayesian MFVAR. The two strategies are evaluated in terms of their accuracy to nowcast Macedonian GDP growth, using same monthly frequency data set. The results of this study indicate that the MIDAS regressions demonstrate comparable forecasting performance to that of MF-VAR model. Moreover, it is interesting to note that the two approaches are reciprocal, since in general, their combined forecast demonstrates clear superiority in predicting business cycle turning points. Additionally, the MF-VAR model showed higher precision in times of increased uncertainty.

Improving forecasting accuracy using quantile regression neural network combined with unrestricted mixed data sampling

“…The findings suggest that machine learning approaches improve nowcasting accuracy [20,52]. The main premise of nowcasting is to construct an "early estimate" before the data is officially released by using information available earlier and at a higher frequency than the variable of interest [53].…”

“…The vast majority of these became stationary at the log of the first difference. Monthly data, such as the industrial output volume index, consumer price index, Real M2 money stock, and so on, can be used to estimate GDP [53]. The GACV, BIC, or AIC will be used to choose the best values for the lag order and the inner layer nodes.…”