Author: Cesar C. Rufino
Year: 2019, Volume 29 No. 1
One of the most anticipated data releases of the Philippine statistical system is the quarterly real gross domestic product. This all-important variable provides the basis of establishing the economic performance of the country on a year-on-year basis. Official publication of this statistic, however, comes at a significant delay of up to two months, upsetting the planning function of various economic stakeholders. Under this backdrop, data scientists coined the term “nowcasting” which refers to the prediction of the present, the very near future, and the very recent past based on information provided by available data that are sampled at higher frequencies (monthly, weekly, daily, etc.). This study aims to demonstrate the viability of using a state-of-the-art technique called MIDAS (Mixed Data Sampling) regression to solve the mixed frequency problem in implementing the nowcasting of the country’s economic growth. Different variants of the MIDAS model are estimated
using quarterly Real GDP data and monthly data on inflation, industrial production, and Philippine Stock Exchange index. These models are empirically compared against each other and the models traditionally used by forecasters in the context of mixed frequency. The results indicate the relative superiority of the MIDAS framework in accurately predicting the growth trajectory of the economy using information from high-frequency economic indicators.
Keywords: Nowcasting, MIDAS Regression, Mixed Frequency Problem, Temporal Aggregation, Ragged Edge Problem,
JEL Classification: C13, C52, C53, E17