A1181
Title: Comparison between forecasting and nowcasting of digital economy
Authors: Pairach Piboonrungroj - Chiang Mai University (Thailand) [presenting]
Abstract: Recently, especially since the Coronavirus pandemic (COVID-19), there has been an urgency for real-time (or nearly) economic indicator reports. However, traditional economic indicators such as Gross Domestic Product (GDP) or Employment rate are mostly reported at a low frequency, quarterly or annually. Hence the status quo of economic indicator reporting was found insufficient for economic policy maker to respond to the economic impacts of COVID-19 and the response measure such as lockdowns. Hence, researchers and government agencies attempt to explore alternative indicators to monitor real-time and precise economic situations. Economic trackers proposed using high-frequency data from private sectors and visualizing data on the map by using spatial econometrics. Also, machine learning is employed to analyze alternative data such as Google trends to nowcast the GDP of OECD members. Methods were proposed to nowcast and forecast the digital economy of Thailand using the scope of OECD to track digital economic development. The performance of nowcasting and forecasting are compared and discussed in future research avenues.
