Start
January 25, 2022 - 9:00 am
End
January 25, 2022 - 10:00 am
Address
Zoom Meeting View mapTracking growth during the new wave of COVID-19 using Google Trends
Student: Võ Phước Trí, VNP 25
Supervisor: Dr. Trần Thị Tuấn Anh
Abstract:
The immediate impact of coronavirus requires the high-frequency data to adapt well to the variation of the macro-economy. Applying the Artificial Neural Network algorithm, this thesis aims to construct a Weekly Tracker, which is used for tracking the growth rate, from the Google Trends dataset. Compared to the Autoregressive model, this thesis shows that predictions from the Artificial Neural Network algorithm outperform in leading information of growth rate movement. The Weekly Tracker suggests that the recovery of 22 economies in the sample has been constrained during the new wave of Covid-19. The growth rate estimate may be still positive, but the trend is horizontal, even in developed countries. And with the Shapley value analysis, this thesis finds out Consumption topics and categories, especially trade and services, are the variables that have a key role for the recovery; Economic Anxiety topics and categories are extremely popular during the coronavirus spread out, especially in the beginning, and have large negative contributions; number searches of Labor Market and Health Condition topics and categories also increase during the pandemic and they contribute positively to the Weekly Tracker. Focusing on Vietnam, this thesis finds out the down trend of the Weekly Tracker during the social-distancing and locked down period in Ho Chi Minh City, same as the movement of Consumption searches number.
JEL Classification: O11, C14, C45
Keywords: growth rate, pandemic time, Covid-19 pandemic, neural network, machine learning, Shapley values
