Fitting and prediction of China's output gap: an empirical study on ARMA model

Lei Shen; Zebin Liu

doi:10.1117/12.2627493

22 April 2022 Fitting and prediction of China's output gap: an empirical study on ARMA model

Lei Shen, Zebin Liu

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 1216321 (2022) https://doi.org/10.1117/12.2627493
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Output gap is an important index to analyze the macroeconomic operation situation. In the macroeconomic policy framework, the formulation of many policies depends on evaluating the output gap. According to the impact of COVID-19 on China's economic growth in 2020, this paper aims to explore the future change law of China's output gap. Firstly, China's real GDP growth rate data is calculated according to the original GDP data. Secondly, the potential output and output gap are estimated by H-P filtering method. Finally, the output gap series is brought into the ARMA model for fitting and prediction. To sum up, under the influence of COVID-19, China's actual economic growth level was significantly lower than the potential economic growth in 2020, forming a higher negative output gap. The epidemic's impact on China's actual economic growth will last for four years, and China's output gap will return to a stable state slightly less than zero in 2025.

Citation Download Citation

Lei Shen and Zebin Liu "Fitting and prediction of China's output gap: an empirical study on ARMA model", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 1216321 (22 April 2022); https://doi.org/10.1117/12.2627493

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available