Abstract—This paper proposes a new Okun Coefficient with error terms distributed as Asymmetric Exponential Power Distribution proposed by Zhu and Zinde-Walsh. Method of Maximum Likelihood Estimation is used to estimate this model. Markov Chain Monte Carlo method (MCMC) is used to generate random variables from AEPD for simulation. In empirical analysis, U.S. and Japan are studied from 1999 Q1 to 2019 Q4. Empirical results show Okun theory is partly supported by US and Japan if the error assumption is changed from Normal to AEPD. Likelihood Ratio (LR) test proves the existence of fat tailness and skewness in residuals and based on Kolmogorov-Smirnov (KS) test, it is accepted that residuals follow AEPD under 5% significance level. Finally, Okun-AEPD has better in-sample fit than Okun-Normal by Akaike Information Criterion (AIC).

Index Terms—Asymmetric Exponential Power Distribution (AEPD), Markov Chain Monte Carlo (MCMC), Maximum Likelihood Estimation (MLE), Okun’s law.

Qinghan Chen is with Beijing 101 High School, No.11 Yiheyuan Road, Haidian District, Beijing, China.

Cite:Qinghan Chen, "A New Okun Coefficient Based on AEPD," International Journal of Applied Physics and Mathematics vol. 11, no. 1, pp. 17-24, 2021.

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