Mildly explosive autoregression with anti-persistent errors

• Main Authors: LUI, Yiu Lim, XIAO, Weilin, Jun YU
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Anti-persistent; Unit root; Mildly explosive; Limit theory; Bubble; Fractional integration; Young integral; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/2434; https://ink.library.smu.edu.sg/context/soe_research/article/3433/viewcontent/Antipersistence15_sv.pdf
• Institution: Singapore Management University

An asymptotic distribution is derived for the least squares (LS) estimate of a first‐order autoregression with a mildly explosive root and anti‐persistent errors. While the sample moments depend on the Hurst parameter asymptotically, the Cauchy limiting distribution theory remains valid for the LS estimates in the model without intercept and a model with an asymptotically negligible intercept. Monte Carlo studies are designed to check the precision of the Cauchy distribution in finite samples. An empirical study based on the monthly NASDAQ index highlights the usefulness of the model and the new limiting distribution.