報(bào)告承辦單位: 長(zhǎng)沙理工大學(xué)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院
報(bào)告內(nèi)容: Automated Estimation of Heavy-tailed Vector Error Correction Models
報(bào)告人姓名: 凌仕卿
報(bào)告人所在單位: 香港科技大學(xué)數(shù)學(xué)系
報(bào)告人職稱/職務(wù)及學(xué)術(shù)頭銜: 教授/IMS Fellow
報(bào)告時(shí)間: 2019年5月21日周二下午3:30
報(bào)告地點(diǎn): 理科樓A-419
報(bào)告人簡(jiǎn)介: 凌仕卿教授于1997年取得香港大學(xué)統(tǒng)計(jì)學(xué)博士學(xué)位，1997年至2000年西澳大學(xué)經(jīng)濟(jì)學(xué)系博士后，2000年至2006年香港科技大學(xué)數(shù)學(xué)系助理教授，2003年至2006年受聘于西澳大學(xué)經(jīng)濟(jì)學(xué)系和數(shù)學(xué)與統(tǒng)計(jì)系兼職副教授，2006年至2010年香港科技大學(xué)數(shù)學(xué)系副教授，2010年至今香港科技大學(xué)數(shù)學(xué)系教授。凌教授的主要研究方向?yàn)椋捍髽颖纠碚摗⒔?jīng)驗(yàn)過程、非平穩(wěn)時(shí)間序列、非線性時(shí)間序列及計(jì)量經(jīng)濟(jì)學(xué)。現(xiàn)為《Journal of Time Series Analysis》聯(lián)合編輯《Statistics & Probability Letters》、《Bernoulli》、《Electronic Journal of Statistics》、《Journal of the Japan Statistical Association》國(guó)際期刊的副主編。2003年和2013年分別榮獲澳大利亞和新西蘭MSS委員會(huì)頒發(fā)的Early Career Research Excellence Prize和Biennial Medal，2005年當(dāng)選為國(guó)際統(tǒng)計(jì)學(xué)會(huì)會(huì)員；2007年榮獲計(jì)量經(jīng)濟(jì)學(xué)期刊(Econometric Theory)頒發(fā)的Multa Scripsit Award的獎(jiǎng)勵(lì)，2013年當(dāng)選為澳大利亞和新西蘭MSS的Fellow。2015年當(dāng)選為ITTI的Inaugural Distinguished Fellow。2019年當(dāng)選為IMS Fellow。
報(bào)告摘要：It has been a challenging problem to determine the co-integrating rank in the vector error correction (VEC) model when its noise is a heavy-tailed random vector. This paper proposes an automated approach via adaptive shrinkage techniques to determine the co-integrating rank and estimate parameters simultaneously in the VEC model with unknown order $p$ when its noises are i.i.d. heavy-tailed random vectors with tail index $\alpha\in (0.2)$. It is showed that the estimated co-integrating rank and order $p$ equal to the true rank and the true order $p_{0}$, respectively, with probability trending to 1 as the sample size $n\to\infty$, while other estimated parameters achieve the oracle property, that is, they have the same rate of convergence and the same limiting distribution as those of estimated parameters when the co-integrating rank and the true order $p_{0}$ are known. This paper also proposes a data-driven procedure of selecting the tuning parameters. Simulation studies are carried to evaluate the performance of this procedure in finite samples. Our techniques are applied to explore the long-run andshort-run behavior of prices of wheat, corn and wheat in USA. Our results may provide a new insight to the Lasso approach for both stationary and non-stationary heavy-tailed time series.
