近日，tyc1286太阳成集团统计与数据科学系柯书尧助理教授的合作论文“Robust inference of panel data models with interactive fixed effects under long memory: A frequency domain approach”（合作者：Peter C. B. Phillips, Liangjun Su）在线发表于计量经济学领域国际顶级期刊Journal of Econometrics.
论文摘要
This paper studies a linear panel data model with interactive fixed effects wherein regressors, factors and idiosyncratic error terms are all stationary but with potential long memory. The setup involves a new formulation of panel data models, where weakly dependent regressors, factors and idiosyncratic errors are embedded as a special case. Standard methods based on principal component decomposition and least squares estimation, as in Bai (2009), are found to be biased and distorted in inference. To cope with this failure and to provide a simple implementable estimation procedure, a frequency domain least squares estimation is proposed. The limit distribution of the frequency domain estimator is established and a self-normalized approach to inference without the need for plug-in estimation of memory parameters is developed. Simulations show that the frequency domain estimator performs robustly under short memory and outperforms the time domain estimator when long range dependence is present. An empirical illustration is provided, examining the long-run relationship between stock returns and realized volatility.
论文链接：https://doi.org/10.1016/j.jeconom.2024.105761
作者简介
柯书尧，tyc1286太阳成集团统计与数据科学系助理教授。主要研究方向包括：面板数据、因子模型、长记忆过程、频域分析，在Journal of Econometrics上发表论文；并担任Journal of Econometrics, Econometric Reviews等期刊匿名审稿人。
校对|麦嘉杰
责编|庄诗蓓
初审|麦嘉杰
复审|孙兰
终审发布|何凌云
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