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本库首次提交范围:2004-06-23 — 2026-10-14。
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A Large Confirmatory Dynamic Factor Model for Stock Market Returns in Different Time Zones

跨时区股票收益的大型验证性动态因子模型

We propose a confirmatory dynamic factor model for a large number of stocks whose returns are observed daily across multiple time zones. The model has a global factor and a continental factor that both drive the individual stock return series. We propose two estimators of the model: a quasi-maximum likelihood estimator (QML-just-identified), and an improved estimator based on an Expectation Maximization (EM) algorithm (QML-all-res). Our estimators are consistent and…

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Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance

不完全依从的协变量自适应随机化中利用回归调整提高估计效率

We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-adjusted estimators, which are based on the doubly robust moment for local average treatment effects, are consistent and asymptotically normal even with heterogeneous probability of assignment and misspecified regression adjustments. We propose an optimal but potentially misspecified linear…

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Estimation of the Kronecker Covariance Model by Quadratic Form

Kronecker 协方差模型的二次型估计

We propose a new estimator, the quadratic form estimator, of the Kronecker product model for covariance matrices. We show that this estimator has good properties in the large dimensional case (i.e., the cross-sectional dimension $n$ is large relative to the sample size $T$). In particular, the quadratic form estimator is consistent in a relative Frobenius norm sense provided $\log^3n/T\to 0$. We obtain the limiting distributions of Lagrange multiplier (LM) and Wald tests…

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Supplementary Material for "Estimation of a Multiplicative Correlation Structure in the Large Dimensional Case"

《高维情形下乘法相关结构的估计》补充材料

Supplementary Material for "Estimation of a Multiplicative Correlation Structure in the Large Dimensional Case"

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Estimation of a Multiplicative Correlation Structure in the Large Dimensional Case

高维情形下乘法相关结构的估计

We propose a Kronecker product model for correlation or covariance matrices in the large dimensional case. The number of parameters of the model increases logarithmically with the dimension of the matrix. We propose a minimum distance (MD) estimator based on a log-linear property of the model, as well as a one-step estimator, which is a one-step approximation to the quasi-maximum likelihood estimator (QMLE). We establish rates of convergence and central limit theorems (CLT)…

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