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带跟踪误差与权重约束的高维投资组合分析AI 解读

This paper explores the statistical properties of forming constrained optimal portfolios within a high-dimensional set of assets. We examine portfolios with tracking error constraints, those with simultaneous tracking error and weight restrictions, and portfolios constrained solely by weight. Tracking error measures portfolio performance against a benchmark (typically an index), while weight constraints determine asset allocation based on regulatory requirements or fund…

高维协变量下对异方差稳健的过度识别约束检验AI 解读

This paper proposes an overidentifying restriction test for high-dimensional linear instrumental variable models. The novelty of the proposed test is that it allows the number of covariates and instruments to be larger than the sample size. The test is scale-invariant and robust to heteroskedastic errors. To construct the final test statistic, we first introduce a test based on the maximum norm of multiple parameters that could be high-dimensional. The theoretical power…

大量混合工具变量与控制变量下的内生处理效应估计AI 解读

Abstract Instrumental variables (IVs) and control variables are frequently used to assist researchers in investigating endogenous treatment effects. When used together, their identities are typically assumed to be known. However, in many practical situations, one is faced with a large and mixed set of covariates, some of which can serve as excluded IVs, some can serve as control variables, whereas others should be discarded from the model. It is often not possible to…

众多弱工具变量与无效工具变量下的工具变量估计AI 解读

Abstract We discuss the fundamental issue of identification in linear instrumental variable (IV) models with unknown IV validity. With the assumption of the ‘sparsest rule’, which is equivalent to the plurality rule but becomes operational in computation algorithms, we investigate and prove the advantages of non-convex penalized approaches over other IV estimators based on two-step selections, in terms of selection consistency and accommodation for individually weak IVs.…

时变最小方差投资组合AI 解读

This paper proposes a new time-varying minimum variance portfolio (TV-MVP) in a large investment universe of assets. Our method extends the existing literature on minimum variance portfolios by allowing for time-varying factor loadings, which facilitates the capture of the dynamics of the covariance structure of asset returns (and hence, the optimal investment strategy in a dynamic setting). We also use a shrinkage estimation method based on a quasi-likelihood function to…

高维数据下条件平均处理效应的估计AI 解读

Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the…

大型似不相关回归系统：惩罚准极大似然估计AI 解读

In this article, using a shrinkage estimator, we propose a penalized quasi-maximum likelihood estimator (PQMLE) to estimate a large system of equations in seemingly unrelated regression models, where the number of equations is large relative to the sample size. We develop the asymptotic properties of the PQMLE for both the error covariance matrix and model coefficients. In particular, we derive the asymptotic distribution of the coefficient estimator and the convergence rate…

非参数加性工具变量估计：组收缩估计视角AI 解读

In this article, we study a nonparametric approach regarding a general nonlinear reduced form equation to achieve a better approximation of the optimal instrument. Accordingly, we propose the nonparametric additive instrumental variable estimator (NAIVE) with the adaptive group Lasso. We theoretically demonstrate that the proposed estimator is root-n consistent and asymptotically normal. The adaptive group Lasso helps us select the valid instruments while the dimensionality…

混合广义经验似然估计：使用自适应Lasso选择工具变量AI 解读

In this paper, we use the adaptive lasso estimator to choose the relevant instruments and eliminate the irrelevant instruments. The limit theory of Zou (2006) is extended from univariate iid case to heteroskedastic and non Gaussian data. Then we use the selected instruments in generalized empirical likelihood estimators (GEL). In this sense, these are called hybrid GEL. It is also shown that the lasso estimators are not model selection consistent whereas the adaptive lasso…