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自回归条件贝塔模型AI 解读

This paper introduces an autoregressive conditional beta (ACB) model that allows regressions with dynamic betas (or slope coefficients) and residuals with GARCH conditional volatility. The model fits in the (quasi) score-driven approach recently proposed in the literature, and it is semi-parametric in the sense that the distributions of the innovations are not necessarily specified. The time-varying betas are allowed to depend on past shocks and exogenous variables. We…

高维工具变量协整估计与推断AI 解读

A semiparametric triangular systems approach shows how multicointegrating linkages occur naturally in an I(1) cointegrated regression model when the long run error variance matrix in the system is singular. Under such singularity, cointegrated I(1) systems embody a multicointegrated structure that makes them useful in many empirical settings. Earlier work shows that such systems may be analyzed and estimated without appealing to the associated I(2) system but with suboptimal…

一般政策干预的无条件效应AI 解读

This paper studies the unconditional effects of a general policy intervention, which includes location-scale shifts and simultaneous shifts as special cases. The location-scale shift is intended to study a counterfactual policy aimed at changing not only the mean or location of a covariate but also its dispersion or scale. The simultaneous shift refers to the situation where shifts in two or more covariates take place simultaneously. For example, a shift in one covariate is…

部分识别控制函数模型中的推断AI 解读

In multiple contributions to the literature, James L. Powell and coauthors have developed estimators for semiparametric models where sample selectivity and/or endogeneity can be handled through a “control function”. Their methods rely on pairwise comparisons of observations which match (asymptotically) the control functions. Conditional on this matching, a moment condition can identify the parameters of the model. However, there exist instances where the control functions…

混杂变量维度增长时利用神经网络推断一般处理效应AI 解读

Semiparametric efficient estimation of various multi-valued causal effects, including quantile treatment effects, is important in economic, biomedical, and other social sciences. Under the unconfoundedness condition, adjustment for confounders requires estimating the nuisance functions relating outcome or treatment to confounders nonparametrically. This paper considers a generalized optimization framework for efficient estimation of general treatment effects using artificial…

含协整解释变量的半参数单指数预测回归AI 解读

This paper considers the estimation of a semi-parametric single-index regression model that allows for nonlinear predictive relationships. This model is useful for predicting financial asset returns, whose observed behaviour resembles a stationary process, if the multiple nonstationary predictors are cointegrated. The presence of cointegrated regressors imposes a single-index structure in the model, and this structure not only balances the nonstationarity properties of the…

非参数工具变量模型中的自适应速率最优假设检验AI 解读

We propose a new adaptive hypothesis test for inequality (e.g., monotonicity, convexity) and equality (e.g., parametric, semiparametric) restrictions on a structural function in a nonparametric instrumental variables (NPIV) model. Our test statistic is based on a modified leave‐one‐out sample analog of a quadratic distance between the restricted and unrestricted sieve two‐stage least squares estimators. We provide computationally simple, data‐driven choices of sieve tuning…

非高斯结构向量自回归模型的局部稳健推断AI 解读

All parameters in structural vector autoregressive (SVAR) models are locally identified when the structural shocks are independent and follow non‐Gaussian distributions. Unfortunately, standard inference methods that exploit such features of the data for identification fail to yield correct coverage for structural functions of the model parameters when deviations from Gaussianity are small. To this extent, we propose a locally robust semiparametric approach to conduct…

固定效应多项选择模型的矩不等式AI 解读

This paper proposes a new approach to identification of the semiparametric multinomial choice model with fixed effects. The framework employed is the semiparametric version of the traditional multinomial logit with the fixed‐effects model (Chamberlain (1980)). This semiparametric multinomial choice model places no restrictions on either the joint distribution of the random utility disturbances across choices or their within group (or across time) correlations. We show that a…

平坦极限下的高斯过程回归AI 解读

Gaussian process (GP) regression is a fundamental tool in Bayesian statistics. It is also known as kriging and is the Bayesian counterpart to the frequentist kernel ridge regression. Most of the theoretical work on GP regression has focused on a large-n asymptotics, characterising the behaviour of GP regression as the amount of data increases. Fixed-sample analysis is much more difficult outside of simple cases, such as locations on a regular grid. In this work, we perform a…

长期处理效应的半参数估计AI 解读

Long-term outcomes of experimental evaluations are necessarily observed after long delays. We develop semiparametric methods for combining the short-term outcomes of experiments with observational measurements of short-term and long-term outcomes, in order to estimate long-term treatment effects. We characterize semiparametric efficiency bounds for various instances of this problem. These calculations facilitate the construction of several estimators. We analyze the…

改进聚束与缺口分析AI 解读

This paper studies the bunching identification strategy for an elasticity parameter that summarizes agents’ responses to changes in slope (kink) or intercept (notch) of a schedule of incentives. We show that current bunching methods may be very sensitive to implicit assumptions in the literature about unobserved individual heterogeneity. We overcome this sensitivity concern with new non- and semi-parametric estimators. Our estimators allow researchers to show how bunching…

多个分位数的半参数建模AI 解读

We develop a semiparametric model to track a large number of quantiles of a time series. The model satisfies the condition of non-crossing quantiles and the defining property of fixed quantiles. A key feature of the specification is that the updating scheme for time-varying quantiles at each probability level is based on the gradient of the check loss function. Theoretical properties of the proposed model are derived such as weak stationarity of the quantile process and…

反事实密度的半参数估计AI 解读

Abstract Causal effects are often characterized with averages, which can give an incomplete picture of the underlying counterfactual distributions. Here we consider estimating the entire counterfactual density and generic functionals thereof. We focus on two kinds of target parameters: density approximations and the distance between counterfactual densities. We study nonparametric efficiency bounds, giving results for smooth but otherwise generic models and distances.…

近端中介分析AI 解读

Summary A common concern when trying to draw causal inferences from observational data is that the measured covariates are insufficiently rich to account for all sources of confounding. In practice, many of the covariates may only be proxies of the latent confounding mechanism. Recent work has shown that in certain settings where the standard no-unmeasured-confounding assumption fails, proxy variables can be leveraged to identify causal effects. Results currently exist for…

含无效工具变量的半参数有效G估计AI 解读

Summary The instrumental variable method is widely used in the health and social sciences for identification and estimation of causal effects in the presence of potential unmeasured confounding. To improve efficiency, multiple instruments are routinely used, raising concerns about bias due to possible violation of the instrumental variable assumptions. To address such concerns, we introduce a new class of G-estimators that are guaranteed to remain consistent and…

数据融合下的有效估计AI 解读

Summary We aim to make inferences about a smooth, finite-dimensional parameter by fusing together data from multiple sources. Previous works have studied the estimation of a variety of parameters in similar data fusion settings, including estimation of the average treatment effect and average reward under a policy, with the majority of them merging one historical data source with covariates, actions and rewards, and one data source of the same covariates. In this article, we…

高维半参数高斯 Copula 向量自回归模型的估计与推断AI 解读

This paper develops simple, robust estimation and inference methods for the transition matrix of a high-dimensional semiparametric Gaussian copula vector autoregressive process. Our estimator is based on rank estimators of the large variance and auto-covariance matrices of a transformed latent high-dimensional Gaussian process. We derive rates of convergence of our estimator based on which we develop de-biased inference for Granger causality. Numerical results demonstrate…

Fine–Gray 模型中的有效估计AI 解读

Direct regression for the cumulative incidence function (CIF) has become increasingly popular since the Fine and Gray model was suggested (Fine and Gray) due to its more direct interpretation on the probability risk scale. We here consider estimation within the Fine and Gray model using the theory of semiparametric efficient estimation. We show that the Fine and Gray estimator is semiparametrically efficient in the case without censoring. In the case of right-censored data,…

基于常微分方程的生存分析AI 解读

This article introduces an Ordinary Differential Equation (ODE) notion for survival analysis. The ODE notion not only provides a unified modeling framework, but more importantly, also enables the development of a widely applicable, scalable, and easy-to-implement procedure for estimation and inference. Specifically, the ODE modeling framework unifies many existing survival models, such as the proportional hazards model, the linear transformation model, the accelerated…

从条件分位数回归到边际分位数估计：缺失数据与因果推断应用AI 解读

It is well known that information on the conditional distribution of an outcome variable given covariates can be used to obtain an enhanced estimate of the marginal outcome distribution. This can be done easily by integrating out the marginal covariate distribution from the conditional outcome distribution. However, to date, no analogy has been established between marginal quantile and conditional quantile regression. This article provides a link between them. We propose two…

教师与班级的配置及学生成绩AI 解读

We study the effects of counterfactual teacher-to-classroom assignments on average student achievement in U.S. elementary and middle schools. We use the Measures of Effective Teaching (MET) experiment to semiparametrically identify the average reallocation effects (AREs) of such assignments. Our identification strategy exploits the random assignment of teachers to classrooms in MET schools. To account for noncompliance of some students and teachers to the random assignment,…

面板数据模型中追寻同质分组的非参数分位数回归AI 解读

Many panel data have the latent subgroup effect on individuals, and it is important to correctly identify these groups since the efficiency of resulting estimators can be improved significantly by pooling the information of individuals within each group. However, the currently assumed parametric and semiparametric relationship between the response and predictors may be misspecified, which leads to a wrong grouping result, and the nonparametric approach hence can be…

广义协方差估计量AI 解读

We consider a class of semi-parametric dynamic models with iid errors, including the nonlinear mixed causal-noncausal Vector Autoregressive (VAR), Double-Autoregressive (DAR) and stochastic volatility models. To estimate the parameters characterizing the (nonlinear) serial dependence, we introduce a generic Generalized Covariance (GCov) estimator, which minimizes a residual-based multivariate portmanteau statistic. In comparison to the standard methods of moments, the GCov…