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有向网络半参数形成模型的推断AI 解读

We propose a semiparametric model for dyadic link formations in directed networks. The model contains a set of degree parameters that measure different effects of popularity or outgoingness across nodes, a regression parameter vector that reflects the homophily effect resulting from the nodal attributes or pairwise covariates associated with edges, and a set of latent random noises with unknown distributions. Our interest lies in inferring the unknown degree parameters and…

通过再生核希尔伯特空间中的函数迁移学习调整选择偏差AI 解读

As accessibility to different data sources increases, transfer learning becomes prevalent in improving statistical efficiency by integrating different datasets. In this article, we focus on the popular non-probability sampling from a finite population and propose a functional transfer learning method to adjust its selection bias through calibrating marginal means of functions in a reproducing kernel Hilbert space by incorporating a reference probability sample. Compared with…

动态分位数的函数系数 VAR 模型及非参数金融网络构建AI 解读

In this article, we investigate a functional coefficient vector autoregressive model for conditional quantiles, in which the interdependences among tail risks such as Value-at-Risk are allowed to vary smoothly with a variable of general economy. Methodologically, we develop an easy-to-implement two-stage procedure to estimate functionals in the dynamic network system based on the deep learning method of neural networks and the local linear smoothing technique. We establish…

时间聚合对 MIDAS 回归的影响AI 解读

Time series used in practice are often temporal aggregates. A MIDAS regression model may be fitted wrongly assuming the independent variables are not aggregated when in fact they are. We derive the correct model’s specification under temporal aggregation of the independent variables, thus identifying the correct number of dynamic terms and the order of the MA component. We also show that these depend on the frequency of the variables and the aggregation order used. As a…

估计美式期权隐含的状态价格密度AI 解读

We propose a new method to estimate state price densities implicit in American-style options. The method involves estimating the parameters of a Gauss-Hermite series expansion and solving a sequence of recursive equations for the early exercise premium. The resulting estimator can capture sudden shifts in density that may occur during financial crises or in response to significant policy events. It also provides an estimate of the early exercise premium that is of…

高维函数时间序列的因子建模AI 解读

Many economic and scientific problems involve the analysis of high-dimensional functional time series, where the number of functional variables p diverges as the number of serially dependent observations n increases. In this paper, we present a novel functional factor model for high-dimensional functional time series that maintains and makes use of the functional and dynamic structure to achieve great dimension reduction and find the latent factor structure. To estimate the…

连续处理的双重去偏机器学习非参数推断AI 解读

We propose a doubly robust inference method for causal effects of continuous treatment variables, under unconfoundedness and with nonparametric or high-dimensional nuisance functions. Our double debiased machine learning (DML) estimators for the average dose-response function (or the average structural function) and the partial effects are asymptotically normal with nonparametric convergence rates. The first-step estimators for the nuisance conditional expectation function…

具有不可观测分组异质性的面板自回归模型统一推断AI 解读

This paper considers unified estimation and inference in panel autoregressive (PAR) models. The PAR coefficients are assumed to contain a latent group structure that allows the degree of persistence for each time series to be heterogeneous and unknown. We propose a novel penalized weighted least squares approach to simultaneously identify the unknown group membership and consistently estimate the PAR coefficients, regardless of whether the underlying PAR process is…

具有函数型协变量的非参数因果推断AI 解读

Functional data and their analysis have become increasingly popular in various fields of data science. This paper considers estimation and inference of the average treatment effect under unconfoundedness when the covariates involve a functional variable, and proposes the inverse probability weighting estimator, where the propensity score is estimated by utilizing a kernel estimator for functional variables. We establish the n-consistency and asymptotic normality of the…

共同成分结构 VARAI 解读

Structural VAR models (SVAR) produce results that can vary dramatically with the choice of variables, because information is deficient. We argue that if the variables of interest belong to a High-Dimensional Factor Model and are replaced in the SVAR by their common components, the information issue finds a solution, provided that the number of common components is larger than the number of structural shocks, so that the SVAR is dynamically singular. This is the Common…

气候敏感度是否因地区而异？变系数方法AI 解读

Global mean surface temperature has been increasing in response to growing greenhouse gas concentrations (IPCC, 2021). While Earth is getting warmer overall, regions that have different local geographical features experience unequal increases in temperature. In this paper, we develop a varying-coefficient dynamic panel data model and use it to measure local climate sensitivity, defined as the increase in temperature in a given location following a doubling of CO2…

隐含波动率预测中的神经切线核：非线性函数自回归方法AI 解读

Forecasting implied volatility across different levels of moneyness and maturity is crucial yet challenging due to the high dimensionality of the Implied Volatility Surface (IVS) and the nonlinearity that characterizes its temporal dependence. We adopt a Nonlinear Functional Autoregressive (NFAR) framework to a sequence of IVS and employ neural networks that admit a Neural Tangent Kernel (NTK) parameterization to capture nonlinear interactions between surfaces. We illustrate…

未知测量误差分布下的非参数分位数回归与一致推断AI 解读

This article studies the nonparametric estimation and uniform inference for the conditional quantile regression function (CQRF) with covariates exposed to measurement errors. We consider the case that the distribution of the measurement error is unknown and allowed to be either ordinary or super smooth. We estimate the density of the measurement error by the repeated measurements and propose the deconvolution kernel estimator for the CQRF. We derive the uniform Bahadur…

二元关系数据的双重／去偏机器学习AI 解读

This article presents novel methods and theories for estimation and inference about parameters in statistical models using machine learning for nuisance parameter estimation when data are dyadic. We propose a dyadic cross-fitting method to remove over-fitting biases under arbitrary dyadic dependence. Together with the use of Neyman orthogonal scores, this novel cross-fitting method enables root- n consistent estimation and inference robustly against dyadic dependence. We…

福利改革实验及其儿童影响的结构元分析AI 解读

Using a model of maternal labor supply and investment in children, this paper synthesizes the findings from three separate welfare reform experiments across six sites. The proposed model maps variation in experimental design to parameters that define labor supply behavior, childcare use, and the importance of money and childcare arrangements in the development of child skills. The estimation procedure—which aggregates available evidence to identify the model’s key causal…

第一价格拍卖的简便稳健识别方法AI 解读

This paper proposes a new approach to the identification of first-price auctions that is robust to overbidding, but at the same time remains contiguous with the canonical point-identification approach of Guerre et al. (2000) (GPV) and its simple estimators. We show that a weak identifying restriction allows us to reinterpret the GPV estimates as a bound. We demonstrate that the identifying restriction holds in a set of commonly used auction models that can generate…

具有网络关联固定效应的动态面板分位数回归AI 解读

This paper introduces a dynamic panel data quantile regression model with network-linked fixed effects, named DQR-NFE, in which unobserved individual heterogeneity is structured through an underlying network. The corresponding estimator is derived by incorporating a quantile network cohesion (QNC) penalty into the dynamic panel quantile regression framework. This penalty encourages connected units within the network to exhibit similar conditional quantiles, with a…

局部投影的 GLS 估计：稳健性与效率的权衡AI 解读

Local projections (LPs) are widely used for estimating impulse responses (IRs) as they are considered more robust to model misspecification than forward-iterated IRs from dynamic models such as VARs. However, this robustness comes at the cost of higher variance, particularly at longer horizons. To mitigate this trade-off, several GLS transformations of LPs have been proposed. This paper analyzes two broad strands of GLS-type LP estimators: those that condition on residuals…

协变量自适应随机化下两阶段实验的推断AI 解读

This paper studies inference in two-stage randomized experiments under covariate-adaptive randomization. In the initial stage of this experimental design, clusters (e.g., households, schools, or graph partitions) are stratified and randomly assigned to control or treatment groups based on cluster-level covariates. Subsequently, an independent second-stage design is carried out, wherein units within each treated cluster are further stratified and randomly assigned to either…

通过筛方法高效估计结构模型AI 解读

We propose a class of sieve-based efficient estimators for structural models (SEES), which approximate the solution using a linear combination of basis functions and impose equilibrium conditions as a penalty to determine the best-fitting coefficients. Our estimators circumvent repeated solution of the structural model, apply to a broad class of models, and are consistent, asymptotically normal, and asymptotically efficient. Moreover, they solve unconstrained optimization…

高维控制变量下条件平均处理效应的双重稳健推断AI 解读

Plausible identification of conditional average treatment effects (CATEs) can rely on controlling for a large number of variables to account for confounding factors. In these high-dimensional settings, estimation of the CATE requires estimating first-stage models whose consistency relies on correctly specifying their parametric forms. While doubly-robust estimators of the CATE exist, inference procedures based on the second-stage CATE estimator are not doubly-robust. Using…

广义共同相关效应估计AI 解读

The widely-used common correlated effects (CCE) estimator, pioneered by Pesaran (2006), is computed using least squares applied to auxiliary regressions where the observed regressors are augmented with cross-sectional averages of the dependent variable and regressors. However, the CCE estimator requires a crucial rank condition and becomes inconsistent when this condition is violated and the factor loadings of the x- and y -equations are correlated, causing an endogeneity…

多向聚类数据下因果函数的估计与推断AI 解读

We propose methods for estimation and uniform inference for a broad class of causal functions, such as conditional average treatment effects and continuous treatment effects, under multi-way clustering. The causal function is identified as the conditional expectation of a Neyman-orthogonal signal that depends on high-dimensional nuisance parameters. We introduce a two-step procedure: the first step uses machine learning to estimate the nuisance parameters, and the second…

学校选择中的非参数处理效应识别AI 解读

This paper studies nonparametric identification and estimation of causal effects in centralized school assignment. In many centralized assignment algorithms, students face both lottery-driven variation and regression discontinuity- (RD) driven variation. We characterize the full set of identified atomic treatment effects (aTEs), defined as the conditional average treatment effect between a pair of schools given student characteristics. Atomic treatment effects are the…