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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…

高斯自回归中充分统计量的有限样本密度及相关检验AI 解读

A first-order Gaussian autoregressive model is considered. The exact finite-sample joint density of the minimal sufficient statistic is derived, for any value of the autoregressive parameter. This allows us to derive explicitly the exact density of the autocorrelation coefficient and its Studentized t-ratio, whose densities were available only in the asymptotic case and not for all values of the parameter and the statistic. This article also demonstrates how to solve a…

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…

部分平稳性下非线性动态面板模型的识别AI 解读

This paper provides a general identification approach for a wide range of nonlinear panel data models, including binary choice, ordered response, and other types of limited dependent variable models. Our approach accommodates dynamic models with any number of lagged dependent variables as well as other types of endogenous covariates. Our identification strategy relies on a partial stationarity condition, which allows for not only an unknown distribution of errors, but also…

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…

高维广义矩阵因子模型的估计与推断AI 解读

This article introduces a nonlinear generalized matrix factor model, moving beyond the linear-Gaussian framework to accommodate a broader class of response models typically handled via logit, probit, Poisson, or Tobit structures. We introduce a novel Lagrange multiplier method carefully tailored to ensure that the penalized likelihood function is locally concave around the true factor and loading parameters. This leads to central limit theorems of the estimated factors and…

利用日内高频数据识别资产价格的温和漂移AI 解读

Asset prices are commonly represented as a drift-diffusion process, wherein the drift component denotes the anticipated return of the asset within some time frame, while the diffusion component accommodates random shocks. The drift component has substantial practical significance but accurate estimation is typically challenging and has met with limited success in the existing literature except over large time spans. This paper explores a comprehensive range of…

大量参与者下的策略性网络形成AI 解读

We derive asymptotic approximations for models of strategic network formation, where limits are taken as the number of nodes (agents) increases to infinity. Our framework assumes a random utility model where agents have heterogeneous tastes over links, and payoffs allow for anonymous and non-anonymous interaction effects, and the observed network is assumed to be pairwise stable. Our main results concern convergence of the link intensity from finite pairwise stable networks…

市场摩擦的多期限检验AI 解读

We test for the presence of market frictions that induce transitory deviations of observed asset prices from the underlying efficient prices. Our test is based on the joint inference of return covariances across multiple horizons. We demonstrate that a small set of horizons suffices to identify a broad spectrum of frictions, both theoretically and practically. Our method works for high- and low-frequency data under different asymptotic regimes. Extensive simulations show our…

外生考虑集与扩展随机效用AI 解读

In a consideration set model, an individual maximizes utility among the considered alternatives. I relate an exogenous consideration set additive random utility model to classic discrete choice and the extended additive random utility model, in which utility can be − ∞ for infeasible alternatives. When observable utility shifters are bounded, all three models are observationally equivalent. Moreover, they have the same counterfactual bounds and welfare formulas given…

缺失数据稳健性：崩溃点分析AI 解读

Missing data is pervasive in econometric applications, and rarely is it plausible that the data are missing (completely) at random. This paper proposes a methodology for studying the robustness of results drawn from incomplete datasets. Selection is measured as the divergence from the distribution of complete observations to the distribution of incomplete observations. The breakdown point is defined as the minimal amount of selection needed to overturn a given result.…

连续处理的数据驱动政策学习AI 解读

This paper studies policy learning for continuous treatments from observational data. Continuous treatments present more significant challenges than discrete ones because population welfare may need nonparametric estimation, and policy space may be infinite-dimensional and may satisfy shape restrictions. We propose to approximate the policy space with a sequence of finite-dimensional spaces and, for any given policy, obtain the empirical welfare by applying the kernel…

带约束的经验福利最大化AI 解读

Empirical Welfare Maximization (EWM) is a framework that can be used to select welfare program eligibility policies based on data. This paper extends EWM by allowing for uncertainty in estimating the budget needed to implement the selected policy, in addition to its welfare. Due to the additional estimation error, I show there exist no rules that achieve the highest welfare possible while satisfying a budget constraint uniformly over a wide range of DGPs. This differs from…