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This paper develops a framework for estimating demand in differentiated product markets with high-dimensional product characteristics, building on the Berry, Levinsohn, and Pakes (1995) model with market-level data; the number of characteristics may exceed the number of markets. First, we propose a Neyman orthogonal estimator in which high-dimensional nuisance parameters are estimated by machine-learning methods such as LASSO, and show that it is $\sqrt{T}$-asymptotically…

不规则相关随机系数面板模型的局部多项式估计AI 解读

We study estimation of the average partial effect (APE) in an irregular correlated random coefficients panel data model in which the time dimension equals the number of random coefficients, $T=p$, and the density function is degenerate. We extend Graham and Powell (2012) in two directions. First, we allow the density of $d_i=\det(X_i)$ to vanish or diverge at the origin at the polynomial rate $|d|^{a}$ with $a>-1$, and show that the aggregate time effects and the APE remain…

Assessing covariate balance is central to causal inference, but commonly used summary measures may miss distributional differences. I develop weighted extensions of the Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and Cramer-von Mises (CVM) statistics and investigate their calibration when weights are externally supplied or estimated from treatment assignment. With exogenous weights, fixed-weight permutation produced approximately nominal Type I error. With estimated…

网络群体中的最优处理分配规则AI 解读

I analyze decision problems where a planner must choose, based on data from an experiment in a pilot population, how to simultaneously distribute a limited number of treatments that generate network spillovers across individuals in a target population. A global treatment rule maps the target population's observed covariates and network features into a jointly feasible allocation. Under local-response, stability, support, and policy-relevant overlap conditions, I identify the…

任务漂移下的保分数持续LassoAI 解读

Continual learning (CL) aims to learn from sequential tasks while mitigating catastrophic forgetting when historical data are no longer accessible. We study continual learning for high-dimensional sparse linear regression, where $\ell_1$ regularization creates a difficulty absent from continual ridge regression: that recursively reusing an $\ell_1$-penalized estimator can propagate earlier shrinkage and accumulate regularization bias across tasks. We propose score-preserving…

趋势一致灰色关联度：无需先验密度的似然闭式小样本推断AI 解读

Grey incidence analysis ranks the factors of a system from short sequences without assuming a probability model, but the grade it returns has no sampling theory: nothing indicates whether an ordering reflects the system or the few data at hand. This paper reduces trend agreement between two sequences to the signs of their centred increments and analyses the resulting counts with Lovegrove's likelinesses, averages of probabilities over an underlying set of distributions that…

接近不可识别时的精确全局极小极大风险AI 解读

We study squared-error estimation when a nuisance parameter is unidentified on a submodel but affects a target that vanishes there. For models with higher-order profile expansions, we give sufficient conditions under which the largest critical-path minimax risk is the exact asymptotic minimax risk over the full parameter space. An efficient observable feature, negligible calibration error and stable recovery yield one estimator attaining the leading constant uniformly, with…

Predictive models trained on observational data often fail to generalise when deployed, especially when the training data is a product of the system being optimised. Recommender systems are a canonical example: they are trained on interaction logs confounded by the deployed policy, past user behaviour, and platform filtering. As a result, the training distribution differs from the candidate distribution scored at serving time, a gap that makes offline metrics unreliable…

通过混合频率状态空间模型进行动态死亡率预测AI 解读

High-frequency death counts are now widely available and contain timely information about intra-year mortality dynamics, but most stochastic mortality models are still estimated on annual data and therefore update only when annual totals are released. We propose a mixed-frequency state-space (MF--SS) extension of the Lee--Carter framework that jointly uses annual mortality rates and monthly death counts. The two series are linked through a shared latent monthly mortality…

Simulated maximum likelihood is widely used in latent-variable models where likelihood evaluation requires intractable integrals or probabilities. With a fixed number of simulation draws, its estimating equation is generally biased. We develop Stochastic Approximation with Unbiased Simulated Scores (SAUSS), combining score simulation with stochastic gradient descent. The Hajivassiliou--McFadden identity, an instance of Fisher's identity, expresses the likelihood score as the…

用于处理效应估计的预测驱动数据融合AI 解读

Randomized controlled trials (RCTs) identify treatment effects without confounding but are often small, whereas observational studies (OBS) are large but may be confounded. Many estimators combining a small RCT with a large OBS have been developed for the average treatment effect (ATE) and the conditional ATE (CATE). However, existing ATE estimators either make assumptions on the OBS or do not borrow enough power from them. The CATE has been studied less than the ATE.…

利用异方差神经网络学习双向因果交互AI 解读

Estimating contemporaneous bidirectional interactions from observational data is difficult because each outcome is endogenous to the other, while flexible regressions may capture only reduced-form dependence. This paper proposes SEM-DNN, a heteroscedastic neural simultaneous-equation estimator that learns reciprocal structural interactions without external instruments. Identification exploits conditional covariance diagonalization: when structural shocks have zero…

因果推断中回归调整估计量的高阶渐近性质AI 解读

Regression adjustment is widely used to improve the precision of average treatment effect estimates from randomized controlled trials. Interacted regression weakly dominates simple regression in terms of its first-order behavior but, in 50-50 randomized trials, the asymptotic variance of both methods is the same and so relative performance is determined by higher-order behavior. Here, we conduct a higher order analysis to compare simple vs. interacted regression adjustments…

用样条混合动态条件密度拟合具有持续尾部强度的横截面收益AI 解读

We propose dynamic conditional densities with spline mixtures (DCD-SM) to detect whether the tail intensity of financial returns depends on past information after accounting for volatility and tail-order dynamics. The target is a weighted mixture of the conditional return densities of individual assets in a cross section. Individual distributions may differ, and returns within a date may be dependent. A normalized spline-mixture correction uses score-driven updates to change…

平行趋势作为一个谱系：基于L矩的分布双重差分AI 解读

Difference-in-differences (DiD) designs are typically used to identify and estimate average causal effects, even though treatment can affect the entire outcome distribution. This has led to a growing interest in distributional DiD methods. However, identifying the full counterfactual distribution requires a parallel trends assumption on the entire distributional dynamics, which can be too strong in practice. We address this problem by introducing a spectrum of identifying…

面向目标人群推广试验结论的贝叶斯倾向得分预测推断AI 解读

The CODIACS Vanguard randomized trial compared a depression care intervention with usual care for patients with persistent depressive symptoms following acute coronary syndrome. We assess whether its findings can be extended to broader patient populations represented by two cohorts constructed from the All of Us electronic health records. This extension is challenging when treatment effects vary across covariates whose distributions differ between trial and target…

高维计算机模拟的贝叶斯代理模型综述AI 解读

Bayesian surrogate models play a central role in the analysis of computationally expensive physics-based simulators, particularly when uncertainty quantification (UQ) is a primary objective. This review provides a high-level overview of commonly used Bayesian surrogate modeling methods for high-dimensional computer simulations, with emphasis on both methodological foundations and practical applications. We review the advantages of Bayesian surrogates, the challenges posed by…

波动率校正预测区间：面向概率时间序列预测的局部自适应不确定性量化AI 解读

Prediction intervals from probabilistic time-series forecasters are commonly assessed by marginal coverage, the long-run proportion of observations falling inside the intervals. However, forecast errors in time series are often serially dependent and heteroskedastic, so prediction intervals can attain near-nominal marginal coverage while miscovering systematically during periods of larger local error variance. We identify and address this failure mode, which we call…

谱自监督学习是否为密度比估计的极小极大最优方法？AI 解读

Spectral self-supervised learning is a method of density ratio estimation for the setting in which only samples drawn from the joint density of the numerator are available. The method has extensively been studied in terms of the computational properties, such as optimal solutions and connections to representation learning. However, theoretical understanding of its statistical property is still insufficient. In this work, we study three questions, namely, the minimax…

应对因果分析中未检验假设的主观贝叶斯方法AI 解读

Causal analyses using observational data rely on identifying assumptions that cannot be verified from the observed data alone. When such unchecked assumptions fail, effect estimates may be biased and uncertainty may be understated. We propose the Adjusted Model for Unchecked Assumptions (AMUA), a subjective Bayesian framework that propagates uncertainty about the joint validity of key causal assumptions through a two-component posterior mixture. One component corresponds to…

利用中介变量更有效地迁移因果效应AI 解读

We develop flexible semiparametric estimators of the average treatment effect transported to a target population that leverage mediators to achieve identification, and prior knowledge of population heterogeneity or summary-level statistics to achieve improved efficiency. We first propose a one-step semiparametric estimator that assumes knowledge of the baseline covariates driving the heterogeneity across populations. This approach improves efficiency by transporting only…

交错采纳面板中的因果推断：正确的比较集取决于干预类型AI 解读

A powerful data structure for causal inference is staggered adoption: many units are observed over many time periods, during which some units adopt an irreversible treatment at various time points while others remain untreated. Popular methods that apply in this setting include difference in difference, fixed effects, matching, and synthetic control. All of these methods compare the future outcomes of treatment adopters and non-adopters to answer the question: for units who…

未知网络结构下短面板同伴效应的检验AI 解读

We propose an instrument-based test for peer effects in short-T panels when the underlying network is unknown. The test avoids estimating the network by aggregating pairwise comparisons between covariate-projected outcomes of potential peers and residualized own outcomes. The statistic is designed for fixed-T, large-N settings with individual fixed effects, heterogeneous slopes, heteroskedasticity, and serial dependence within units. We also develop a weighted extension that…

This paper develops a general two-point dependent wild bootstrap (DWB) for weakly dependent estimating equations. Its key feature is that the two-point marginal distribution and the latent serial dependence specification can be chosen separately. The construction combines a normalized two-point distribution with a stationary latent Gaussian process via a Gaussian copula transformation, includes dependent Rademacher and Mammen multipliers, and nests the classical iid…