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用分布回归双重差分评估最低工资引入对工资、收入与工时分布的影响AI 解读

ABSTRACT We estimate the causal effects of the introduction of the minimum wage in Germany on the distributions of hourly wages, monthly earnings, and hours worked, combining rich large‐scale data sources. We compare linear and non‐linear versions of distribution regression difference‐in‐differences (DR‐DiD) and also estimate bivariate DR‐DiD. We show that the introduction of the minimum wage in 2015 led to shifts in low hourly wages around the minimum wage threshold…

结构预测分析AI 解读

Abstract This paper shows how the structural representation of a vector autoregressive model can support forecast analysis. We offer a unified framework that formalizes how the structural form of the model can help form a narrative for two key statistics in real-time VAR forecasting: the forecast errors at the outturn of the data, and the consequent revisions of the forecast. We conduct an exercise on the UK, focusing on the inflation surge that started in 2022. We show that…

蝾螈视网膜神经节细胞动态感受野的结构化贝叶斯建模AI 解读

Neurons in the visual system are selective for specific spatial and temporal stimulus features, described by their \emph{receptive field}. Estimating one means a coefficient per pixel per time bin from few trials -- a high-dimensional problem requiring regularization. Sparse regularizers such as the LASSO handle the dimension but select pixels independently at each time point, with nothing to keep the region coherent in space or smooth in time; it can fragment or reorganize…

高维多输出回归的联合稀疏迁移学习AI 解读

Multitask linear models can improve estimation and prediction by exploiting structure shared across responses, bridging taskwise fitting and complete pooling. In many applications, however, the objective is estimation in a data-limited target domain, while data-rich but heterogeneous source domains are available. Borrowing from these sources can improve efficiency but introduce bias. We develop a joint-sparse transfer-learning framework for high-dimensional multi-output…

预测变量相关下基于自适应虚拟变量的可扩展变量选择AI 解读

Reliable high-dimensional variable selection requires scalable error-controlling methods. The Terminating-Random Experiments (T-Rex) selector estimates the false discovery rate (FDR) by aggregating early-terminated forward-selection paths in which predictors compete with synthetic dummies. We address two remaining challenges: i) predictor dependence can bias dummy-predictor competition; ii) computation is wasted on recomputing terms shared across experiments. Building on…

保持收敛率的收缩支撑高斯过程预测AI 解读

Gaussian process prediction is a central tool in spatial statistics, but standard implementations require dense matrix operations that become prohibitive for large datasets. We propose a scale-adjusted compactly supported working correlation for Gaussian process prediction, using the generalized Wendland family with a support radius $φ_n$ that is allowed to decrease with the sample size $n$. Under fixed-domain asymptotics with quasi-uniform designs, a fixed support radius…

两样本孟德尔随机化中更具可信度的点识别假设AI 解读

Two-sample Mendelian randomization (MR) is a widely applied methodology in epidemiology. In two-sample MR, summary data (typically, regression coefficients and standard errors) quantifying the association between multiple genetic variants and the exposure and the outcome are used in an instrumental variable framework aimed at estimating the causal effect of the exposure on the outcome. Most two-sample MR methods were developed under data-generating models where the…

MARCEDES：非高斯条件下连续优化的评分式因果发现AI 解读

We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) with non-Gaussian errors. Motivated by an intentionally misspecified non-Gaussian SEM with all Laplace errors, we first introduce the mean absolute residual risk, defined over the space of all real matrices, and show that, asymptotically, the risk of the true weighted causal DAG matrix is strictly smaller than that of any other…

部分观测生存曲线的动态因子与双重 PCA 模型：短租需求预测AI 解读

This paper develops prediction models for population-level survival curves observed over time and sampled from a heterogeneous mix of populations. We consider a discrete-time setting where each curve is only partially observed and forecasts of the remaining trajectory are needed for downstream decision making. Our approach recasts cross-population heterogeneity into a multivariate sampling model. We propose two forecasting models for partially observed curves: a full factor…

析因多变量贝叶斯因果森林：多处理的异质主效应与交互效应AI 解读

Many studies expose units to several binary treatments at once and record several correlated outcomes, yet analysts usually estimate one treatment's average effect on one outcome at a time -- discarding how treatments interact and how their effects vary across units. We introduce the factorial multivariate Bayesian causal forest, a Bayesian nonparametric model that decomposes the factorial response surface over the treatment lattice into a prognostic sum-of-trees plus one…

似然模型中的正交矩AI 解读

Many models, such as fixed-effect models for panel or network data, are hard to estimate because they feature nuisance parameters that are both numerous and estimated imprecisely. This, in general, causes an incidental-parameter problem in the estimator of the parameters of interest. The problem can be alleviated by working with an estimating equation whose expectation is insensitive to the value of the nuisance parameters. We discuss and contrast three notions of…

基于负二项边际与高斯 Copula 的重复计数结局参照组敏感性分析AI 解读

Reference-based multiple imputation is used in longitudinal clinical trials to assess sensitivity to assumptions about outcomes unobserved after intercurrent events. Most existing methods target continuous outcomes and use multivariate normal working models. Scheduled count outcomes require a model that preserves integer support, skewness, overdispersion, longitudinal dependence, and an exposure-based rate interpretation. We propose visit-specific negative binomial (NB)…

函数单调性的多重检验AI 解读

Instead of having a single "yes" or "no" result from a test of the global null hypothesis that a function is increasing, we propose a multiple testing procedure of the function's increasingness at several points. If the global null is rejected, then multiple testing provides more information about why. If the global null is not rejected, then multiple testing can provide stronger evidence in favor of increasingness, by rejecting null hypotheses that the function is…

测度线性表示模型中识别、计算与推断的对抗方法AI 解读

We develop a framework for identification, computation, and inference in econometric models with a linear-in-measures representation. These models express maintained restrictions as moment conditions linear in the joint probability measure of observed and latent inputs, and map that measure linearly to the distribution of outputs, even with nonlinear outcome equations. We construct an adversarial discrepancy function whose zeros characterize the identified set for structural…

共同相关效应动态面板模型的自助法推断AI 解读

We study recursive-design wild bootstrap inference for dynamic panel data models with unobserved common factors estimated by Common Correlated Effects. In the large N,T setting, the bootstrap reproduces the biased limiting distribution in pure autoregressive models, but fails to capture all bias and factor-estimation variance components in models with additional regressors, particularly under weak exogeneity. We trace this failure to holding regressors fixed across bootstrap…

Google Play 用户评论情绪指数的统计基础：信号融合、收缩、分布验证与动态平滑AI 解读

The decision problem is whether valence among the users who write reviews of an app has changed enough to warrant investigation of a release, outage, or support issue. We propose a statistical specification for that decision, applied to written Google Play reviews collected under a declared locale, language, sort order, retrieval cap, and window rule. That endpoint returns a selected collection rather than a random sample, so the index describes review writers under the…

未观测竞争下的第一价格拍卖非参数识别：密度不连续框架AI 解读

We consider nonparametric identification of independent private value first-price auction models, in which the analyst only observes winning bids. Our benchmark model assumes an exogenous number of bidders N. We show that, if the bidders observe N, the resulting discontinuities in the winning bid density can be used to identify the distribution of N. The private value distribution can be nonparametrically identified in a second step. This extends, under testable…

经过误差修正的组合预测AI 解读

This paper proposes corrected forecast combinations when the original combined forecast errors are serially dependent. Motivated by the classic Bates and Granger (1969) example, we show that combined forecast errors can be strongly autocorrelated and that a simple correction, which adds a fraction of the previous combined error to the next-period combined forecast, can deliver sizable improvements in forecast accuracy, often exceeding the original gains from combining. We…

利用单一代理变量识别因果效应AI 解读

Unobserved confounding is a key challenge when estimating causal effects from a treatment on an outcome. In this work, we assume that we observe a single, potentially multi-dimensional proxy variable of the unobserved confounder and that we know the mechanism that generates the proxy from the confounder. Under an assumption called Single Proxy Identifiability of Causal Effects or simply SPICE, we prove that this error mechanism is complete and causal effects are…

使用预训练嵌入处理非结构化数据的计量经济学AI 解读

Unstructured data, such as images and text, are increasingly used in empirical economics. Since training machine-learning models on unstructured data is costly, economists often use off-the-shelf pre-trained deep learning models developed by computer scientists to extract embeddings, which are then used as covariates in target economic analyses. Despite the popularity of this practice, its theoretical foundations remain limited. There are two main difficulties. First,…

利用算子值核与正则化随机梯度下降学习算子AI 解读

We consider a class of statistical inverse problems involving the estimation of a regression operator from a Polish space to a separable Hilbert space, where the target lies in a vector-valued reproducing kernel Hilbert space induced by an operator-valued kernel. To address the associated ill-posedness, we analyze regularized stochastic gradient descent (SGD) algorithms in both online and finite-horizon settings. The former uses polynomially decaying step sizes and…

基于时延嵌入置信界的拓扑周期性检验AI 解读

Time-delay embedding is a fundamental technique in Topological Data Analysis (TDA) for reconstructing phase-space dynamics of time-series data, where persistent homology can reveal loops associated with periodicity. However, rigorous statistical uncertainty quantification for these features remains underdeveloped. First, we analyze the topology of time-delay embeddings, showing that the embedded trajectory is homotopy equivalent to a circle ($S^1$) for periodic signals and…

基于瞬时波动率的四次变差估计札记AI 解读

We study an estimator of twice the integrated quarticity of a continuous Itô semimartingale, constructed from squared returns and local estimates of spot volatility. For a shrinking estimation window, we establish a functional stable central limit theorem for a bias-corrected version of the estimator. The limiting conditional variance is $56\int_0^t c_s^4\dd s$, where $c$ denotes spot variance. The proof identifies a common martingale approximation for the three components…

样本量依赖核的高维加权与双样本 U 统计量高斯近似AI 解读

We develop Gaussian approximations for high-dimensional vectors formed by second-order $U$-and $V$-statistics whose kernels depend on sample size. To accommodate weighted and two-sample $U$-and $V$-statistics within a unified framework, we establish our results under independent but not identically distributed (i.n.i.d.) sampling. The resulting theorems apply to a broad range of problems, including estimation in linear regression models with many covariates or many-weak…