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内生性检验：一种基于矩的贝叶斯方法AI 解读

A standard assumption in the Bayesian estimation of linear regression models is that the regressors are exogenous in the sense that they are uncorrelated with the model error term. In practice, however, this assumption can be invalid. In this article, using the exponentially tilted empirical likelihood (ETEL) framework, we develop a Bayes factor test for endogeneity that compares a base model that is correctly specified under exogeneity but misspecified under endogeneity…

平均处理效应局部化：合成控制中的投影方法AI 解读

Many real-world policies and business interventions require assessing short-term effects to inform timely decisions, even though most causal inference methods focus on long-term average treatment effects. In this article, we introduce average treatment effect localization (ATEL), which captures localized, short-term policy impacts in panel data settings with a single treated unit and provides early indicators of policy impact. To accommodate both time-varying and nonlinear…

异质性面板数据模型的估计及其在项目评估中的应用AI 解读

This article studies panel data models with two-dimensional unobserved slope heterogeneity and interactive fixed effects. We propose a two-step approach to estimate the parameters in the model. We apply the proposed model to estimate heterogeneous treatment effects at both individual and aggregate levels. Monte Carlo simulations show that the proposed estimators perform remarkably well in finite samples in comparison with some existing methods such as the synthetic control…

半参数广义帕累托模型中的尾部期望分位数估计AI 解读

Expectiles have received increasing attention as a market risk measure that is both coherent and elicitable. They are defined as a least squares analog to quantiles. Their estimation from heavy-tailed loss data in an extreme value framework is an important and difficult problem, especially when the target tail expectile is beyond the range of the data. This problem has been studied only fairly recently, using solely the Weissman extrapolation method. The competing…

存在干扰参数时矩约束的统一推断AI 解读

This article proposes a simple unified inference approach on moment restrictions in the presence of nuisance parameters. The proposed test is constructed based on a new characterization that avoids the estimation of nuisance parameters and can be broadly applied across diverse settings. Under suitable conditions, the test is shown to be asymptotically size controlled and consistent for both independent and dependent samples. Monte Carlo simulations show that the test…

异方差稳健检验统计量尺寸可控的充要条件AI 解读

We revisit size controllability results in Pötscher and Preinerstorfer (2025, Econometric Theory 41, 249–301) concerning heteroskedasticity robust test statistics in regression models. For the special, but important, case of testing a single restriction (e.g., a zero restriction on a single coefficient), we provide a necessary and sufficient condition for size controllability, whereas the condition in Pötscher and Preinerstorfer (2025, Econometric Theory 41, 249–301) is, in…

真实模型包含在候选集中时最小二乘模型平均的渐近最优性AI 解读

Asymptotic optimality is a key theoretical property in model averaging. Due to technical difficulties, existing studies rely on restricted weight sets or on the assumption that there is no true model with fixed dimensions in the candidate set. The focus of this article is to overcome these difficulties by studying a class of least squares model averaging methods with nested candidate models, which includes Mallows model averaging (MMA) of Hansen (2007, Econometrica 75,…

持久时间序列中随机周期的估计AI 解读

A number of economic, financial, and climatic time series exhibit persistent cycles which are characterized by time-dependence patterns and peaks in the spectrum. In this article, we introduce a class of semiparametric cyclical–memory processes which enable the modeling of random cyclical patterns in stationary and non-stationary time series. We develop a theoretical background and asymptotic estimation theory for the frequency of a cycle represented by the location of a…

以分位数为目标的有限样本极小极大遗憾处理规则AI 解读

Consider a setup in which a decision maker is informed about the population by a finite sample and, based on that sample, has to decide whether or not to apply a certain treatment. We work out finite sample minimax regret treatment rules under various sampling schemes when potential outcomes are restricted onto the unit interval. In contrast to Stoye (2009, Journal of Econometrics 151, 70–81), where the focus is on the maximization of expected utility, the focus here is…

部分时期回归量完全缺失的非参数时变面板模型识别与估计AI 解读

This article considers a nonparametric time-varying panel data model in which the primary regressor of interest is completely missing in some periods. Under standard assumptions, identification results are established for the unknown functions and the distributions of the missing regressor. Specifically, one period of completely observed data suffices to identify the random effects model, whereas two periods are required for the fixed effects model. Following identification,…

区间识别集参数推断的功效性质AI 解读

This article studies the power properties of confidence intervals (CIs) for a partially-identified parameter of interest with an interval identified set. We assume that the researcher has bounds estimators needed to construct the CIs proposed by Imbens and Manski (2004), Stoye (2009), and Stoye (2020), denoted by C I α 1 $CI_{\alpha }^{1}$ upper C upper I Subscript alpha Superscript 1 , C I α 2 $CI_{\alpha }^{2}$ upper C upper I Subscript alpha Superscript 2 , C I α 3 $CI_{\alpha }^{3}$…

一般处理模型的高阶去偏估计量AI 解读

We have witnessed tremendous progress in developing the foundation for econometrics and causal inference in the past decades. The most popular paradigm is the classical (first-order) semiparametric theory, in which a key building block is the (first-order) influence function. However, it is well known that estimators based on influence functions can be sub-optimal in various settings. To address this issue, higher-order influence functions (HOIF) are developed. However, most…

函数型时间序列共同趋势的统计推断AI 解读

We study statistical inference on unit roots and cointegration for time series in a Hilbert space. We develop statistical inference on the number of common stochastic trends embedded in the time series, that is, the dimension of the nonstationary subspace. We also consider tests of hypotheses on the nonstationary and stationary subspaces themselves. The Hilbert space can be of an arbitrarily large dimension, and our methods remain asymptotically valid even when the time…

凸成对差分估计量的稳健推断AI 解读

This article develops distribution theory and bootstrap-based inference methods for a broad class of convex pairwise difference estimators. These estimators minimize a kernel-weighted convex-in-parameter function over observation pairs with similar covariates, where the similarity is governed by a localization (bandwidth) parameter. While classical results establish asymptotic normality under restrictive bandwidth conditions, we show that valid Gaussian and bootstrap-based…

部分识别下的稳健贝叶斯处理选择AI 解读

We study a class of binary treatment choice problems with partial identification through the lens of robust (multiple prior) Bayesian analysis. We use a convenient set of prior distributions to derive ex-ante and ex-post robust Bayes decision rules, both for decision makers who can randomize and for decision makers who cannot. Our main messages are as follows: First, ex-ante and ex-post robust Bayes decision rules do not agree in general, whether or not randomized rules are…

局部单位根回归的自加权估计及应用AI 解读

A new self-weighted (SW) least squares (LS) estimation approach is developed for local unit root (LUR) autoregression with conditionally heteroskedastic innovations. Asymptotically controlled trimming of the most extreme observations in LS regression is shown to lead to mixed Gaussian limit theory and studentization produces standard normality free of the unknown LUR parameter. The estimator is super consistent with a convergence rate slightly below the O P ( n ) $O_P(n)$ upp…

非线性模型时变参数的局部多项式估计AI 解读

We develop a novel asymptotic theory for local polynomial extremum estimators of time-varying parameters in a broad class of nonlinear time-series models, including discrete-valued ones. We show that the proposed estimators are asymptotically normally distributed under weak conditions. We also provide a precise characterization of the leading bias term due to smoothing. We demonstrate the usefulness of our general results by establishing primitive conditions for local…

协变量增强的累积和泡沫监测程序AI 解读

We explore how information from covariates can be incorporated into the CUSUM-based real-time monitoring procedure for explosive asset price bubbles developed in Homm and Breitung (2012, Journal of Financial Econometrics 10, 198–231). Where dynamic covariates are present in the data generating process (DGP), the false positive rate (FPR) of the basic CUSUM procedure, which is based on the assumption that prices follow a univariate DGP, under the null of no explosivity will…

时间聚合下线性模型的稳定性AI 解读

Linear factor models dominate the field of empirical asset pricing and are largely considered a successful framework even though testing for model adequacy is uncommon in empirical research. This article develops a probabilistic argument under the assumption that return data are generated by a multivariate Lévy process and shows that linear factor models are asymptotically adequate as the return frequency declines. Rate-of-convergence results are provided assuming finite or…

含议价机制双向拍卖的非参数识别与估计AI 解读

This article studies the nonparametric identification and estimation of double auctions with one buyer and one seller. This model assumes that both bidders submit their own sealed bids, and the transaction price is determined by a weighted average between the submitted bids when the buyer’s offer is higher than the seller’s ask. It captures the bargaining process between two parties. Working within this double auction model, we first establish the nonparametric…

拓展回归模型的适用空间AI 解读

This article introduces and analyzes a framework that accommodates general heterogeneity in regression modeling. It demonstrates that regression models with fixed or time-varying parameters can be estimated using the ordinary least squares (OLS) and time-varying OLS methods, respectively, across a broad class of regressors and noise processes not covered by existing theory. The proposed setting facilitates the development of asymptotic theory and the estimation of robust…

异方差误差线性模型中多个变点的置信区间AI 解读

We consider the problem of estimating and deriving confidence intervals for change points in linear models with heteroscedastic errors. A CUSUM process-based estimator is proposed, and we establish its asymptotic properties when the linear regression model exhibits change points in both the regression parameters and the distribution of the errors. This theory motivates the construction of confidence sets for multiple change points by refining preliminary change point…

通过近似消息传递实现高维 Newey–Powell 检验AI 解读

We propose a high-dimensional extension of the heteroscedasticity test proposed in Newey and Powell (1987). Our test is based on expectile regression in the proportional asymptotic regime where $n/p \to \delta \in (0,1]$ . The asymptotic analysis of the test statistic uses the approximate message passing algorithm, from which we obtain the limiting distribution of the test and establish its asymptotic power. The numerical performance of the test is validated through an…

基于积分条件矩的一致卡方设定检验AI 解读

In spite of the omnibus property of integrated conditional moment (ICM) specification tests, they are not commonly used in empirical practice owing to features such as the non-pivotality of the test and the high computational cost of available bootstrap schemes, especially in large samples. This article proposes specification and mean independence tests based on ICM metrics. The proposed test exhibits consistency, asymptotic $\chi ^2$ -distribution under the null hypothesis,…