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矩阵秩的改进推断AI 解读

This paper develops a general framework for conducting inference on the rank of an unknown matrix Π 0 . A defining feature of our setup is the null hypothesis of the form H 0 : rank ( Π 0 ) ≤ r . The problem is of first‐order importance because the previous literature focuses on H 0 ′ : rank ( Π 0 ) = r by implicitly assuming away rank ( Π 0 ) < r , which may lead to invalid rank tests due to overrejections. In particular, we show that limiting distributions of test…

收入动态中经典永久—暂时模型的半参数估计AI 解读

This paper presents identification and estimation results for a flexible state space model. Our modification of the canonical model allows the permanent component to follow a unit root process and the transitory component to follow a semiparametric model of a higher‐order autoregressive‐moving‐average (ARMA) process. Using panel data of observed earnings, we establish identification of the nonparametric joint distributions for each of the permanent and transitory components…

雅可比矩阵近乎奇异时的估计与推断AI 解读

This paper develops extremum estimation and inference results for nonlinear models with very general forms of potential identification failure when the source of this identification failure is known. We examine models that may have a general deficient rank Jacobian in certain parts of the parameter space. When identification fails in one of these models, it becomes underidentified and the identification status of individual parameters is not generally straightforward to…

动态单位需求模型中的动态选择与搜寻成本分布界限AI 解读

This paper develops a dynamic model of consumer search that, despite placing very little structure on the dynamic problem faced by consumers, allows us to exploit intertemporal variation in price distributions to estimate the distribution from which consumer search costs are initially drawn. We show that static approaches to estimating this distribution may suffer from dynamic sample selection bias. This can happen if consumers are forward‐looking and delay their purchases…

线性工具变量回归中更有功效的子向量 Anderson–Rubin 检验AI 解读

We study subvector inference in the linear instrumental variables model assuming homoskedasticity but allowing for weak instruments. The subvector Anderson and Rubin (1949) test that uses chi square critical values with degrees of freedom reduced by the number of parameters not under test, proposed by Guggenberger, Kleibergen, Mavroeidis, and Chen (2012), controls size but is generally conservative. We propose a conditional subvector Anderson and Rubin test that uses…

利用未观测异质性的二元代理识别内生非可分模型AI 解读

In this paper, I study identification of a nonseparable model with endogeneity arising due to unobserved heterogeneity. Identification relies on the availability of binary proxies that can be used to control for the unobserved heterogeneity. I show that the model is identified in the limit as the number of proxies increases. The argument does not require an instrumental variable that is excluded from the outcome equation nor does it require the support of the unobserved…

结构脉冲响应函数的贝叶斯推断AI 解读

I propose to estimate structural impulse responses from macroeconomic time series by doing Bayesian inference on the Structural Vector Moving Average representation of the data. This approach has two advantages over Structural Vector Autoregressions. First, it imposes prior information directly on the impulse responses in a flexible and transparent manner. Second, it can handle noninvertible impulse response functions, which are often encountered in applications. Rapid…

通过扩展子分布实现部分识别AI 解读

I show that sharp identified sets in a large class of econometric models can be characterized by solving linear systems of equations. These linear systems determine whether, for a given value of a parameter of interest, there exists an admissible joint distribution of unobservables that can generate the distribution of the observed variables. The joint distribution of unobservables is not required to satisfy any parametric restrictions, but can (if desired) be assumed to…

中介分析中的主层可忽略性：贯通并超越序贯可忽略性AI 解读

In causal mediation analysis, the definitions of the natural direct and indirect effects involve potential outcomes that can never be observed, so-called a priori counterfactuals. This conceptual challenge translates into issues in identification, which requires strong and often unverifiable assumptions, including sequential ignorability. Alternatively, we can deal with post-treatment variables using the principal stratification framework, where causal effects are defined as…

变平面Cox模型AI 解读

We propose a projection pursuit technique in survival analysis for finding lower-dimensional projections that exhibit differentiated survival outcome. This idea is formally introduced as the change-plane Cox model, a non-regular Cox model with a change-plane in the covariate space dividing the population into two subgroups whose hazards are proportional. The proposed technique offers a potential framework for principled subgroup discovery. Estimation of the change-plane is…

利用未观测混杂因素的代理变量识别因果效应AI 解读

We consider a causal effect that is confounded by an unobserved variable, but with observed proxy variables of the confounder. We show that, with at least two independent proxy variables satisfying a certain rank condition, the causal effect is nonparametrically identified, even if the measurement error mechanism, i.e., the conditional distribution of the proxies given the confounder, may not be identified. Our result generalizes the identification strategy of Kuroki & Pearl…

部分线性结构方程模型中的因果推断AI 解读

We consider identifiability of partially linear additive structural equation models with Gaussian noise (PLSEMs) and estimation of distributionally equivalent models to a given PLSEM. Thereby, we also include robustness results for errors in the neighborhood of Gaussian distributions. Existing identifiability results in the framework of additive SEMs with Gaussian noise are limited to linear and nonlinear SEMs, which can be considered as special cases of PLSEMs with…

有限混合估计的强可识别性与最优极小极大速率AI 解读

We study the rates of estimation of finite mixing distributions, that is, the parameters of the mixture. We prove that under some regularity and strong identifiability conditions, around a given mixing distribution with $m_{0}$ components, the optimal local minimax rate of estimation of a mixing distribution with $m$ components is $n^{-1/(4(m-m_{0})+2)}$. This corrects a previous paper by Chen [Ann. Statist. 23 (1995) 221–233]. By contrast, it turns out that there are…

异乡的家？外国需求与伦敦房价AI 解读

Identifying the effects of “flights to safety” on asset prices using pure time-series methods is difficult because crises are infrequent. We develop a new cross-sectional identification approach, motivated by the insight that investors may differ in their “preferred habitats” within a broad asset class. We apply the method to the question of whether foreign capital is responsible for residential real estate price movements in global cities such as London and New York,…

非线性定价的结构分析AI 解读

This paper proposes a new methodology for analyzing nonlinear pricing data. We establish identification of the model primitives with a known tariff and characterize the model restrictions on observables. We propose a quantile-based nonparametric estimator that achieves consistency at the parametric rate. We introduce unobserved product heterogeneity with an unknown tariff and show how our identification and estimation results extend. A Monte Carlo study analyzes the…

时间利用与劳动生产率：睡眠的回报AI 解读

Abstract We investigate how the largest use of time—sleep—affects productivity. Time use data from the United States allow us to test a model in which sleep improves productivity. Consistent with theory, we find sleep is more complementary to home production than to leisure for nonemployed individuals. We then show that later sunset time reduces worker sleep and earnings. After ruling out alternative hypotheses, we implement an instrumental variables specification that…

工作中的机器人AI 解读

We analyze for the first time the economic contributions of modern industrial robots, which are flexible, versatile, and autonomous machines. We use novel panel data on robot adoption within industries in seventeen countries from 1993 to 2007 and new instrumental variables that rely on robots’ comparative advantage in specific tasks. Our findings suggest that increased robot use contributed approximately 0.36 percentage points to annual labor productivity growth, while at…

存在变量测量误差的非参数工具变量回归AI 解读

This paper considers nonparametric instrumental variable regression when the endogenous variable is contaminated with classical measurement error. Existing methods are inconsistent in the presence of measurement error. We propose a wavelet deconvolution estimator for the structural function that modifies the generalized Fourier coefficients of the orthogonal series estimator to take into account the measurement error. We establish the convergence rates of our estimator for…

尾部限制下二元响应模型截距估计量的根号 n 一致性AI 解读

The intercept of the binary response model is not regularly identified (i.e., $\sqrt n$ consistently estimable) when the support of both the special regressor V and the error term ε are the whole real line. The estimator of the intercept potentially has a slower than $\sqrt n$ convergence rate, which can result in a large estimation error in practice. This paper imposes additional tail restrictions which guarantee the regular identification of the intercept and thus the $\sqrt n$…

给定协变量的高维分位数关联网络的错误发现率控制AI 解读

Summary Motivated by gene coexpression pattern analysis, we propose a novel sample quantile contingency (SQUAC) statistic to infer quantile associations conditioning on covariates. It features enhanced flexibility in handling variables with both arbitrary distributions and complex association patterns conditioning on covariates. We first derive its asymptotic null distribution, and then develop a multiple-testing procedure based on the SQUAC statistic to test simultaneously…

高维协变量下的内生性检验AI 解读

Modern, high dimensional data has renewed investigation on instrumental variables (IV) analysis, primarily focusing on estimation of effects of endogenous variables and putting little attention towards specification tests. This paper studies in high dimensions the Durbin–Wu–Hausman (DWH) test, a popular specification test for endogeneity in IV regression. We show, surprisingly, that the DWH test maintains its size in high dimensions, but at an expense of power. We propose a…

条件同方差下子集Anderson–Rubin检验的Bootstrap不一致与Bonferroni尺寸校正AI 解读

We focus on the linear instrumental variable model with two endogenous regressors under conditional homoskedasticity, and study the subset Anderson and Rubin (1949, AR) test when the nuisance structural parameter, the unrestricted slope coefficient of endogenous regressor, may be weakly identified. Weak identification leads to nonstandard null limiting distributions, and alternative to the usual chi-squared critical value is needed. We first investigate the bootstrap…

断点数量未知的大维因子模型估计AI 解读

In this paper we study the estimation of a large dimensional factor model when the factor loadings exhibit an unknown number of changes over time. We propose a novel three-step procedure to detect the breaks if any and then identify their locations. In the first step, we divide the whole time span into subintervals and fit a conventional factor model on each interval. In the second step, we apply the adaptive fused group Lasso to identify intervals containing a break. In the…

删失分位数回归模型的序贯估计AI 解读

In this paper we propose sequential censored quantile regression (SCQR) and sequential instrumental variables censored quantile regression estimators (SIVCQR). We effectively transform the difficult censored quantile regression and censored instrumental variables quantile regression problems into more standard QR and IVQR procedures, consequently, our approaches make the quantile regression techniques for censored data easily accessible to applied researchers. Simulation…