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通过原型分析进行非负矩阵分解AI 解读

Hamid Javadia* & Andrea Montanarib*a Department of Electrical and Computer Engineering, Rice University, Houston, TX; b Department of Electrical Engineering and Statistics, Stanford University, Stanford, CAColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Hamid Javadi hrhakim@rice.edu Department of…

贝叶斯推断的几何变分方法AI 解读

We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher–Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphere S∞ in L2, and the Fisher–Rao metric reduces to the standard L2 metric. Exploiting such a Riemannian structure, we formulate the task of approximating the…

两种含内生性的分位数模型比较AI 解读

This article studies the relationship between the two most-used quantile models with endogeneity: the instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen 2005) and the local quantile treatment effects (LQTE) model (Abadie, Angrist, and Imbens 2002). The key condition of the IVQR model is the rank similarity assumption, a restriction on the evolution of individual ranks across treatment states, under which population quantile treatment effects…

条件外生匹配下平均匹配函数的识别与效率界AI 解读

Consider two heterogenous populations of agents who, when matched, jointly produce an output, Y. For example, teachers and classrooms of students together produce achievement, parents raise children, whose life outcomes vary in adulthood, assembly plant managers and workers produce a certain number of cars per month, and lieutenants and their platoons vary in unit effectiveness. Let W∈W={w1,…,wJ}$W\in \mathbb {W}=\lbrace w_{1},\ldots,w_{J}\rbrace $ and X∈X={x1,…,xK}$X\in \mathbb {X}=\lbrace x_{1},\ldots,x_{K}\rbrace $…

识别假设检验的警示：真人秀真的降低了青少年生育率吗？AI 解读

Evaluating policy changes that occur everywhere at the same time is difficult because of the lack of a clear counterfactual. Hoping to address this problem, researchers often proxy for differential exposure using some observed characteristic in the pretreatment period. As a cautionary tale of how difficult identification is in such settings, we re-examine the results of an influential paper by Melissa Kearney and Phillip Levine, who found that the MTV program 16 and Pregnant…

分类未观测混杂下双重负控调整的多重稳健因果推断AI 解读

Summary Unmeasured confounding is a threat to causal inference in observational studies. In recent years, the use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a long-standing tradition in laboratory sciences and epidemiology to rule out non-causal explanations, although they have been used primarily for bias detection. Recently, Miao and colleagues have described sufficient conditions under…

连续函数 Banach 空间中的函数型数据分析AI 解读

Functional data analysis is typically conducted within the $L^{2}$-Hilbert space framework. There is by now a fully developed statistical toolbox allowing for the principled application of the functional data machinery to real-world problems, often based on dimension reduction techniques such as functional principal component analysis. At the same time, there have recently been a number of publications that sidestep dimension reduction steps and focus on a fully functional $L^{2}$…

Archimax Copula 的统计推断AI 解读

Archimax copula models can account for any type of asymptotic dependence between extremes and at the same time capture joint risks at medium levels. An Archimax copula is characterized by two functional parameters: the stable tail dependence function $\ell $, and the Archimedean generator $\psi $ which distorts the extreme-value dependence structure. This article develops semiparametric inference for Archimax copulas: a nonparametric estimator of $\ell $ and a moment-based…

大规模社会网络的多元空间自回归模型AI 解读

The rapid growth of social network platforms generates a large amount of social network data, where multivariate responses are frequently collected from users. To statistically model such type of data, the multivariate spatial autoregressive (MSAR) model is studied. To estimate the model, the quasi maximum likelihood estimator (QMLE) is obtained under certain technical conditions. However, it is found that the computational cost of QMLE is expensive. To fix this problem, a…

群组数过度设定的面板模型识别与估计AI 解读

We propose a simple and fast approach to identify and estimate the unknown group structure in panel models by adapting the M-estimation method. We consider both linear and nonlinear panel models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown to researchers. The main result of the paper is that under certain assumptions, our approach is able to provide uniformly consistent estimation as long…

相依微观结构噪声与高频数据的积分波动率估计AI 解读

In this paper, we develop econometric tools to analyze the integrated volatility (IV) of the efficient price and the dynamic properties of microstructure noise in high-frequency data under general dependent noise. We first develop consistent estimators of the variance and autocovariances of noise using a variant of realized volatility. Next, we employ these estimators to adapt the pre-averaging method and derive consistent estimators of the IV, which converge stably to a…

利用短面板识别动态离散选择模型AI 解读

This paper analyzes the identification of flow payoffs and counterfactual choice probabilities (CCPs) in single-agent dynamic discrete choice models. We develop new results on non-stationary models where the time horizon for the agent extends beyond the length of the data (short panels). We show that counterfactual CCPs in short panels are identified when induced by temporary policy changes affecting payoffs, even though the utility flows are not. Counterfactual CCPs induced…

对代理变量误差稳健的生产函数估计AI 解读

This paper proposes a new approach to the identification and estimation of production functions. It extends the literature on the structural estimation of production functions, which dates back to the seminal work of Olley and Pakes (1996), by relaxing the scalar-unobservable assumption about the proxy variables. The key additional assumption needed in the identification argument is the existence of two conditionally independent proxy variables. The proposed generalized…

连边形成指数模型的非参数识别AI 解读

We consider a dyadic link formation model with a homophily effect index and a degree heterogeneity index. We provide nonparametric identification results for the potentially nonparametric homophily effect function, the realizations of unobserved individual fixed effects and the unknown distribution of idiosyncratic shocks, up to normalization. We propose a novel form of scale normalization on an arbitrary interquantile range, which is not only theoretically general but also…

可容许相似检验的刻画AI 解读

This article studies a classical problem in statistical decision theory: a hypothesis test of a sharp null in the presence of a nuisance parameter. The main contribution of this article is a characterization of two finite-sample properties often deemed reasonable in this environment: admissibility and similarity . Admissibility means that a test cannot be improved uniformly over the parameter space. Similarity requires the null rejection probability to be unaffected by the…

利用鞅矩条件非参数识别混合风险率模型AI 解读

Nonparametric identification of the Mixed Hazard model is shown. The setup allows for covariates that are random, time-varying, satisfy a rich path structure and are censored by events. For each set of model parameters, an observed process is constructed. The process corresponding to the true model parameters is a martingale, the ones corresponding to incorrect model parameters are not. The unique martingale structure yields a family of moment conditions that only the true…

处理效应异质性依赖未观测固定效应时的面板平均处理效应估计AI 解读

Summary This paper proposes a new panel data approach to identify and estimate the time‐varying average treatment effect (ATE). The approach allows for treatment effect heterogeneity that depends on unobserved fixed effects. In the presence of this type of heterogeneity, existing panel data approaches identify the ATE for limited subpopulations only. In contrast, the proposed approach identifies and estimates the ATE for the entire population. The approach relies on the…

二元工具变量模型中的测量误差AI 解读

Summary Instrumental variable methods can identify causal effects even when the treatment and outcome are confounded. We study the problem of imperfect measurements of the binary instrumental variable, treatment and outcome. We first consider nondifferential measurement errors, that is, the mismeasured variable does not depend on other variables given its true value. We show that the measurement error of the instrumental variable does not bias the estimate, that the…

含滞后因变量的离散选择模型的非参数识别AI 解读

Variation in covariates can be used to nonparametrically identify a discrete choice model with a lagged dependent variable and discrete unobserved heterogeneity (Kasahara and Shimotsu, 2009; Browning and Carro, 2014). In some cases the number of support points of the unobserved heterogeneity distribution is restricted only by the number of points of support in the distribution of the covariates. This paper provides conditions under which these models can be identified with…

无原地不变个体的时变非可分面板模型识别与估计AI 解读

This paper explores the identification and estimation of nonseparable panel data models. We show that the structural function is nonparametrically identified when it is strictly increasing in a scalar unobservable variable, the conditional distributions of unobservable variables do not change over time, and the joint support of explanatory variables satisfies some weak assumptions. To identify the target parameters, existing studies assume that the structural function does…

稀疏匹配数据下双向固定效应线性模型的估计与推断AI 解读

Models with multiway fixed effects are frequently used to address selection on unobservables. The data used for estimating these models often contain few observations per value of either indexing variable (sparsely matched data). I show that this sparsity has important implications for inference and propose an asymptotically valid inference method based on subsetting. Sparsity also has important implications for point estimation when covariates or instrumental variables are…

页岩革命与原油价格动态的转变AI 解读

Summary Oil price fluctuates in response to both demand and supply shocks. This paper proposes a new methodology that allows for timely identification of the shifting contribution from the two types of shock through a joint analysis of crude futures options and stock index options. Historical analysis shows that crude oil price movements are dominated by supply shocks from 2004 to 2008, but demand shocks have become much more dominant since then. The large demand shock…

具有固定效应的区间删失回归AI 解读

Summary This paper considers identification and estimation of a fixed‐effects model with an interval‐censored dependent variable. In each time period, the researcher observes the interval (with known endpoints) in which the dependent variable lies but not the value of the dependent variable itself. Two versions of the model are considered: a parametric model with logistic errors and a semiparametric model with errors having an unspecified distribution. In both cases, the…

石油供给冲击对美国经济活动的影响：我们学到了什么？AI 解读

Summary Estimated responses of real oil prices and US gross domestic product (GDP) to oil supply disruptions vary widely. We show that most variation is attributable to differences in identification assumptions and in the model specification. Models that allow for a large short‐run price elasticity of oil supply imply a larger response of oil prices and a larger, longer lived contraction in US real GDP. We find that, if we condition on a range of supply elasticity values…