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工具变量相关随机系数模型的识别AI 解读

We study identification and estimation of the average partial effect in an instrumental variable correlated random coefficients model with continuously distributed endogenous regressors. This model allows treatment effects to be correlated with the level of treatment. The main result shows that the average partial effect is identified by averaging coefficients obtained from a collection of ordinary linear regressions that condition on different realizations of a control…

利用不变预测进行因果推断：识别与置信区间AI 解读

Summary What is the difference between a prediction that is made with a causal model and that with a non-causal model? Suppose that we intervene on the predictor variables or change the whole environment. The predictions from a causal model will in general work as well under interventions as for observational data. In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on variables. Here, we propose to exploit this invariance…

无处理无混杂性条件下有序处理模型的平均处理效应识别AI 解读

We show identification of the Average Treatment Effect (ATE) when treatment is specified by ordered choice in cross section or panel models. Treatment is determined by location of a latent variable (containing a continuous instrument) relative to two or more thresholds. We place no functional form restrictions on latent errors and potential outcomes. Unconfoundedness of treatment does not hold and identification at infinity for the treated is not possible. Yet we still show…

有序行动博弈的估计：连锁店进入的应用AI 解读

We study the estimation of static games where players are allowed to have ordered actions, such as the number of stores to enter into a market. Assuming that payoff functions satisfy general shape restrictions, we show that equilibrium of the game implies a covariance restriction between each player's action and a component of the player's payoff function that we call the strategic index. The strategic index captures the direction of strategic interaction (i.e., patterns of…

稳健非正规极大似然：调优、计算及与其他稳健高斯聚类方法的比较AI 解读

The two main topics of this article are the introduction of the “optimally tuned robust improper maximum likelihood estimator” (OTRIMLE) for robust clustering based on the multivariate Gaussian model for clusters, and a comprehensive simulation study comparing the OTRIMLE to maximum likelihood in Gaussian mixtures with and without noise component, mixtures of t-distributions, and the TCLUST approach for trimmed clustering. The OTRIMLE uses an improper constant density for…

非可忽略缺失下正态及正态混合模型的可识别性AI 解读

Missing data problems arise in many applied research studies. They may jeopardize statistical inference of the model of interest, if the missing mechanism is nonignorable, that is, the missing mechanism depends on the missing values themselves even conditional on the observed data. With a nonignorable missing mechanism, the model of interest is often not identifiable without imposing further assumptions. We find that even if the missing mechanism has a known parametric form,…

含时变内生空间权重、共同因子与随机系数的空间面板自回归模型：贝叶斯分析AI 解读

This article examines spatial panel autoregressive (SAR) models with dynamic, time-varying endogenous spatial weights matrices, common factors, and random coefficients. An empirical application is on the spillover effects of state Medicaid spending. Endogeneity of spatial weights matrices comes from the correlation of “economic distance” and the disturbances in the SAR equation. Common factors control for common shocks to all states and random coefficients may capture…

高维面板模型中的推断：枪支管制应用AI 解读

We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high-dimensional setting. The setting allows the number of time-varying regressors to be larger than the sample size. To make informative estimation and inference feasible, we require that the overall contribution of the time-varying variables after eliminating the individual specific heterogeneity can be captured by a relatively small number of the…

因子增广向量自回归模型的估计与推断AI 解读

The factor-augmented vector autoregressive (FAVAR) model is now widely used in macroeconomics and finance. In this model, observable and unobservable factors jointly follow a vector autoregressive process, which further drives the comovement of a large number of observable variables. We study the identification restrictions for FAVAR models, and propose a likelihood-based two-step method to estimate the model. The estimation explicitly accounts for factors being partially…

计量经济模型中的混合分布：简要综述与新结果AI 解读

This paper is concerned with applications of mixture models in econometrics. Focused attention is given to semiparametric and nonparametric models that incorporate mixture distributions, where important issues about model specifications arise. For example, there is a significant difference between a finite mixture and a continuous mixture in terms of model identifiability. Likewise, the dimension of the latent mixing variables is a critical issue, in particular when a…

短时间维度下内生空间权重动态面板模型的工具变量估计AI 解读

The spatial dynamic panel data (SDPD) model is a standard tool for analysing data with both spatial correlation and dynamic dependences among economic units. Conventional estimation methods rely on the key assumption that the spatial weight matrix is exogenous, which would likely be violated in some empirical applications where spatial weights are determined by economic factors. In this paper, we propose an SDPD model with individual fixed effects in a short time dimension,…

含生成协变量的半参数估计AI 解读

We study a general class of semiparametric estimators when the infinite-dimensional nuisance parameters include a conditional expectation function that has been estimated nonparametrically using generated covariates. Such estimators are used frequently to e.g., estimate nonlinear models with endogenous covariates when identification is achieved using control variable techniques. We study the asymptotic properties of estimators in this class, which is a nonstandard problem…

意识形态型选民的偏好分布识别与估计AI 解读

This article studies the non-parametric identification and estimation of voters’ preferences when voters are ideological. We establish that voter preference distributions and other parameters of interest can be identified from aggregate electoral data. We also show that these objects can be consistently estimated and illustrate our analysis by performing an actual estimation using data from the 1999 European Parliament elections.

双成分混合模型的估计及其多重检验应用AI 解读

Summary We consider a two-component mixture model with one known component. We develop methods for estimating the mixing proportion and the unknown distribution non-parametrically, given independent and identically distributed data from the mixture model, using ideas from shape-restricted function estimation. We establish the consistency of our estimators. We find the rate of convergence and asymptotic limit of the estimator for the mixing proportion. Completely automated…

利用多项试验识别主层因果效应并评价替代终点AI 解读

Summary Principal stratification is a causal framework to analyse randomized experiments with a post-treatment variable between the treatment and end point variables. Because the principal strata defined by the potential outcomes of the post-treatment variable are not observable, we generally cannot identify the causal effects within principal strata. Motivated by a real data set of phase III adjuvant colon cancer clinical trials, we propose approaches to identifying and…

条件矩不等式的多尺度自适应推断AI 解读

This paper considers inference for conditional moment inequality models using a multiscale statistic. We derive the asymptotic distribution of this test statistic and use the result to propose feasible critical values that have a simple analytic formula, and to prove the asymptotic validity of a modified bootstrap procedure. The asymptotic distribution is extreme value, and the proof uses new techniques to overcome several technical obstacles. The test detects local…

内生删失面板数据模型的识别AI 解读

We study inference on parameters in linear panel data models when outcomes are censored. We allow the censoring to depend on both observable and unobservable variables in arbitrary ways. Generally, these models are set identified and the main contribution of this paper is to derive and characterize the identified sets under general conditions. Our main characterization theorems show that every parameter in the sharp set–and only those parameters–can generate the observed…

Stein 协方差估计量的理论研究AI 解读

Stein proposed an estimator to address the poor performance of the sample covariance matrix for samples of small size. The estimator does not impose sparsity conditions and uses an isotonizing algorithm to preserve the order of the sample eigenvalues. Despite its superior numerical performance, its theoretical properties are not well understood. We demonstrate that Stein's covariance estimator gives modest risk reductions when it is not isotonized, and when it is isotonized…

无限方差线性过程的筛方法推断AI 解读

We extend the available asymptotic theory for autoregressive sieve estimators to cover the case of stationary and invertible linear processes driven by independent identically distributed (i.i.d.) infinite variance (IV) innovations. We show that the ordinary least squares sieve estimates, together with estimates of the impulse responses derived from these, obtained from an autoregression whose order is an increasing function of the sample size, are consistent and exhibit…

单频或混频数据下 VARMA 模型的扩展 Yule–Walker 识别AI 解读

Chen and Zadrozny (1998) developed the linear extended Yule–Walker (XYW) method for determining the parameters of a vector autoregressive (VAR) model with available covariances of mixed-frequency observations on the variables of the model. If the parameters are determined uniquely for available population covariances, then, the VAR model is identified. The present paper extends the original XYW method to an extended XYW method for determining all ARMA parameters of a vector…

美国政治腐败与企业财务政策AI 解读

Using US Department of Justice data on local political corruption, I find that firms in more corrupt areas hold less cash and have greater leverage than firms in less corrupt areas. The results are robust to including a range of controls and to using an instrumental variable approach, two alternative survey measures of corruption, and propensity score matching. Further, the association between corruption and leverage is largest among firms that operate primarily around their…

多元自回归系统与混频数据：一般参数可识别性和估计AI 解读

This paper is concerned with the problem of identifiability of the parameters of a high frequency multivariate autoregressive model from mixed frequency time series data. We demonstrate identifiability for generic parameter values using the population second moments of the observations. In addition we display a constructive algorithm for the parameter values and establish the continuity of the mapping attaching the high frequency parameters to these population second…

工具变量回归中基于因子的识别稳健推断AI 解读

Summary Robust methods for instrumental variable inference have received considerable attention recently. Their analysis has raised a variety of problematic issues such as size/power trade‐offs resulting from weak or many instruments. We show that information reduction methods provide a useful and practical solution to this and related problems. Formally, we propose factor‐based modifications to three popular weak‐instrument‐robust statistics, and illustrate their validity…

双边市场的半参数分析：美国地方日报应用AI 解读

Summary This paper considers an empirical semiparametric model for two‐sided markets. Contrary to existing empirical literature on two‐sided markets, we specify network effects and probability distribution functions of net benefits of the two sides nonparametrically. We then estimate the model by nonparametric instrumental variables regression for local daily newspapers from the USA. We show that semiparametric specification is supported by the data and the network effects…