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含群组效应的高效同伴效应估计AI 解读

We study linear peer effect models where peers interact in groups and individual’s outcomes are linear in the group mean outcome and characteristics. We allow for unobserved random group effects as well as observed fixed group effects. The specification is in part motivated by the moment conditions imposed in Graham (2008). We show that these moment conditions can be cast in terms of a linear random group effects model and that they lead to a class of GMM estimators with…

双重差分的新进展：近期计量文献的综合梳理AI 解读

This paper synthesizes recent advances in the econometrics of difference-in-differences (DiD) and provides concrete recommendations for practitioners. We begin by articulating a simple set of “canonical” assumptions under which the econometrics of DiD are well-understood. We then argue that recent advances in DiD methods can be broadly classified as relaxing some components of the canonical DiD setup, with a focus on (i) multiple periods and variation in treatment timing,…

社会互动阈值回归AI 解读

This paper develops a threshold social interaction model which introduces group-specific endogenous effects as well as contextual effects into a conventional spatial Durbin model. We propose a two-step GMM estimator for the threshold and regression parameters, derive asymptotic theory, and provide bootstrap inference. Finally, we assess the performance of our methods using a Monte Carlo simulation and provide an empirical application on the role of peer effects on student…

空间动态面板中潜在群组结构的识别AI 解读

This paper considers the identification of latent group structures in spatial dynamic panels. We follow Lee and Yu (2010) and consider a rich spatial dynamic panel data (SDPD) model with two-way fixed effects. In addition, we also allow for latent panel structures where individuals can be classified into a few groups such that individuals within the same group share the common slope parameters and do not otherwise. Both the number of groups and the individuals’ group…

NPIV模型平均导数的有效估计：神经网络估计量的模拟比较AI 解读

Artificial Neural Networks (ANNs) can be viewed as nonlinear sieves that can approximate complex functions of high dimensional variables more effectively than linear sieves. We investigate the performance of various ANNs in nonparametric instrumental variables (NPIV) models of moderately high dimensional covariates that are relevant to empirical economics. We present two efficient procedures for estimation and inference on a weighted average derivative (WAD): an…

第一价格拍卖模型的函数估计方法AI 解读

This paper introduces new methods of identification and estimation of the first-price sealed bid auction model and compares them with the previous existing ones. The first method of estimation allows us to estimate directly (through an iterative algorithm) the cumulative distribution function of the private values without estimating the private values beforehand. In the second method, we use a quantile approach. Although the first-price auction is a complex nonlinear inverse…

Tobit模型的工具变量方法AI 解读

This paper studies models of processes generating censored outcomes with endogenous explanatory variables and instrumental variable restrictions. Tobit-type left censoring at zero is the primary focus in the exposition. Extension to stochastic censoring is sketched. The models do not specify the process determining endogenous explanatory variables and they do not embody restrictions justifying control function approaches. Consequently, they can be partially or point…

存在溢出与不依从的实验中如何识别因果效应AI 解读

This paper shows how to use a randomized saturation experimental design to identify and estimate causal effects in the presence of spillovers–one person’s treatment may affect another’s outcome–and one-sided non-compliance—subjects can only be offered treatment, not compelled to take it up. Two distinct causal effects are of interest in this setting: direct effects quantify how a person’s own treatment changes her outcome, while indirect effects quantify how her peers’…

价值函数的一致推断AI 解读

We propose a method to conduct uniform inference for the (optimal) value function, that is, the function that results from optimizing an objective function marginally over one of its arguments. Marginal optimization is not Hadamard differentiable (that is, compactly differentiable) as a map between the spaces of objective and value functions, which is problematic because standard inference methods for nonlinear maps usually rely on Hadamard differentiability. However, we…

纵向数据惩罚分位数回归的野生 bootstrap 推断AI 解读

The existing theory of penalized quantile regression for longitudinal data has focused primarily on point estimation. In this work, we investigate statistical inference. We propose a wild residual bootstrap procedure and show that it is asymptotically valid for approximating the distribution of the penalized estimator. The model puts no restrictions on individual effects, and the estimator achieves consistency by letting the shrinkage decay in importance asymptotically. The…

含交互固定效应的异质面板二元响应模型AI 解读

In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we build a link between a maximum likelihood estimation and a least squares approach,…

集合识别线性模型的去偏机器学习AI 解读

This paper provides estimation and inference methods for an identified set’s boundary (i.e., support function) where the selection among a very large number of covariates is based on modern regularized tools. I characterize the boundary using a semiparametric moment equation. Combining Neyman-orthogonality and sample splitting ideas, I construct a root-N consistent, uniformly asymptotically Gaussian estimator of the boundary and propose a multiplier bootstrap procedure to…

面板数据潜在群组结构的估计与识别AI 解读

This paper provides a framework for joint estimation and identification of latent group structures in panel data models using a pairwise fusion penalized approach. The latent structure of the model allows individuals to be classified into different groups where the number of groups and the group membership are unknown. The individuals within a group have common slope parameters, while parameter heterogeneity is allowed across the groups. A penalized least squares (PLS)…

面板数据似然中得分偏差与信息偏差的作用AI 解读

We show why reducing information bias can improve the performance of likelihood based estimators and confidence regions in small samples, and why it seems to matter more for inference than for estimation. The insights in this paper are helpful in explaining several simulation findings in the panel data literature. E.g., we can explain the well documented phenomenon that reducing the score bias alone often reduces the finite sample variance of estimators and improves the…

收入冲击中随时间变化的不可观测异质性AI 解读

This paper considers the transitory-permanent model for the earnings process, and allows for time-varying individual-specific unobserved heterogeneity in each shock. The cross-sectional heterogeneity in each shock is drawn from an unknown distribution at each time period. Sufficient conditions for the nonparametric identification of the cross-sectional density functions of the heterogeneity are provided, under different assumptions on the time series behavior of the…

同伴效应与内生社会互动AI 解读

This paper proposes a solution to the problem of endogenous selection of peers in the linear-in-means model. We do not require to specify a model for how the selection of peers comes about. Rather, we exploit two restrictions that are inherent in many such specifications to construct conditional moment conditions. The restrictions in question are that link decisions that involve a given individual are not all independent of one another, but that they are independent of the…

高维时间序列的社群网络自回归AI 解读

Modeling responses on the nodes of a large-scale network is an important task that arises commonly in practice. This paper proposes a community network vector autoregressive (CNAR) model, which utilizes the network structure to characterize the dependence and intra-community homogeneity of the high-dimensional time series. The CNAR model greatly increases the flexibility and generality of the network vector autoregressive (NAR) model proposed by Zhu et al. (2017) by allowing…

拍卖决定合同中的激励效应与选择效应区分AI 解读

This paper develops a novel approach to estimate how contract and principal–agent characteristics influence a post-auction outcome when the matching between agents and principals derives from an auction process. We propose a control-function approach to account jointly for the endogeneity of contracts and matching. This consists of, first, estimating the primitives of an interdependent values auction model – which is shown to be non-parametrically identified from the bidding…

离散工具变量与解释变量下的非参数识别和估计AI 解读

In a nonparametric instrumental regression model in which the regressor and instrument are discretely distributed, we strengthen the conventional moment independence assumption between instrument and residual to include higher order moments. We give conditions under which a function of interest is partially identified when the regressor has more mass points than the instrument. We also show that the function is point identified as long as certain latent parameters…

含时变解释变量的持续时间模型：删失分位数回归两步估计AI 解读

Common duration models are characterized by strong homogeneity and thus are highly restrictive in allowing for how regressors affect the conditional duration distribution. In particular, the implied sign and relative marginal quantile effects remain the same over the entire range of the conditional duration distribution, which rules out general heterogeneous effects in duration data. Quantile regression, which offers a flexible and unified framework that allows for general…

扩展Roy模型的非参数识别与估计AI 解读

We propose a new identification method for the extended Roy model, in which the agents maximize their utility rather than just their outcome. We nonparametrically identify the joint distribution of potential outcomes, which is of great importance in causal inference. We exploit the extended Roy model structure and the monotonicity assumption but do not require any functional form assumption nor any support assumption. The identification is achieved by matching the…

动态面板数据模型的间接推断估计AI 解读

This paper proposes an estimator for higher-order dynamic panel models based on the idea of indirect inference by matching the simple within-group estimator with its analytical approximate expectation. The resulting estimator is shown to be consistent and asymptotically normal. For the special case of first-order dynamic panel, the estimator yields numerically the same result from an existing procedure in the literature, but the inference to follow differs and this paper…

连边误分类下网络形成博弈的识别与推断AI 解读

This paper considers a network formation model when links are potentially misclassified. We focus on a game-theoretical model of strategic network formation with incomplete information, in which the linking decisions depend on agents’ exogenous attributes and endogenous network characteristics. In the presence of link misclassification, we derive moment conditions that characterize the identified set for the preference parameters associated with homophily and network…

股票市场中的新闻隐含联系与局部依赖AI 解读

This paper studies a heterogeneous coefficient spatial factor model that separately addresses both common factor risks (strong cross-sectional dependence) and local dependency (weak cross-sectional dependence) in equity returns. From the asset pricing perspective, we derive the theoretical implications of no asymptotic arbitrage for the heterogeneous spatial factor model, generalizing the work of Kou et al. (2018). We also provide the associated Wald tests for the APT…