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含二元内生解释变量的广义双变量Probit模型识别AI 解读

This paper provides identification results for a class of models specified by a triangular system of two equations with binary endogenous variables. The joint distribution of the latent error terms is specified through a parametric copula structure that satisfies a particular dependence ordering, while the marginal distributions are allowed to be arbitrary but known. This class of models is broad and includes bivariate probit models as a special case. The paper demonstrates…

平滑系数空间自回归模型的半参数估计与检验AI 解读

This paper considers a flexible semiparametric spatial autoregressive (mixed-regressive) model in which unknown coefficients are permitted to be nonparametric functions of some contextual variables to allow for potential nonlinearities and parameter heterogeneity in the spatial relationship. Unlike other semiparametric spatial dependence models, ours permits the spatial autoregressive parameter to meaningfully vary across units and thus allows the identification of a…

非可分工具变量模型的独立性最小距离估计AI 解读

I develop a semiparametric minimum distance from independence estimator for a nonseparable instrumental variables model. An independence condition identifies the model for many types of discrete and continuous instruments. The estimator is taken as the parameter value that most closely satisfies this independence condition. Implementing the estimator requires a quantile regression of the endogenous variables on the instrument, so the procedure is two-step, with a finite or…

董事会改革与企业价值：全球证据AI 解读

We examine the impact of corporate board reforms on firm value in 41 countries. Using a difference-in-differences design, we find that board reforms increase firm value. Reforms involving board and audit committee independence, but not reforms involving separation of chairman and chief executive officer positions, drive the valuation increases. In addition, while comply-or-explain reforms result in a greater increase in firm value than rule-based reforms, the effects of…

金融衍生品的积极作用：期权交易与企业创新AI 解读

Do financial derivatives enhance or impede innovation? We answer this question by examining the relationship between equity options markets and standard measures of firm innovation. We find that firms with more options trading activity generate more patents and patent citations per dollar invested in research and development (R&D), after accounting for other confounding factors. These results are confirmed when we use a propensity score matching procedure and an instrumental…

银行业整合与房价共同波动AI 解读

The correlation in house price growth across US states increased steadily between 1976 and 2000. This paper shows that the contemporaneous geographic integration of the US banking market, via the emergence of large banks, was a primary driver of this phenomenon. To this end, we first theoretically derive an appropriate measure of banking integration across state pairs and show that house price growth correlation is strongly related to this measure of financial integration.…

引力模型的双向效应估计AI 解读

Empirical models for dyadic interactions between n agents often feature agent-specific parameters. Fixed-effect estimators of such models generally have bias of order n−1, which is nonnegligible relative to their standard error. Therefore, confidence sets based on the asymptotic distribution have incorrect coverage. This paper looks at models with multiplicative unobservables and fixed effects. We derive moment conditions that are free of fixed effects and use them to set up…

固定效应有序Logit模型的估计AI 解读

This paper introduces a new estimator for the fixed-effects ordered logit model. The proposed method has two advantages over existing estimators. First, it estimates the differences in the cut points along with the regression coefficient, leading to provide bounds on partial effects. Second, the proposed estimator for the regression coefficient is more efficient. I use the fact that the ordered logit model with J outcomes and T observations can be converted to a binary…

离散—连续动态选择模型的内生网格法：有无偏好冲击的情形AI 解读

We present a fast and accurate computational method for solving and estimating a class of dynamic programming models with discrete and continuous choice variables. The solution method we develop for structural estimation extends the endogenous grid-point method (EGM) to discrete-continuous (DC) problems. Discrete choices can lead to kinks in the value functions and discontinuities in the optimal policy rules, greatly complicating the solution of the model. We show how these…

利用非线性合同的不连续性估计经济主体的价格敏感度AI 解读

This paper proposes a method to estimate price sensitivity of economic agents exploiting discontinuity in nonlinear contracts. As an application, we study contracts between a managed care organization and hospitals for organ transplants. Exploiting donut holes in the reimbursement contracts, we show that the impact of the reimbursement rate on hospitals' provision of health care services varies significantly across patients with different levels of illness severity. Our…

电子拍卖竞价模型的识别与估计AI 解读

Because of discrete bid increments, bidders at electronic auctions engage in shading instead of revealing their valuations, which would occur under the commonly assumed second-price rule. We demonstrate that misspecifying the pricing rule can lead to biased estimates of the latent valuation distribution, and then explore identification and estimation of a model with a correctly specified pricing rule. A further challenge to econometricians is that only a lower bound on the…

预防性储蓄与总需求AI 解读

We construct, and then estimate by maximum likelihood, a tractable dynamic stochastic general equilibrium model with incomplete insurance and heterogenous agents. The key feature of our framework is that cross-sectional heterogeneity remains finite dimensional. The solution to the model thus admits a state-space representation that can be used to recover the distribution of the model's parameters. Household heterogeneity expands the set of observables to cross-sectional…

已知第一阶段符号时的无偏工具变量估计AI 解读

We derive mean-unbiased estimators for the structural parameter in instrumental variables models with a single endogenous regressor where the sign of one or more first-stage coefficients is known. In the case with a single instrument, there is a unique nonrandomized unbiased estimator based on the reduced-form and first-stage regression estimates. For cases with multiple instruments we propose a class of unbiased estimators and show that an estimator within this class is…

容许逆从者？不依赖单调性的局部平均处理效应AI 解读

Instrumental variables (IVs) are commonly used to estimate the effects of some treatments. A valid IV should be as good as randomly assigned, it should not have a direct effect on the outcome, and it should not induce any unit to forgo treatment. This last condition, the so-called monotonicity condition, is often implausible. This paper starts by showing that actually, IVs are still valid under a weaker condition than monotonicity. It then derives conditions that are…

二元内生非可分模型中的反事实映射与个体处理效应AI 解读

This paper establishes nonparametric identification of individual treatment effects in a nonseparable model with a binary endogenous regressor. The outcome variable may be continuous, discrete, or a mixture of both, while the instrumental variable can take binary values. First, we study the case where the model includes a selection equation for the binary endogenous regressor. We establish point identification of the individual treatment effects and the structural function…

一般纵向数据的可忽略性AI 解读

Likelihood factors that can be disregarded for inference are termed ignorable. We demonstrate that close ties exist between ignorability and identification of causal effects by covariate adjustment. A graphical condition, stability, plays a role analogous to that of missingness at random, but is applicable to general longitudinal data. Our formulation of ignorability does not depend on any notion of missing data, so is appealing in situations where missing data may not…

一般结果与混杂情形下工具变量的偏差放大作用AI 解读

Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this unconfoundedness assumption is. This belief has had a huge impact on practical causal inference, suggesting that we should adjust for all…

协变量辅助的谱聚类AI 解读

Biological and social systems consist of myriad interacting units. The interactions can be represented in the form of a graph or network. Measurements of these graphs can reveal the underlying structure of these interactions, which provides insight into the systems that generated the graphs. Moreover, in applications such as connectomics, social networks, and genomics, graph data are accompanied by contextualizing measures on each node. We utilize these node covariates to…

广义分位数预测的偏差校正评分分解AI 解读

Decompositions of the score of a forecast represent useful tools for assessing its performance. We consider local score decompositions permitting detailed forecast assessments across a spectrum of conditions of interest. We derive corrections to the bias of the decomposition components in the framework of point forecasts of quantile-type functionals, and illustrate their performance by simulation. Related bias corrections have thus far only been known for squared error…

基于主层评分的主分层分析AI 解读

Summary Practitioners are interested in not only the average causal effect of a treatment on the outcome but also the underlying causal mechanism in the presence of an intermediate variable between the treatment and outcome. However, in many cases we cannot randomize the intermediate variable, resulting in sample selection problems even in randomized experiments. Therefore, we view randomized experiments with intermediate variables as semiobservational studies. In parallel…

死亡截断下有序处理与二元结局的因果分析AI 解读

Summary It is common that, in multiarm randomized trials, the outcome of interest is ‘truncated by death’, meaning that it is only observed or well-defined conditioning on an intermediate outcome. In this case, in addition to pairwise contrasts, the joint inference for all treatment arms is also of interest. Under a monotonicity assumption we present methods for both pairwise and joint causal analyses of ordinal treatments and binary outcomes in the presence of truncation by…

高维重复测量数据的协方差结构检验AI 解读

In regression analysis with repeated measurements, such as longitudinal data and panel data, structured covariance matrices characterized by a small number of parameters have been widely used and play an important role in parameter estimation and statistical inference. To assess the adequacy of a specified covariance structure, one often adopts the classical likelihood-ratio test when the dimension of the repeated measurements ($p$) is smaller than the sample size ($n$).…

网络向量自回归AI 解读

We consider here a large-scale social network with a continuous response observed for each node at equally spaced time points. The responses from different nodes constitute an ultra-high dimensional vector, whose time series dynamic is to be investigated. In addition, the network structure is also taken into consideration, for which we propose a network vector autoregressive (NAR) model. The NAR model assumes each node’s response at a given time point as a linear combination…

二元面板模型中充分性条件的一则札记AI 解读

Consider estimating the slope coefficients of a fixed‐effect binary‐choice model from two‐period panel data. Two approaches to semiparametric estimation at the regular parametric rate have been proposed: one is based on a sufficiency requirement, and the other is based on a conditional‐median restriction. We show that, under standard assumptions, both conditions are equivalent.