论文检索

董事会多样性、企业风险与公司政策AI 解读

We examine the effects of diversity in the board of directors on corporate policies and risk. Using a multidimensional measure, we find that greater board diversity leads to lower volatility and better performance. The lower risk levels are largely due to diverse boards adopting more persistent and less risky financial policies. However, consistent with diversity fostering more efficient (real) risk-taking, firms with greater board diversity also invest persistently more in…

秩相似性检验AI 解读

We introduce a test of the rank invariance or rank similarity assumption common in treatment effects and instrumental variables models. The test probes the implication that the conditional distribution of ranks should be identical across treatment states using a regression-based test statistic. We apply the test to data from the Tennessee STAR class-size reduction experiment and show that systematic slippages in rank can be important statistically and economically.

识别金融传染AI 解读

Summary Identifying contagion effects during periods of financial crisis is known to be complicated by the changing volatility of asset returns during periods of stress. To untangle this we propose a GARCH (generalized autoregressive conditional heteroskedasticity) common features approach, where systemic risk emerges from a common factor source (or indeed multiple factor sources) with contagion evident through possible changes in the factor loadings relating to the common…

多变量选择与社会互动的识别AI 解读

Summary This paper considers the identification of social interaction effects in the context of multivariate choices. First, we generalize the theoretical social interaction model to allow individuals to make interdependent choices in different activities. Based on the theoretical model, we propose a simultaneous equation network model and discuss the identification of social interaction effects in the econometric model. We also provide an empirical example to show the…

博弈中非均衡行为的实证模型AI 解读

This paper studies the identification and estimation of the decision rules that individuals use to determine their actions in games, based on a structural econometric model of non‐equilibrium behavior in games. The model is based primarily on various notions of limited strategic reasoning, allowing multiple modes of strategic reasoning and heterogeneity in strategic reasoning across individuals and within individuals. The paper proposes the model and provides sufficient…

识别、数据合并与信息披露风险AI 解读

It is commonplace that the data needed for econometric inference are not contained in a single source. In this paper we analyze the problem of parametric inference from combined individual‐level data when data combination is based on personal and demographic identifiers such as name, age, or address. Our main question is the identification of the econometric model based on the combined data when the data do not contain exact individual identifiers and no parametric…

以因果模型选择识别犯罪的动态溢出AI 解读

Does crime in a neighborhood cause future crime? Without a source of quasi‐experimental variation in local crime, we develop an identification strategy that leverages a recently developed test of exogeneity (Caetano (2015)) to select a feasible regression model for causal inference. Using a detailed incident‐based data set of all reported crimes in Dallas from 2000 to 2007, we find some evidence of dynamic spillovers within certain types of crimes, but no evidence that…

非参数工具变量回归的最优上确界范数收敛率与非线性泛函一致推断AI 解读

This paper makes several important contributions to the literature about nonparametric instrumental variables (NPIV) estimation and inference on a structural function h0 and functionals of h0. First, we derive sup‐norm convergence rates for computationally simple sieve NPIV (series two‐stage least squares) estimators of h0 and its derivatives. Second, we derive a lower bound that describes the best possible (minimax) sup‐norm rates of estimating h0 and its derivatives, and…

We revisit the classic semi‐parametric problem of inference on a low‐dimensional parameter θ0 in the presence of high‐dimensional nuisance parameters η0. We depart from the classical setting by allowing for η0 to be so high‐dimensional that the traditional assumptions (e.g. Donsker properties) that limit complexity of the parameter space for this object break down. To estimate η0, we consider the use of statistical or machine learning (ML) methods, which are particularly…

短期删失动态面板模型的半参数识别与估计AI 解读

In this paper, we present a semi‐parametric identification and estimation method for censored dynamic panel data models of short time periods and their average partial effects with only two periods of data. The proposed method transforms the semi‐parametric specification of censored dynamic panel data models into a parametric family of distribution functions of observables without specifying the distribution of the initial condition. Then the censored dynamic panel data…

严格外生工具变量下线性回归的简便稳健估计AI 解读

In this paper, I investigate the estimation of linear regression models with strictly exogenous instruments under minimal identifying assumptions. I introduce a uniformly (in the data‐generating process) consistent estimator under nearly minimal identifying assumptions. The proposed estimator, called the integrated instrumental variables (IIV) estimator, is a simple weighted least‐squares estimator. It does not require the choice of a bandwidth or tuning parameter, or the…

高维内生回归变量与工具变量下的稀疏线性模型及 ℓ₁ 正则化两阶段最小二乘AI 解读

We explore the validity of the 2-stage least squares estimator with l1-regularization in both stages, for linear triangular models where the numbers of endogenous regressors in the main equation and instruments in the first-stage equations can exceed the sample size, and the regression coefficients are sufficiently sparse. For this l1-regularized 2-stage least squares estimator, we first establish finite-sample performance bounds and then provide a simple practical method…

对称性限制下样本选择模型的非参数识别与估计AI 解读

Under a conditional mean restriction Das et al. (2003) considered nonparametric estimation of sample selection models. However, their method can only identify the outcome regression function up to a constant. In this paper we strengthen the conditional mean restriction to a symmetry restriction under which selection biases due to selection on unobservables can be eliminated through proper matching of propensity scores; consequently we are able to identify and obtain…

具有过度识别限制的动态结构因子模型估计与推断AI 解读

This paper develops a new estimator for the impulse response functions in structural factor models with a fixed number of over-identifying restrictions. The proposed identification scheme nests the conventional just-identified recursive scheme as a special case. We establish the asymptotic distributions of the new estimator and develop test statistics for the over-identifying restrictions. Simulation results show that adding a few more over-identifying restrictions can lead…

条件矩不等式的自适应检验AI 解读

Many economic models yield conditional moment inequalities that can be used for inference on parameters of these models. In this paper, I construct new tests of parameter hypotheses in conditional moment inequality models based on studentized kernel estimates of moment functions. The tests automatically adapt to the unknown smoothness of the moment functions, have uniformly correct asymptotic size, and are rate-optimal against certain classes of alternatives. Some existing…

具有不可观测状态代理变量的动态离散选择模型闭式识别AI 解读

Proxies for unobserved skills and technologies are increasingly available in empirical data. For dynamic discrete choice models of forward-looking agents where a continuous state variable is unobserved but its proxy is available, we derive closed-form identification of the structure by explicitly solving integral equations. In the first step, we derive closed-form identification of Markov components, including the conditional choice probabilities and the law of state…

相依因子模型中联合分布的识别AI 解读

This paper studies linear factor models that have arbitrarily dependent factors. Assuming that the coefficients are known and that their matrix representation satisfies rank conditions, we identify the nonparametric joint distribution of the unobserved factors using first and then second-order partial derivatives of the log characteristic function of the observed variables. In conjunction with these identification strategies the mean and variance of the vector of factors are…

平均处理效应估计的一般双重稳健性结果AI 解读

In this paper we study doubly robust estimators of various average and quantile treatment effects under unconfoundedness; we also consider an application to a setting with an instrumental variable. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting; and both inverse-probability weighted (IPW) and doubly robust estimators. We also allow for…

异方差误差删失线性回归模型的半参数效率AI 解读

Using a simplified approach developed by Severini and Tripathi (2001), we calculate the semiparametric efficiency bound for the finite-dimensional parameters of censored linear regression models with heteroskedastic errors. Under an additional identification at infinity type assumption, we propose an efficient estimator based on a novel result from Lewbel and Linton (2002). An extension to censored partially linear single-index models is also presented.

偏态模型聚类的变量选择：识别新的睡眠表型AI 解读

In sleep research, applying finite mixture models to sleep characteristics captured 8 through multiple data types, including self-reported sleep diary, a wrist monitor capturing movement (actigraphy), and brain waves (polysomnography), may suggest new phenotypes that reflect underlying disease mechanisms. However, a direct mixture model application is challenging because there are many sleep variables from which to choose, and sleep variables are often highly skewed even in…

《可逆指数模型的简单估计量》讨论AI 解读

This is an interesting article that considers the question of inference on unknown linear index coefficients in a general class of models where reduced form parameters are invertible function of one or more linear index. Interpretable sufficient conditions such as monotonicity and or smoothness for the invertibility condition are provided. The results generalize some work in the previous literature by allowing the number of reduced form parameters to exceed the number of…

可逆指标模型的简易估计量AI 解读

This article considers estimation of the unknown linear index coefficients of a model in which a number of nonparametrically identified reduced form parameters are assumed to be smooth and invertible function of one or more linear indices. The results extend the previous literature by allowing the number of reduced form parameters to exceed the number of indices (i.e., the indices are “overdetermined” by the reduced form parameters. The estimator of the unknown index…

利用解释变量幂变换序贯检验多项式模型假设AI 解读

Summary We provide a methodology for testing a polynomial model hypothesis by generalizing the approach and results of Baek, Cho, and Phillips ( Journal of Econometrics , 2015, 187 , 376–384; BCP), which test for neglected nonlinearity using power transforms of regressors against arbitrary nonlinearity. We use the BCP quasi‐likelihood ratio test and deal with the new multifold identification problem that arises under the null of the polynomial model. The approach leads to…

利用分位数估计福利效应的分布AI 解读

Summary This paper proposes a framework to model welfare effects that are associated with a price change in a population of heterogeneous consumers. The framework is similar to that of Hausman and Newey ( Econometrica , 1995, 63 , 1445–1476), but allows for more general forms of heterogeneity. Individual demands are characterized by a general model that is nonparametric in the regressors, as well as monotonic in unobserved heterogeneity, allowing us to identify the…