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截断回归模型的非参数识别与估计AI 解读

In this paper, we consider non-parametric identification and estimation of truncated regression models in both cross-sectional and panel data settings. For the cross-sectional case, Lewbel and Linton (2002) considered non-parametric identification and estimation through continuous variation under a log-concavity condition on the error distribution. We obtain non-parametric identification under weaker conditions. In particular, we obtain non-parametric identification through…

面板 Probit 模型中结构参数与边际效应的固定效应估计AI 解读

Fixed effects estimators of nonlinear panel models can be severely biased due to the incidental parameters problem. In this paper, I characterize the leading term of a large-T expansion of the bias of the MLE and estimators of average marginal effects in parametric fixed effects panel binary choice models. For probit index coefficients, the former term is proportional to the true value of the coefficients being estimated. This result allows me to derive a lower bound for the…

基尼系数的可靠推断AI 解读

Although attention has been given to obtaining reliable standard errors for the plug-in estimator of the Gini index, all standard errors suggested until now are either complicated or quite unreliable. An approximation is derived for the estimator by which it is expressed as a sum of IID random variables. This approximation allows us to develop a reliable standard error that is simple to compute. A simple but effective bias correction is also derived. The quality of inference…

不平等测度的 Edgeworth 展开与正态化变换AI 解读

Finite sample distributions of studentized inequality measures differ substantially from their asymptotic normal distribution in terms of location and skewness. We study these aspects formally by deriving the second-order expansion of the first and third cumulant of the studentized inequality measure. We state distribution-free expressions for the bias and skewness coefficients. In the second part we improve over first-order theory by deriving Edgeworth expansions and…

通过社会网络识别同伴效应AI 解读

We provide new results regarding the identification of peer effects. We consider an extended version of the linear-in-means model where interactions are structured through a social network. We assume that correlated unobservables are either absent, or treated as network fixed effects. We provide easy-to-check necessary and sufficient conditions for identification. We show that endogenous and exogenous effects are generally identified under network interaction, although…

追随德国：利用国际货币联系估计货币政策的经济效应AI 解读

Forward-looking behavior on the part of the monetary authority makes it difficult to estimate the effect of monetary policy interventions on output. We present instrumental variables estimates of the impact of interest rates on quarterly real output for several European countries, using German interest rates as the instrument. These estimates confirm a strong forward-looking bias in least squares estimates that persists even conditional on standard controls for the history…

同方差与异方差过度识别线性模型的改进 JIVE 估计量AI 解读

We introduce two simple new variants of the jackknife instrumental variables (JIVE) estimator for overidentified linear models and show that they are superior to the existing JIVE estimator, significantly improving on its small-sample-bias properties. We also compare our new estimators to existing Nagar (1959) type estimators. We show that, in models with heteroskedasticity, our estimators have superior properties to both the Nagar estimator and the related B2SLS estimator…

双色随机强化 urn 生成的反应自适应设计：渐近性质AI 解读

This paper illustrates asymptotic properties for a response-adaptive design generated by a two-color, randomly reinforced urn model. The design considered is optimal in the sense that it assigns patients to the best treatment, with probability converging to one. An approach to show the joint asymptotic normality of the estimators of the mean responses to the treatments is provided in spite of the fact that allocation proportions converge to zero and one. Results on the rate…

纵向研究中删失分位数回归模型的推断AI 解读

We develop inference procedures for longitudinal data where some of the measurements are censored by fixed constants. We consider a semi-parametric quantile regression model that makes no distributional assumptions. Our research is motivated by the lack of proper inference procedures for data from biomedical studies where measurements are censored due to a fixed quantification limit. In such studies the focus is often on testing hypotheses about treatment equality. To this…

用局部工具变量估计潜在结果分布：大学入学与工资不平等应用AI 解读

This paper extends the method of local instrumental variables developed by Heckman and Vytlacil [Heckman, J., Vytlacil E., 2005. Structural equations, treatment, effects and econometric policy evaluation. Econometrica 73(3), 669-738] to the estimation of not only means, but also distributions of potential outcomes. The newly developed method is illustrated by applying it to changes in college enrollment and wage inequality using data from the National Longitudinal Survey of…

使用聚合数据的随机系数Logit模型贝叶斯分析AI 解读

We present a Bayesian approach for analyzing aggregate level sales data in a market with differentiated products. We consider the aggregate share model proposed by Berry et al. [Berry, Steven, Levinsohn, James, Pakes, Ariel, 1995. Automobile prices in market equilibrium. Econometrica. 63 (4), 841-890], which introduces a common demand shock into an aggregated random coefficient logit model. A full likelihood approach is possible with a specification of the distribution of…

形状约束下的半参数二元回归及印度入学数据应用AI 解读

We consider estimation of the regression function in a semiparametric binary regression model defined through an appropriate link function (with emphasis on the logistic link) using likelihood-ratio based inversion. The dichotomous response variable [Delta] is influenced by a set of covariates that can be partitioned as (X,Z) where Z (real valued) is the covariate of primary interest and X (vector valued) denotes a set of control variables. For any fixed X, the conditional…

构建贝叶斯最优选择实验设计的高效算法AI 解读

While Bayesian - and -optimal designs for the multinomial logit model have been shown to have better predictive performance than Bayesian - and -optimal designs, the algorithms for generating them have been too slow for commercial use. In this article, we present a much faster algorithm for generating Bayesian optimal designs for all four criteria while simultaneously improving the statistical efficiency of the designs. We also show how to augment a choice design allowing…

德国失业补偿制度改革：基于行政登记数据的非参数界限分析AI 解读

Economic theory suggests that an extension of the maximum length of entitlement for unemployment benefits increases the duration of unemployment. Empirical results for the reform of the unemployment compensation system in Germany during the 1980s are less clear. The analysis in this article is motivated by recent developments in econometrics for partial identification. We use extensive administrative data with the drawback that we can only observe lower and upper bounds for…

非线性固定效应面板模型中降低偏差的惩罚函数方法AI 解读

We consider estimation of nonlinear panel data models with individual specific fixed effects. Estimation of these models is complicated since estimation of the fixed effects when the time dimension is short generally results in inconsistent estimates of all model parameters. We present a penalized objective function that reduces the bias in the resulting point estimates. The penalty function is simple to construct and requires no modification for models with multiple…

非参数相关随机效应模型中边际效应的识别AI 解读

In this article, we consider identification and estimation of average marginal effects in a correlated random effects model without imposing functional form assumptions on the structural likelihood or the mixing distribution. Identification is achieved through imposing that the mixing distribution depends on observed covariates only through an index function. We leave the functional form of the index function unrestricted subject to smoothness conditions. We present…

混合类别与连续协变量下平均处理效应的有效估计AI 解读

In this article, we consider the nonparametric estimation of average treatment effects when there exist mixed categorical and continuous covariates. One distinguishing feature of the approach presented herein is the use of kernel smoothing for both the continuous and the discrete covariates. This approach, together with the cross-validation method. which we use for selecting the smoothing parameters, has the ability to automatically remove irrelevant covariates. We establish…

时间序列面板的非参数加性模型AI 解读

This paper discusses nonparametric models for panels of time series. There is already a substantial literature on nonlinear models and nonparametric methods in a regression and time series setting. But almost without exception these developments have been limited to univariate and multivariate models of moderate dimensions. Very little has been done for panels, where the dimension, often corresponding to a number of individuals, typically is very large but where the number…

利用聚合数据贝叶斯估计随机系数选择模型AI 解读

Abstract This article discusses the use of Bayesian methods for estimating logit demand models using aggregate data. We analyze two different demand systems: independent samples and consumer panel. Under the first system, there is a different and independent random sample of N consumers in each period and each consumer makes only a single purchase decision. Under the second system, the same N consumers make a purchase decision in each of T periods. Interestingly, there…

利用品牌层面营销变量建模品类层面的购买时点AI 解读

Abstract Purchase timing of households is usually modeled at the category level. However, many potential explanatory variables are observed at the brand level. To explain interpurchase times one has to either construct category‐level measures of marketing efforts, or integrate the model with a model for brand choice. In this paper we pursue the latter where we use latent brand preferences to capture the relevance of the marketing mix of an individual brand. We compare our…

带测量误差的部分线性模型变量选择AI 解读

This article focuses on variable selection for partially linear models when the covariates are measured with additive errors. We propose two classes of variable selection procedures, penalized least squares and penalized quantile regression, using the nonconvex penalized principle. The first procedure corrects the bias in the loss function caused by the measurement error by applying the so-called correction-for-attenuation approach, whereas the second procedure corrects the…

用于开发动态治疗方案的筛选实验AI 解读

Dynamic treatment regimes are time-varying treatments that individualize sequences of treatments to the patient. The construction of dynamic treatment regimes is challenging because a patient will be eligible for some treatment components only if he has not responded (or has responded) to other treatment components. In addition there are usually a number of potentially useful treatment components and combinations thereof. In this article, we propose new methodology for…

利用主分层对职业培训项目因果效应进行似然分析AI 解读

Government-sponsored job-training programs must be subject to evaluation to assess whether their effectiveness justifies their cost to the public. The evaluation usually focuses on employment and total earnings, although the effect on wages is also of interest, because this effect reflects the increase in human capital due to the training program, whereas the effect on total earnings may be simply reflecting the increased likelihood of employment without any effect on wage…

事件依赖删失与终止下复发事件均值函数及治疗效应的稳健估计AI 解读

In clinical trials featuring recurrent clinical events, the definition and estimation of treatment effects involves a number of interesting issues, especially when loss to follow-up may be event-related and when terminal events such as death preclude the occurrence of further events. This paper discusses a clinical trial of breast cancer patients with bone metastases where the recurrent events are skeletal complications, and where patients may die during the trial. We argue…