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工具变量与平均处理效应的符号AI 解读

This paper considers identification and inference about the sign of the average effect of a binary endogenous regressor (or treatment) on a binary outcome of interest when a binary instrument is available. In this setting, the average effect of the endogenous regressor on the outcome is sometimes referred to as the average treatment effect (ATE). We consider four different sets of assumptions: instrument exogeneity, instrument exogeneity and monotonicity on the outcome…

不连续与折点激励分配机制下加权平均处理效应的识别及小波估计AI 解读

This paper studies identification, estimation, and inference of a weighted average treatment effect (W-ATE) parameter in a class of switching regime models, where the agent’s selection of treatment is affected by either a discontinuous or kink incentive assignment mechanism and some unobservable characteristic. For each assignment mechanism, we (i) establish identification and propose a local wavelet estimator of the W-ATE; (ii) establish asymptotic properties of the local…

纯跳跃设定下的期权面板推断AI 解读

We develop parametric inference procedures for large panels of noisy option data in a setting, where the underlying process is of pure-jump type, i.e., evolves only through a sequence of jumps. The panel consists of options written on the underlying asset with a (different) set of strikes and maturities available across the observation times. We consider an asymptotic setting in which the cross-sectional dimension of the panel increases to infinity, while the time span…

GARCH-X 类模型的检验AI 解读

We present novel theory for testing for reduction of GARCH-X type models with an exogenous (X) covariate to standard GARCH type models. To deal with the problems of potential nuisance parameters on the boundary of the parameter space as well as lack of identification under the null, we exploit a noticeable property of specific zero-entries in the inverse information of the GARCH-X type models. Specifically, we consider sequential testing based on two likelihood ratio tests…

高维纵向数据协方差矩阵的同质性检验AI 解读

Summary This paper deals with the detection and identification of changepoints among covariances of high-dimensional longitudinal data, where the number of features is greater than both the sample size and the number of repeated measurements. The proposed methods are applicable under general temporal-spatial dependence. A new test statistic is introduced for changepoint detection, and its asymptotic distribution is established. If a changepoint is detected, an estimate of…

面板二元响应模型识别的脆弱性AI 解读

Summary The present paper considers a linear binary response model for panel data with random effects that differ across individuals but are constant over time, and it investigates the roles of the various assumptions that are used to establish conditions for identification. The paper also shows that even for this simple model, it is always possible—including in the logistic case—to find a distribution of the random effects given the exogenous variables, such that the…

外生不确定性与使用外部工具变量的结构向量自回归识别AI 解读

Summary We provide necessary and sufficient conditions for the identification (point‐identification) of structural vector autoregressions (SVARs) with external instruments considering the case in which r instruments are used to identify g structural shocks of interest, r ≥ g ≥ 1. Novel frequentist estimation methods are discussed by considering both a “partial shocks” identification strategy, where only g structural shocks are of interest and are instrumented, and a “full…

含内生虚拟解释变量的广义双变量Probit模型估计AI 解读

Summary The purpose of this paper is to provide guidelines for empirical researchers who use a class of bivariate threshold crossing models with dummy endogenous variables. A common practice employed by the researchers is the specification of the joint distribution of unobservables as a bivariate normal distribution, which results in a bivariate probit model . To address the problem of misspecification in this practice, we propose an easy‐to‐implement semiparametric…

用野生自助法改善聚类工具变量模型推断的两个应用AI 解读

Summary Microeconomic data often have within‐cluster dependence, which affects standard error estimation and inference. When the number of clusters is small, asymptotic tests can be severely oversized. In the instrumental variables (IV) model, the potential presence of weak instruments further complicates hypothesis testing. We use wild bootstrap methods to improve inference in two empirical applications with these characteristics. Building from estimating equations and…

谱方法与正则化极大似然均可实现最优前 K 项排序AI 解读

This paper is concerned with the problem of top-$K$ ranking from pairwise comparisons. Given a collection of $n$ items and a few pairwise comparisons across them, one wishes to identify the set of $K$ items that receive the highest ranks. To tackle this problem, we adopt the logistic parametric model—the Bradley–Terry–Luce model, where each item is assigned a latent preference score, and where the outcome of each pairwise comparison depends solely on the relative scores of…

收入区间数据下离散选择的应用福利分析AI 解读

This paper concerns empirical measurement of Hicksian consumer welfare under interval-reported income. Bhattacharya (2015, 2018a) has shown that for discrete choice, welfare distributions resulting from a hypothetical price-change can be expressed as closed-form transformations of choice probabilities. However, when income is interval-reported, as is the case in many surveys, the choice probabilities, and hence welfare distributions are not point-identified. We derive bounds…

部分识别光滑凸模型的贝叶斯推断AI 解读

This paper proposes novel Bayesian procedures for partially identified models when the identified set is convex with a smooth boundary, whose support function is locally smooth with respect to the data distribution. Using the posterior of the identified set, we construct Bayesian credible sets for the identified set, the partially identified parameter and their scalar transformations. These constructions, based on the support function, benefit from several computationally…

政府债务与企业杠杆：国际证据AI 解读

We empirically investigate the impact of government debt on corporate financing decisions in an international setting. We show a negative relation between government debt and corporate leverage using data on 40 countries between 1990–2014. This negative relation is stronger for government debt that is financed domestically, for firms that are larger and more profitable, and in countries with more developed equity markets. To address potential endogeneity concerns, we use an…

以个股检验资产定价模型：处理风险溢价估计中的变量测量误差偏差AI 解读

To attenuate an inherent errors-in-variables bias, portfolios are widely employed to test asset pricing models; but portfolios might mask relevant risk- or return-related features of individual stocks. We propose an instrumental variables approach that allows the use of individual stocks as test assets, yet delivers consistent estimates of ex post risk premiums. This estimator also yields well-specified tests in small samples. The market risk premium under the capital asset…

资产定价的需求系统方法AI 解读

We develop an asset pricing model with flexible heterogeneity in asset demand across investors, designed to match institutional and household holdings. A portfolio choice model implies characteristics-based demand when returns have a factor structure and expected returns and factor loadings depend on the assets’ own characteristics. We propose an instrumental variables estimator for the characteristics-based demand system to address the endogeneity of demand and asset…

存在部分无效工具变量时Lasso在工具变量估计中的使用AI 解读

We investigate the behavior of the Lasso for selecting invalid instruments in linear instrumental variables models for estimating causal effects of exposures on outcomes, as proposed recently by Kang et al. Invalid instruments are such that they fail the exclusion restriction and enter the model as explanatory variables. We show that for this setup, the Lasso may not consistently select the invalid instruments if these are relatively strong. We propose a median estimator…

局部平稳过程变系数可加模型的估计与模型识别AI 解读

The additive model and the varying-coefficient model are both powerful regression tools, with wide practical applications. However, our empirical study on a financial data has shown that both of these models have drawbacks when applied to locally stationary time series. For the analysis of functional data, Zhang and Wang have proposed a flexible regression method, called the varying-coefficient additive model (VCAM), and presented a two-step spline estimation method.…

因子成分与特异成分的经验过程AI 解读

The distributions of the common and idiosyncratic components for an individual variable are important in forecasting and applications. However, they are not identified with low-dimensional observations. Using the recently developed theory for large dimensional approximate factor model for large panel data, the common and idiosyncratic components can be estimated consistently. Based on the estimated common and idiosyncratic components, we construct the empirical processes for…

删失点独立性的检验AI 解读

Identification in censored regression analysis and hazard models of duration outcomes relies on the condition that censoring points are conditionally independent of latent outcomes, an assumption which may be questionable in many settings. This article proposes a test for this assumption based on a Cramer–von-Mises-like test statistic comparing two different nonparametric estimators for the latent outcome cdf: the Kaplan–Meier estimator, and the empirical cdf conditional on…

带补充样本的多期面板中的不可忽略流失AI 解读

In panel surveys, some observation units drop out before the end of the observation period. This panel attrition should not be ignored if it is related to the variables of interest. Hirano, Imbens, Ridder, and Rubin proposed the additively nonignorable (AN) attrition model to correct for the potential selectivity of the attrition in panels with two periods. If a refreshment sample is available in the second period, their model nonparametrically just-identifies the population…

大数据流失偏误的面板分位数方法：随机实验背景下的证据AI 解读

This paper introduces a quantile regression estimator for panel data models with individual heterogeneity and attrition. The method is motivated by the fact that attrition bias is often encountered in Big Data applications. For example, many users sign-up for the latest program but few remain active users several months later, making the evaluation of such interventions inherently very challenging. Building on earlier work by Hausman and Wise (1979), we provide a simple…

三阶段半参数推断：控制变量与可微性AI 解读

We show the usefulness of the path-derivative calculations that were introduced in econometrics by Newey (1994) for multi-step semi-parametric estimators. These estimators estimate a finite-dimensional parameter using moment conditions that depend on nonparametric regressions on observed and estimated regressors that are estimated in the second and first step of the estimation procedure, respectively. Our earlier paper showed that Newey’s calculations can be extended to…

若干非可分离散选择模型的构造性识别AI 解读

This paper introduces new results on the nonparametric identification of separable and nonseparable discrete choice models. It presents constructive methods for recovering the derivatives of the utility functions of the alternatives in a set, when these utility functions are nonparametric and nonseparable in unobservable random terms. When the utility functions are separable, the constructive methods require fewer assumptions. It is assumed that only the probability of…

横截面与面板数据中的非可分多项选择模型AI 解读

Multinomial choice models are fundamental for empirical modeling of economic choices among discrete alternatives. We analyze identification of binary and multinomial choice models when the choice utilities are nonseparable in observed attributes and multidimensional unobserved heterogeneity with cross-section and panel data. We show that derivatives of choice probabilities with respect to continuous attributes are weighted averages of utility derivatives in cross-section…