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存在干扰参数时的局部回归推断AI 解读

We consider inference based on local estimating equations in the presence of nuisance parameters. The framework is useful for a number of applications including those in economic policy evaluation based on discontinuities or kinks and in real-time financial risk management. We focus on the criterion-function-based (in particular, empirical likelihood-based) inference, and establish conditions under which the test statistic has a pivotal asymptotic distribution. In the key…

少量处理簇下双重差分的随机化推断AI 解读

Inference using difference-in-differences with clustered data requires care. Previous research has shown that, when there are few treated clusters, t-tests based on cluster-robust variance estimators (CRVEs) severely overreject, and different variants of the wild cluster bootstrap can either overreject or underreject dramatically. We study two randomization inference (RI) procedures. A procedure based on estimated coefficients may be unreliable when clusters are…

增发股票的时机与后果：回归不连续方法AI 解读

The likelihood of seasoned equity offerings (SEOs) jumps discontinuously when the stock price equals the most recent equity offer price. Anchoring on the last offer price holds after considering executive turnovers, stock splits, earnings management, or dividend adjustments. Using a fuzzy regression discontinuity design around this cutoff, which exploits local randomness in stock prices, we investigate the consequences of anchoring in SEOs. We find significant increases in…

混合效应时变网络模型及其在脑连接分析中的应用AI 解读

Time-varying networks are fast emerging in a wide range of scientific and business applications. Most existing dynamic network models are limited to a single-subject and discrete-time setting. In this article, we propose a mixed-effect network model that characterizes the continuous time-varying behavior of the network at the population level, meanwhile taking into account both the individual subject variability as well as the prior module information. We develop a multistep…

技能要求上升：劳动力充裕时雇主是否要求更高技能？AI 解读

Using a proprietary database of online job postings, we find that education and experience requirements rose during the Great Recession. These increases were larger in states and occupations that experienced greater increases in the supply of available workers. This finding is robust to controlling for local demand conditions and firm [Formula: see text] job-title fixed effects and using a natural experiment arising from troop withdrawals as an exogenous shock to labor…

信用不良也无妨？不良信用报告的信贷与劳动市场后果AI 解读

ABSTRACT We study the financial and labor market impacts of bad credit reports. Using difference‐in‐differences variation from the staggered removal of bankruptcy flags, we show that bankruptcy flag removal leads to economically large increases in credit limits and borrowing. Using administrative tax records linked to personal bankruptcy records, we estimate economically small effects of flag removal on employment and earnings outcomes. We rationalize these contrasting…

套利限制对资产定价异象的因果影响AI 解读

ABSTRACT We examine the causal effect of limits to arbitrage on 11 well‐known asset pricing anomalies using the pilot program of Regulation SHO, which relaxed short‐sale constraints for a quasi‐random set of pilot stocks, as a natural experiment. We find that the anomalies became weaker on portfolios constructed with pilot stocks during the pilot period. The pilot program reduced the combined anomaly long–short portfolio returns by 72 basis points per month, a difference…

通过高维协变量平衡倾向得分稳健估计因果效应AI 解读

Summary We propose a robust method to estimate the average treatment effects in observational studies when the number of potential confounders is possibly much greater than the sample size. Our method consists of three steps. We first use a class of penalized $M$-estimators for the propensity score and outcome models. We then calibrate the initial estimate of the propensity score by balancing a carefully selected subset of covariates that are predictive of the outcome.…

高维线性中介模型中间接效应的估计与推断AI 解读

Summary Mediation analysis is difficult when the number of potential mediators is larger than the sample size. In this paper we propose new inference procedures for the indirect effect in the presence of high-dimensional mediators for linear mediation models. We develop methods for both incomplete mediation, where a direct effect may exist, and complete mediation, where the direct effect is known to be absent. We prove consistency and asymptotic normality of our indirect…

广义工具变量不等式：检验工具变量独立性假设AI 解读

Summary This paper proposes a new set of testable implications for the instrumental variable independence assumption for discrete treatment, but unrestricted outcome and instruments: generalized instrumental inequalities. When outcome and treatment are both binary, but instruments are unrestricted, we show that the generalized instrumental inequalities are necessary and sufficient to detect all observable violations of the instrumental variable independence assumption. To…

现在偏误与时间偏好的识别：土地租赁合同数据的应用AI 解读

Summary What can contracts—traded and priced in a competitive market and featuring a pre-specified system of future payments—teach us about time preferences and present bias? We first show that identification of present bias requires assumptions on the felicity function and that agents must have credit constraints on consumption expenditure. Moreover, when there is heterogeneity in present bias, identification requires that agents with the same present bias parameter buy…

异质性处理效应模型中的双向排除限制AI 解读

Summary In this paper, we propose a novel method to identify the conditional average treatment effect partial derivative (CATE-PD) in an environment in which the treatment is endogenous, the treatment effect is heterogeneous, the candidate 'instrumental variables' can be correlated with latent errors, and the treatment selection does not need to be (weakly) monotone. We show that CATE-PD is point-identified under mild conditions if two-way exclusion restrictions exist: (a)…

深度神经网络与计量方法预测气候变化对农业产量影响的比较AI 解读

Summary Predicting the impact of climate change on crop yield is difficult, in part because the production function mapping weather to yield is high dimensional and nonlinear. We compare three approaches to predicting yields: (a) deep neural networks (DNNs), (b) traditional panel-data models, and (c) a new panel-data model that allows for unit and time fixed effects in both intercepts and slopes in the agricultural production function—made feasible by a new estimator called…

非平稳误差下广义变换面板模型的半参数估计AI 解读

Summary Early studies of the generalized transformation panel data model resorted to the identical marginal distribution of the error term over time. This stationarity condition is restrictive for many applications, especially as the number of time periods increases. This paper considers nonstationary censored generalized transformation panel data models where the idiosyncratic error has an unknown nonseparable form and admits a flexible relationship between the observable…

具有多个阈值的回归不连续设计AI 解读

Numerous empirical studies employ regression discontinuity designs with multiple cutoffs and heterogeneous treatments. A common practice is to normalize all the cutoffs to zero and estimate one effect. This procedure identifies the average treatment effect (ATE) on the observed distribution of individuals local to existing cutoffs. However, researchers often want to make inferences on more meaningful ATEs, computed over general counterfactual distributions of individuals,…

固定效应分位数回归的无偏渐近正态性AI 解读

Nonlinear panel data models with fixed individual effects provide an important set of tools for describing microeconometric data. In a large class of such models (including probit, proportional hazard and quantile regression to name just a few) it is impossible to difference out the individual effects, and inference is usually justified in a ‘large n large T’ asymptotic framework. However, there is a considerable gap in the type of assumptions that are currently imposed in…

具有跨期优化的空间动态模型：设定与估计AI 解读

In this paper, we introduce a dynamic spatial interaction econometric model. There are n forward-looking agents of whom each has a parametric linear-quadratic payoff, and they interact with their neighbors through a spatial network. Considering a Markov perfect equilibrium (MPE), we derive a unique Nash equilibrium equation and construct a new spatial dynamic panel data (SDPD) model. For estimation, we suggest mainly the quasi-maximum likelihood (QML) method. Asymptotic…

时变个体效应面板数据中的变点估计AI 解读

Summary Existing panel data methods remove unobserved individual effects before change point estimation through data transformations such as first‐differencing. In this paper, we show that multiple change points can be consistently estimated in short panels via ordinary least squares. Since no data variation is removed before change point estimation, our method has better small‐sample properties compared to first‐differencing methods. We also propose two tests that identify…

适用于固定T面板的截面平均主成分方法AI 解读

Summary Because of the increased availability of large panel data sets, common factor models have become very popular. The workhorse of the literature is the principal components (PC) method, which is based on an eigen‐analysis of the sample covariance matrix of the data. Some of its uses are to estimate the factors and their loadings, to determine the number of factors, and to conduct inference when estimated factors are used in panel regression models. The bulk of the…

图及其边上的各向同性协方差函数AI 解读

We develop parametric classes of covariance functions on linear networks and their extension to graphs with Euclidean edges, that is, graphs with edges viewed as line segments or more general sets with a coordinate system allowing us to consider points on the graph which are vertices or points on an edge. Our covariance functions are defined on the vertices and edge points of these graphs and are isotropic in the sense that they depend only on the geodesic distance or on a…

非齐次随机图的双样本假设检验AI 解读

The study of networks leads to a wide range of high-dimensional inference problems. In many practical applications, one needs to draw inference from one or few large sparse networks. The present paper studies hypothesis testing of graphs in this high-dimensional regime, where the goal is to test between two populations of inhomogeneous random graphs defined on the same set of $n$ vertices. The size of each population $m$ is much smaller than $n$, and can even be a constant…

双重切片辅助的高维删失数据充分降维AI 解读

This paper provides a unified framework and an efficient algorithm for analyzing high-dimensional survival data under weak modeling assumptions. In particular, it imposes neither parametric distributional assumption nor linear regression assumption. It only assumes that the survival time $T$ depends on a high-dimensional covariate vector $\mathbf{X}$ through low-dimensional linear combinations of covariates $\Gamma ^{T}\mathbf{X}$. The censoring time is allowed to be…

用于识别服从者与推广因果效应的锐度工具变量AI 解读

It is well known that, without restricting treatment effect heterogeneity, instrumental variable (IV) methods only identify “local” effects among compliers, that is, those subjects who take treatment only when encouraged by the IV. Local effects are controversial since they seem to only apply to an unidentified subgroup; this has led many to denounce these effects as having little policy relevance. However, we show that such pessimism is not always warranted: it can be…

总产出生产函数的识别AI 解读

We study the nonparametric identification of gross output production functions under the environment of the commonly employed proxy variable methods. We show that applying these methods to gross output requires additional sources of variation in the demand for flexible inputs (e.g., prices). Using a transformation of the firm’s first-order condition, we develop a new nonparametric identification strategy for gross output that can be employed even when additional sources of…