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公司债市场存在风险收益权衡吗？时间序列与横截面证据AI 解读

We provide time-series and cross-sectional evidence on the significance of a risk-return tradeoff in the bond and equity markets. We find a significantly positive intertemporal relation between expected return and risk in the bond market. We also propose novel measures of systematic and idiosyncratic risk for individual corporate bonds and find a significantly positive cross-sectional relation between systematic risk and expected bond returns, whereas there is no significant…

非上市企业的并购传闻AI 解读

We examine 68,044 completed or abandoned M&A transactions involving unlisted targets to determine the effect of transaction rumors on deal-closing propensity and transaction values. Estimation is challenging because rumors may be spread on purpose or emerge accidentally while transaction values are only observable for completed deals and no regulation requires to disclose them. We use indirect inference to overcome these challenges. We find that (a) M&A rumors are deal…

实证公司金融中的溢出效应AI 解读

Despite their importance, the discussion of spillover effects in empirical research often misses the rigor dedicated to endogeneity concerns. We analyze a broad set of workhorse models of firm interactions and show that spillovers naturally arise in many corporate finance settings. This has important implications for the estimation of treatment effects: (i) even with random treatment, spillovers lead to a complicated bias; (ii) fixed effects can exacerbate the…

核心盈余：新数据与证据AI 解读

Using a novel dataset, we show that components of firms’ GAAP earnings stemming from ancillary business activities or transitory shocks are significant in frequency and magnitude. These components have grown over time and are dispersed across various sections of the 10-K. Excluding them from GAAP earnings yields a core earnings measure that distinguishes between the recurring and non-recurring components of net income and forecasts future performance. Analysts and market…

基于非参数似然的相对误差精确统计量AI 解读

This paper develops a new test statistic for parameters defined by moment conditions that exhibits desirable relative error properties for the approximation of tail area probabilities. Our statistic, called the tilted exponential tilting (TET) statistic, is constructed by estimating certain cumulant generating functions under exponential tilting weights. We show that the asymptotic p -value of the TET statistic can provide an accurate approximation to the p -value of an…

大规模数据的时变协方差矩阵估计AI 解读

Time variation is a fundamental problem in statistical and econometric analysis of macroeconomic and financial data. Recently, there has been considerable focus on developing econometric modelling that enables stochastic structural change in model parameters and on model estimation by Bayesian or nonparametric kernel methods. In the context of the estimation of covariance matrices of large dimensional panels, such data requires taking into account time variation, possible…

依赖随机映射的迭代与非线性动态中的外生性AI 解读

We discuss the existence and uniqueness of stationary and ergodic nonlinear autoregressive processes when exogenous regressors are incorporated into the dynamic. To this end, we consider the convergence of the backward iterations of dependent random maps. In particular, we give a new result when the classical condition of contraction on average is replaced with a contraction in conditional expectation. Under some conditions, we also discuss the dependence properties of these…

存在内生性的非线性协整幂函数回归AI 解读

This paper develops an asymptotic theory for nonlinear cointegrating power function regression. The framework extends earlier work on the deterministic trend case and allows for both endogeneity and heteroskedasticity, which makes the models and inferential methods relevant to many empirical economic and financial applications, including predictive regression. A new test for linear cointegration against nonlinear departures is developed based on a simple linearized…

具有不限定分布的相关随机效应的非线性面板模型AI 解读

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a…

异方差非线性回归模型的最小二乘估计AI 解读

This paper develops an asymptotic theory of nonlinear least squares estimation by establishing a new framework that can be easily applied to various nonlinear regression models with heteroscedasticity. As an illustration, we explore an application of the framework to nonlinear regression models with nonstationarity and heteroscedasticity. In addition to these main results, this paper provides a maximum inequality for a class of martingales, which is of interest in its own…

多重相互依赖持续时间模型AI 解读

We propose a multiplex interdependent durations model and study its empirical content. The model considers an empirical stopping game of multiple agents making optimal timing decisions with incomplete information. We characterize the unique Bayesian Nash equilibrium of the stopping game in a system of simultaneous equations involving the conditional distribution of each duration with a moderate strategic interaction condition. The system of nonlinear simultaneous equations…

《事件史与拓扑数据分析》讨论AI 解读

Summary Garside et al. (2021) use event history methods to analyse topological data. We provide additional background on persistent homology to contrast the hazard estimators used in Garside et al. (2021) with standard approaches in topological data analysis. In particular, Garside et al.’s approach is a local method, which has advantages and disadvantages, whereas homology is global. We also provide more details on persistence landscapes and show how a more complete use of…

二元工具变量模型下局部处理效应的估计AI 解读

Summary Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges of causal estimation with a binary outcome and…

基于排列的贪婪因果推断算法的一致性保证AI 解读

Summary Directed acyclic graphical models are widely used to represent complex causal systems. Since the basic task of learning such a model from data is NP-hard, a standard approach is greedy search over the space of directed acyclic graphs or Markov equivalence classes of directed acyclic graphs. As the space of directed acyclic graphs on $p$ nodes and the associated space of Markov equivalence classes are both much larger than the space of permutations, it is desirable to…

协变量数量增长时完全随机实验中的回归调整AI 解读

Summary Randomized experiments have become important tools in empirical research. In a completely randomized treatment-control experiment, the simple difference in means of the outcome is un- biased for the average treatment effect, and covariate adjustment can further improve the efficiency without assuming a correctly specified outcome model. In modern applications, experimenters often have access to many covariates, motivating the need for a theory of covariate adjustment…

尺度未知时多元正态均值向量的可容许估计量AI 解读

Summary We study admissibility of a subclass of generalized Bayes estimators of a multivariate normal vector in the case where the variance is unknown, under scaled quadratic loss. Minimaxity is established for some of these estimators.

连续处理下工具变量有效性的不可检验性AI 解读

Summary This note presents a proof of the conjecture in Pearl (1995) about testing the validity of an instrumental variable in hidden variable models. It implies that instrument validity cannot be tested in the case where the endogenous treatment is continuously distributed. This stands in contrast to the classical testability results for instrument validity when the treatment is discrete. However, imposing weak structural assumptions on the model, such as continuity between…

疫苗试验主替代指标评价中缺失协变量下的变点推断AI 解读

Summary We consider the use of threshold-based regression models to evaluate immune response biomarkers as principal surrogate markers of a vaccine’s protective effect. Threshold-based regression models, which allow the relationship between a clinical outcome and a covariate to change dramatically across a threshold value in the covariate, have been studied by various authors under fully observed data. Limited research, however, has examined these models in the presence of…

学习基于 U 统计量的矩阵块结构AI 解读

Summary We introduce a conceptually simple, efficient and easily implemented approach for learning the block structure in a large matrix. Using the properties of U-statistics and large-dimensional random matrix theory, the group structure of many variables can be directly identified based on the eigenvalues and eigenvectors of the scaled sample matrix. We also establish the asymptotic properties of the proposed approach under mild conditions. The finite-sample performance of…

全基因组关联研究中协变量自适应的族错误率控制AI 解读

Summary The familywise error rate has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focused on false discovery rate control, while covariate-adaptive procedures controlling the familywise error rate remain underdeveloped. Here, we propose a novel…

事件史方法与拓扑数据分析AI 解读

Summary Persistent homology is used to track the appearance and disappearance of features as we move through a nested sequence of topological spaces. Equating the nested sequence to a filtration and the appearance and disappearance of features to events, we show that simple event history methods can be used for the analysis of topological data. We propose a version of the well-known Nelson–Aalen cumulative hazard estimator for the comparison of topological features of random…

基于最大偏差的函数型数据生物等效性检验AI 解读

Summary We study the problem of testing equivalence of functional parameters, such as the mean or the variance function, in the two-sample functional data setting. In contrast to previous work where the functional problem is reduced to a multiple testing problem for the equivalence of scalar data by comparing the functions at each point, our approach is based on an estimate of a distance measuring the maximum deviation between the two functional parameters. Equivalence is…

面板计数数据半参数回归模型的最大似然估计AI 解读

Summary Panel count data, in which the observation for each study subject consists of the number of recurrent events between successive examinations, are commonly encountered in industrial reliability testing, medical research and other scientific investigations. We formulate the effects of potentially time-dependent covariates on one or more types of recurrent events through nonhomogeneous Poisson processes with random effects. We employ nonparametric maximum likelihood…

依赖删失生存数据的半参数建模、估计与推断AI 解读

Summary When modelling survival data, it is common to assume that the survival time $T$ is conditionally independent of the censoring time $C$ given a set of covariates. However, there are numerous situations in which this assumption is not realistic. The goal of this paper is therefore to develop a semiparametric normal transformation model which assumes that, after a proper nonparametric monotone transformation, the vector $(T, C)$ follows a linear model, and the vector of…