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仿射无套利收益率网络模型及欧债危机应用AI 解读

We develop a parsimonious class of affine arbitrage-free yield net models for consistent bond pricing across maturities and issuers of different risk levels. Containing a core curve and multiple peripheral curves, the yield net is spanned by three layers of factors: base factors spanning all curves, and common and individual spread factors. Under the arbitrage-free assumption, we prove a parsimonious solution to the risk-neutral process that guarantees joint identification…

基于后验分布的 Wald 型假设检验统计量AI 解读

A new Wald-type statistic is proposed for hypothesis testing based on Bayesian posterior distributions under the correct model specification. The new statistic can be explained as a posterior version of the Wald statistic and has several nice properties. First, it is well-defined under improper prior distributions. Second, it avoids Jeffreys–Lindley–Bartlett’s paradox. Third, under the null hypothesis and repeated sampling, it follows a χ2 distribution asymptotically,…

因子投资：贝叶斯层级方法AI 解读

This paper investigates the asset allocation problem when returns are predictable. We introduce a market-timing Bayesian hierarchical (BH) approach that adopts heterogeneous time-varying coefficients driven by lagged fundamental characteristics. Our approach estimates the conditional expected returns and residual covariance matrix jointly enables evaluating the estimation risk in the portfolio analysis. The hierarchical prior allows the modeling of different assets…

贝叶斯因子调整稀疏回归AI 解读

Many sparse regression methods rely on an assumption that the covariates are weakly correlated, which hardly holds in many economic and financial datasets. To relax this assumption, we model the strongly correlated covariates by a factor structure: strong correlations among covariates are modeled by common factors, while the remaining variations of covariates are modeled as idiosyncratic components. We then propose a factor-adjusted sparse regression model and develop a…

共同基金行业技能分布的贝叶斯非参数学习AI 解读

In this paper, we use Bayesian nonparametric learning to estimate the skill of actively managed mutual funds and also to estimate the population distribution of skill. A nonparametric hierarchical prior, where the hyperprior distribution is unknown and modeled with a Dirichlet Process prior, is used to model the skill parameter, with its posterior predictive distribution being an estimate of the population distribution. Our nonparametric approach is equivalent to an…

VAR 分布误设下渐近有效的贝叶斯推断AI 解读

Inaccurately imposing Gaussian distributional assumptions in standard multivariate time series models does not affect inference on the autoregressive coefficients but distorts both classical and Bayesian inference on the volatility matrix whenever the true error distribution has excess kurtosis relative to the multivariate normal density. Inference on the intercept is also affected whenever the innovations are generated from a non-symmetric distribution. As a result of…

含未观测异质性的大规模面板选择模型：贝叶斯与极大似然分析AI 解读

This paper considers the estimation and inference procedures for the case of a logistic panel regression model with interactive fixed effects, where multiple individual effects are allowed and the model is capable of capturing high-dimensional cross-section dependence. The proposed model also allows for heterogeneous regression coefficients. New Bayesian and non-Bayesian approaches are introduced to estimate the model parameters. We investigate the asymptotic behaviors of…

长期限预测回归的偏差AI 解读

Analogous to Stambaugh (1999), this paper derives the small sample bias of estimators in J-horizon predictive regressions, providing a closed-form solution in terms of the sample size, horizon and persistence of the predictive variable. For large J, the bias is linear in JT with a slope that depends on the predictive variable's persistence. The paper offers a number of other useful results, including (i) important extensions to the original Stambaugh (1999) setting, (ii)…

具有有限混合结构的计数 Roy 模型AI 解读

Summary This paper develops the Finite Mixture Roy model for count variables and uses this semiparametric model to analyze the effect of supplemental Medigap private insurance on the demand for prescription drugs for the U.S. elderly unemployed Medicare population. The model is an extension of the Count Roy model, which produces unrealistic treatment effects when observed count patterns are consistent with finite mixtures. To estimate the numbers of components in the…

含双向固定效应的指数回归工具变量估计及引力方程应用AI 解读

Summary This paper introduces instrumental‐variable estimators for exponential‐regression models that feature two‐way fixed effects. These techniques allow us to develop a theory‐consistent approach to the estimation of cross‐sectional gravity equations that can accommodate the endogeneity of policy variables. We apply this approach to a dataset in which the policy decision of interest is the engagement in a free‐trade agreement. We explore ways to exploit the transitivity…

具有超越对数生产技术的动态潜在因子技能形成模型识别AI 解读

Summary In this paper, we highlight an important property of the translog production function for the identification of treatment effects in a model of latent skill formation. We show that when using a translog specification of the skill technology, properly anchored treatment effect estimates are invariant to any location and scale normalizations of the underlying measures. By contrast, when researchers assume a CES production function and impose standard location and scale…

通过倾向得分实现协变量分布平衡AI 解读

Summary This paper proposes new estimators for the propensity score that aim to maximize the covariate distribution balance among different treatment groups. Heuristically, our proposed procedure attempts to estimate a propensity score model by making the underlying covariate distribution of different treatment groups as close to each other as possible. Our estimators are data‐driven and can be used to estimate different treatment effect parameters under different…

用随机网络 SIR 模型匹配新冠疫情理论与证据AI 解读

Summary This paper develops an individual‐based stochastic network SIR model for the empirical analysis of the Covid‐19 pandemic. It derives moment conditions for the number of infected and active cases for single as well as multigroup epidemic models. These moment conditions are used to investigate the identification and estimation of the transmission rates. The paper then proposes a method that jointly estimates the transmission rate and the magnitude of under‐reporting of…

关于《多元依赖的多尺度 Fisher 独立性检验》的讨论AI 解读

We read with interest the work of Gorsky & Ma (2022) on statistical dependence testing using a multi-scale Fisher’s independence test, MultiFIT. The procedure consists of first transforming the data to map to the unit ball, then performing univariate Fisher’s exact tests of independence on a collection of |$2 \times 2$| contingency tables, and finally correcting for the use of multiple testing. The collection is obtained using a divide-and-conquer approach with a…

关于“多元依赖的多尺度 Fisher 独立性检验”的讨论：二进制展开视角AI 解读

The multi-scale Fisher’s independence test, MultiFIT, proposed by Gorsky & Ma (2022) is a novel method to test independence between two random vectors. By its design, this test is particularly useful in detecting local dependence. Moreover, by adopting a resampling-free approach, it can easily accommodate massive sample sizes. Another benefit of the proposed method is its ability to interpret the nature of dependency. We congratulate the authors for their very interesting…

关于“多元依赖的多尺度 Fisher 独立性检验”的讨论：文献定位与选择标准AI 解读

We would like to congratulate the authors on an interesting paper that provides many avenues for discussion. The reduction of a nonparametric problem to a sequence of |$2 \times 2$| contingency tables is very elegant, and Fisher’s exact test is used to great effect. There are now a large number of methods for multivariate independence testing, but the proposed procedure stands out by providing uniform Type I error controls without a large computational burden, combining the…

多元依赖的多尺度 Fisher 独立性检验AI 解读

Summary Identifying dependency in multivariate data is a common inference task that arises in numerous applications. However, existing nonparametric independence tests typically require computation that scales at least quadratically with the sample size, making it difficult to apply them in the presence of massive sample sizes. Moreover, resampling is usually necessary to evaluate the statistical significance of the resulting test statistics at finite sample sizes, further…

结合高维节点变量的网络联合潜在空间模型AI 解读

Summary Network latent space models assume that each node is associated with an unobserved latent position in a Euclidean , and such latent variables determine the probability of two nodes connecting with each other. In many applications, nodes in the network are often observed along with high-dimensional node variables, and these node variables provide important information for understanding the network structure. However, classical network latent space models have several…

异质系数、控制变量与多重处理效应识别AI 解读

Summary Multi-dimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use control variables to give necessary and sufficient conditions for identification of average treatment effects. With mutually exclusive treatments we find that, provided the…

利用协变量的局部线性graphon估计AI 解读

Summary We consider local linear estimation of the graphon function, which determines probabilities of pairwise edges between nodes in an unlabelled network. Real-world networks are typically characterized by node heterogeneity, with different nodes exhibiting different degrees of interaction. Existing approaches to graphon estimation are limited to local constant approximations, and are not designed to estimate heterogeneity across the full network. In this paper, we show…

析因实验中的回归因果推断：目标量、模型设定与基于设计的性质AI 解读

Summary Factorial designs are widely used because of their ability to accommodate multiple factors simultaneously. Factor-based regression with main effects and some interactions is the dominant strategy for downstream analysis, delivering point estimators and standard errors simultaneously via one least-squares fit. Justification of these convenient estimators from the design-based perspective requires quantifying their sampling properties under the assignment mechanism…

用于估计时间平均协方差矩阵的翼帆型滞后窗AI 解读

Summary Lag windows are commonly used in time series analysis, econometrics, steady-state simulation and Markov chain Monte Carlo to estimate time-average covariance matrices. In the presence of positive correlation in the underlying process, estimators of this matrix almost always exhibit significant negative bias, leading to undesirable finite-sample properties. We propose a new family of lag windows specifically designed to improve finite-sample performance by offsetting…

含重复观测数据的渐近分布无关变点检测AI 解读

Summary A nonparametric framework for changepoint detection, based on scan statistics utilizing graphs that represent similarities among observations, is gaining attention owing to its flexibility and good performance for high-dimensional and non-Euclidean data sequences. However, this graph-based framework faces challenges when there are repeated observations in the sequence, which is often the case for discrete data such as network data. In this article we extend the…

分位数趋势滤波的风险界AI 解读

Summary We study quantile trend filtering, a recently proposed method for nonparametric quantile regression, with the goal of generalizing existing risk bounds for the usual trend-filtering estimators that perform mean regression. We study both the penalized and the constrained versions, of order $r \geqslant 1$, of univariate quantile trend filtering. Our results show that both the constrained and the penalized versions of order $r \geqslant 1$ attain the minimax rate up to…