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价值函数的一致推断AI 解读

We propose a method to conduct uniform inference for the (optimal) value function, that is, the function that results from optimizing an objective function marginally over one of its arguments. Marginal optimization is not Hadamard differentiable (that is, compactly differentiable) as a map between the spaces of objective and value functions, which is problematic because standard inference methods for nonlinear maps usually rely on Hadamard differentiability. However, we…

含外生协变量的因子 copula 模型估计与推断AI 解读

A factor copula model is proposed in which factors are either simulable or estimable from exogenous information. Point estimation and inference are based on a simulated methods of moments (SMM) approach with non-overlapping simulation draws. Consistency and limiting normality of the estimator is established and the validity of bootstrap standard errors is shown. Doing so, previous results from the literature are verified under low-level conditions imposed on the individual…

纵向数据惩罚分位数回归的野生 bootstrap 推断AI 解读

The existing theory of penalized quantile regression for longitudinal data has focused primarily on point estimation. In this work, we investigate statistical inference. We propose a wild residual bootstrap procedure and show that it is asymptotically valid for approximating the distribution of the penalized estimator. The model puts no restrictions on individual effects, and the estimator achieves consistency by letting the shrinkage decay in importance asymptotically. The…

含交互固定效应的异质面板二元响应模型AI 解读

In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we build a link between a maximum likelihood estimation and a least squares approach,…

扩展参数空间与相似性权重下的空间自回归AI 解读

We provide in this paper asymptotic theory for a spatial autoregressive model (SAR, henceforth) in which the spatial coefficient, λ, is allowed to be less than or equal to unity, as well as consistent with a local to unit root (LUR) model and of the moderate integration (MI) from unity type, and the spatial weights are allowed to be similarity-based and data driven. Other special cases of our setting include the random walk, a model in which all the weights are equal, the…

集合识别线性模型的去偏机器学习AI 解读

This paper provides estimation and inference methods for an identified set’s boundary (i.e., support function) where the selection among a very large number of covariates is based on modern regularized tools. I characterize the boundary using a semiparametric moment equation. Combining Neyman-orthogonality and sample splitting ideas, I construct a root-N consistent, uniformly asymptotically Gaussian estimator of the boundary and propose a multiplier bootstrap procedure to…

新冠疫情期间的股息暂停与现金流：动态计量模型AI 解读

Firms suspended dividend payments in unprecedented numbers in response to the outbreak of the Covid-19 pandemic. We develop a multivariate dynamic econometric model that allows dividend suspensions to affect the conditional mean, volatility, and jump probability of growth in daily industry-level dividends and demonstrate how the parameters of this model can be estimated using Bayesian Gibbs sampling methods. We find considerable heterogeneity across industries in the…

因子模型中长记忆的参数估计AI 解读

A dynamic factor model is proposed in that factor dynamics are driven by stochastic time trends describing arbitrary persistence levels. The proposed model is essentially a long memory factor model, which nests standard I(0) and I(1) behavior smoothly in common factors. In the estimation, principal components analysis (PCA) and conditional sum of squares (CSS) estimations are employed. For the dynamic model parameters, centered normal asymptotics are established at the usual…

面板数据潜在群组结构的估计与识别AI 解读

This paper provides a framework for joint estimation and identification of latent group structures in panel data models using a pairwise fusion penalized approach. The latent structure of the model allows individuals to be classified into different groups where the number of groups and the group membership are unknown. The individuals within a group have common slope parameters, while parameter heterogeneity is allowed across the groups. A penalized least squares (PLS)…

面板数据似然中得分偏差与信息偏差的作用AI 解读

We show why reducing information bias can improve the performance of likelihood based estimators and confidence regions in small samples, and why it seems to matter more for inference than for estimation. The insights in this paper are helpful in explaining several simulation findings in the panel data literature. E.g., we can explain the well documented phenomenon that reducing the score bias alone often reduces the finite sample variance of estimators and improves the…

系统性因子风险暴露的日内横截面分布AI 解读

We develop a test for the detection of intraday changes in the cross-sectional distribution of assets’ exposure to observable factors. The test is constructed for a panel of high-frequency asset returns, with the size of the cross-section and the sampling frequency increasing simultaneously. It is based on a comparison of the empirical characteristic functions of estimates of the assets’ factor loadings at different parts of the trading day, formed from local blocks of asset…

滚动回归估计量的分布AI 解读

We establish the asymptotic distribution for rolling linear regression models using various window widths. The limiting distribution depends on the width of the rolling window and on a “bias process” that is typically ignored in practice. Based on the asymptotic distribution, we tabulate critical values used to find uniform confidence intervals for the average values of regression parameters over the windows. We propose a corrected rolling regression technique that removes…

大型贝叶斯 VAR 的随机波动率设定比较AI 解读

Large Bayesian vector autoregressions with various forms of stochastic volatility have become increasingly popular in empirical macroeconomics. One main difficulty for practitioners is to choose the most suitable stochastic volatility specification for their particular application. We develop Bayesian model comparison methods–based on marginal likelihood estimators that combine conditional Monte Carlo and adaptive importance sampling–to choose among a variety of stochastic…

惩罚化时变模型平均AI 解读

This paper proposes a new penalized time-varying model averaging method to determine optimal time-varying combination weights for candidate models, which avoids over-fitting and yields sparseness from various potential predictive variables. The asymptotic optimality and convergence rate of the selected weights are derived even when all candidate models are misspecified, and the consistency and normality of the proposed time-varying model averaging estimator are obtained when…

收入冲击中随时间变化的不可观测异质性AI 解读

This paper considers the transitory-permanent model for the earnings process, and allows for time-varying individual-specific unobserved heterogeneity in each shock. The cross-sectional heterogeneity in each shock is drawn from an unknown distribution at each time period. Sufficient conditions for the nonparametric identification of the cross-sectional density functions of the heterogeneity are provided, under different assumptions on the time series behavior of the…

同伴效应与内生社会互动AI 解读

This paper proposes a solution to the problem of endogenous selection of peers in the linear-in-means model. We do not require to specify a model for how the selection of peers comes about. Rather, we exploit two restrictions that are inherent in many such specifications to construct conditional moment conditions. The restrictions in question are that link decisions that involve a given individual are not all independent of one another, but that they are independent of the…

高维时间序列的社群网络自回归AI 解读

Modeling responses on the nodes of a large-scale network is an important task that arises commonly in practice. This paper proposes a community network vector autoregressive (CNAR) model, which utilizes the network structure to characterize the dependence and intra-community homogeneity of the high-dimensional time series. The CNAR model greatly increases the flexibility and generality of the network vector autoregressive (NAR) model proposed by Zhu et al. (2017) by allowing…

拍卖决定合同中的激励效应与选择效应区分AI 解读

This paper develops a novel approach to estimate how contract and principal–agent characteristics influence a post-auction outcome when the matching between agents and principals derives from an auction process. We propose a control-function approach to account jointly for the endogeneity of contracts and matching. This consists of, first, estimating the primitives of an interdependent values auction model – which is shown to be non-parametrically identified from the bidding…

长期高频观测回归中的渐近 F 检验AI 解读

This paper proposes tests of linear hypotheses when the variables may be continuous-time processes with observations collected at a high sampling frequency over a long span. Utilizing series long run variance (LRV) estimation in place of the traditional kernel LRV estimation, we develop easy-to-implement and more accurate F tests in both stationary and nonstationary environments. The nonstationary environment accommodates exogenous regressors that are general…

ETF篮子调整协方差估计AI 解读

The increase in trading frequency of Exchanged Traded Funds (ETFs) presents a positive externality for financial risk management when the price of the ETF is available at a higher frequency than the price of the component stocks. The positive spillover consists in improving the accuracy of pre-estimators of the integrated covariance of the stocks included in the ETF basket. The proposed ETF Basket-Adjusted Covariance (BAC) equals the pre-estimator plus a minimal adjustment…

离散工具变量与解释变量下的非参数识别和估计AI 解读

In a nonparametric instrumental regression model in which the regressor and instrument are discretely distributed, we strengthen the conventional moment independence assumption between instrument and residual to include higher order moments. We give conditions under which a function of interest is partially identified when the regressor has more mass points than the instrument. We also show that the function is point identified as long as certain latent parameters…

多层阈值法下的两样本高维协方差检验与信号识别AI 解读

The paper considers testing and signal identification for covariance matrices from two populations of marginally sub-Gaussian distributed. A multi-level thresholding procedure is proposed for testing the equality of two high-dimensional covariance matrices, which is designed to detect sparse and faint differences between the covariances. A novel U-statistic composition is developed to establish the asymptotic distribution of the thresholding statistics in conjunction with…

含时变解释变量的持续时间模型：删失分位数回归两步估计AI 解读

Common duration models are characterized by strong homogeneity and thus are highly restrictive in allowing for how regressors affect the conditional duration distribution. In particular, the implied sign and relative marginal quantile effects remain the same over the entire range of the conditional duration distribution, which rules out general heterogeneous effects in duration data. Quantile regression, which offers a flexible and unified framework that allows for general…

扩展Roy模型的非参数识别与估计AI 解读

We propose a new identification method for the extended Roy model, in which the agents maximize their utility rather than just their outcome. We nonparametrically identify the joint distribution of potential outcomes, which is of great importance in causal inference. We exploit the extended Roy model structure and the monotonicity assumption but do not require any functional form assumption nor any support assumption. The identification is achieved by matching the…