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存在横截面依赖的面板数据模型筛估计AI 解读

In this paper we consider the problem of estimating semiparametric panel data models with cross section dependence, where the individual-specific regressors enter the model nonparametrically whereas the common factors enter the model linearly. We consider both heterogeneous and homogeneous regression relationships when both the time and cross-section dimensions are large. We propose sieve estimators for the nonparametric regression functions by extending Pesaran’s (2006)…

时变杠杆效应AI 解读

Vast empirical evidence points to the existence of a negative correlation, named ”leverage effect”, between shocks to variance and shocks to returns. We provide a nonparametric theory of leverage estimation in the context of a continuous-time stochastic volatility model with jumps in returns, jumps in variance, or both. Leverage is defined as a flexible function of the state of the firm, as summarized by the spot variance level. We show that its point-wise functional…

时间序列设定错误下动态面板模型的偏差AI 解读

We consider within-group estimation of higher-order autoregressive panel models with exogenous regressors and fixed effects, where the lag order is possibly misspecified. Even when disregarding the misspecification bias, the fixed-effect bias formula is quite different from the correctly specified case though its asymptotic order remains the same under the stationarity. We suggest bias reduction methods under the possible time series misspecification.

超越面板单位根检验：用多重检验识别各序列的非平稳性AI 解读

Most panel unit root tests are designed to test the joint null hypothesis of a unit root for each individual series in a panel. After a rejection, it will often be of interest to identify which series can be deemed to be stationary and which series can be deemed nonstationary. Researchers will sometimes carry out this classification on the basis of n individual (univariate) unit root tests based on some ad hoc significance level. In this paper, we suggest and demonstrate how…

面板回归中因子增广估计量的渐近分布AI 解读

In this paper we derive an asymptotic theory for linear panel regression augmented with estimated common factors. We give conditions under which the estimated factors can be used in place of the latent factors in the regression equation. For the principal components estimate of the factor space it is shown that these conditions are satisfied when T/N→0 and N/T3→0 under regularity. Monte Carlo studies verify the asymptotic theory.

派付收益率、风险与错误定价：贝叶斯分析AI 解读

We develop a simple parametric model in which hypotheses about predictability, mispricing, and the risk-return tradeoff can be evaluated simultaneously, while allowing for time variation in both risk and expected return. Most of the return predictability based on aggregate payout yield is unrelated to market risk. We consider a range of Bayesian prior beliefs about the risk-return tradeoff and the extent to which predictability is driven by mispricing. The impact of these…

对估计窗口大小选择稳健的样本外预测检验AI 解读

This article proposes new methodologies for evaluating economic models’ out-of-sample forecasting performance that are robust to the choice of the estimation window size. The methodologies involve evaluating the predictive ability of forecasting models over a wide range of window sizes. The study shows that the tests proposed in the literature may lack the power to detect predictive ability and might be subject to data snooping across different window sizes if used…

厚尾误差下的短记忆检验AI 解读

In this article, we consider the robustness to fat tails of four stationarity tests. We also consider their sensitivity to the number of lags used in long-run variance estimation, and the power of the tests. Lo's modified rescaled range (MR/S) test is not very robust. Choi's Lagrange multiplier (LM) test has excellent robustness properties but is not generally as powerful as the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test. As an analytical framework for fat tails, we…

时变维度模型AI 解读

Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this article proposes several Time Varying Dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different…

为什么采样频率会影响金融时间序列的单位根检验AI 解读

It is generally believed that the power of unit root tests is determined only by the time span of observations, not by their sampling frequency. We show that the sampling frequency does matter for stock data displaying fat tails and volatility clustering, such as financial time series. Our claim builds on recent work on unit root testing based on non-Gaussian GARCH-based likelihood functions. Such methods yield power gains in the presence of fat tails and volatility…

评估 Rothstein 对教师增值模型的批评AI 解读

Value-added models of teacher effectiveness yield consistent estimates of teacher quality under the assumption that students are randomly assigned to classrooms conditional on ability. Rothstein (2010) tested and rejected this underlying sorting assumption, casting doubt on the usefulness of the value-added framework. In this paper, I illustrate that the falsification tests employed by Rothstein perform quite poorly in small samples and I propose an alternative testing…

异方差与多工具变量下的工具变量估计AI 解读

This paper gives a relatively simple, well behaved solution to the problem of many instruments in heteroskedastic data. Such settings are common in microeconometric applications where many instruments are used to improve efficiency and allowance for heteroskedasticity is generally important. The solution is a Fuller (1977) like estimator and standard errors that are robust to heteroskedasticity and many instruments. We show that the estimator has finite moments and high…

随机有序生存函数的逐点非参数极大似然估计AI 解读

In this paper, we consider estimation of survivor functions from groups of observations with right-censored data when the groups are subject to a stochastic ordering constraint. Many methods and algorithms have been proposed to estimate distribution functions under such restrictions, but none have completely satisfactory properties when the observations are censored. We propose a pointwise constrained nonparametric maximum likelihood estimator, which is defined at each time…

缺失数据与因果推断模型中的改进双重稳健估计AI 解读

Recently proposed double-robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double-robust estimators under misspecification of the outcome model. In this paper, we derive a new class of double-robust estimators for the parameters of regression models with incomplete cross-sectional or longitudinal data, and of marginal structural mean models for cross-sectional data with…

类别数增长的随机区块模型AI 解读

We present asymptotic and finite-sample results on the use of stochastic blockmodels for the analysis of network data. We show that the fraction of misclassified network nodes converges in probability to zero under maximum likelihood fitting when the number of classes is allowed to grow as the root of the network size and the average network degree grows at least poly-logarithmically in this size. We also establish finite-sample confidence bounds on maximum-likelihood…

小区域估计的经验自助偏差校正与预测均方误差估计AI 解读

We develop a method for bias correction, which models the error of the target estimator as a function of the corresponding estimator obtained from bootstrap samples, and the original estimators and bootstrap estimators of the parameters governing the model fitted to the sample data. This is achieved by considering a number of plausible parameter values, generating a pseudo original sample for each parameter and bootstrap samples for each such sample, and then searching for…

暴露误分类下的加性交互作用推断AI 解读

Results are given concerning inferences that can be drawn about interaction when binary exposures are subject to certain forms of independent nondifferential misclassification. Tests for interaction, using the misclassified exposures, are valid provided the probability of misclassification satisfies certain bounds. Results are given for additive statistical interactions, for causal interactions corresponding to synergism in the sufficient cause framework and for so-called…

偏倚抽样数据下不变密度比模型的似然方法AI 解读

The full likelihood approach in statistical analysis is regarded as the most efficient means for estimation and inference. For complex length-biased failure time data, computational algorithms and theoretical properties are not readily available, especially when a likelihood function involves infinite-dimensional parameters. Relying on the invariance property of length-biased failure time data under the semiparametric density ratio model, we present two likelihood approaches…

全有或全无依从下随机试验治疗疗效的非参数推断AI 解读

To evaluate the biological efficacy of a treatment in a randomized clinical trial, one needs to compare patients in the treatment arm who actually received treatment with the subgroup of patients in the control arm who would have received treatment had they been randomized into the treatment arm. In practice, subgroup membership in the control arm is usually unobservable. This paper develops a nonparametric inference procedure to compare subgroup probabilities with…

环境序列样本的系统发育 Kantorovich–Rubinstein 距离AI 解读

Summary It is now common to survey microbial communities by sequencing nucleic acid material extracted in bulk from a given environment. Comparative methods are needed that indicate the extent to which two communities differ given data sets of this type. UniFrac, which gives a somewhat ad hoc phylogenetics-based distance between two communities, is one of the most commonly used tools for these analyses. We provide a foundation for such methods by establishing that, if we…

极值建模中阈值诊断与不确定性的似然方法AI 解读

Summary For extreme value modelling based on threshold techniques, a well-documented issue is the sensitivity of inference from the model to the choice of threshold. The threshold above which we assume a non-homogeneous Poisson process, or equivalently generalized Pareto representation, to be a reasonable approximation to the distribution is traditionally selected before analysis and subsequently treated as fixed and known. In doing so, the analyst cannot account for the…

对数据选择后参数的调整贝叶斯推断AI 解读

Summary We address the problem of providing inference from a Bayesian perspective for parameters selected after viewing the data. We present a Bayesian framework for providing inference for selected parameters, based on the observation that providing Bayesian inference for selected parameters is a truncated data problem. We show that if the prior for the parameter is non-informative, or if the parameter is a ‘fixed’ unknown constant, then it is necessary to adjust the…

因果中介分析的半参数理论：效率界、多重稳健性与敏感性分析AI 解读

Whilst estimation of the marginal (total) causal effect of a point exposure on an outcome is arguably the most common objective of experimental and observational studies in the health and social sciences, in recent years, investigators have also become increasingly interested in mediation analysis. Specifically, upon evaluating the total effect of the exposure, investigators routinely wish to make inferences about the direct or indirect pathways of the effect of the exposure…

基于稳健秩相关的变量筛选AI 解读

Independence screening is a variable selection method that uses a ranking criterion to select significant variables, particularly for statistical models with nonpolynomial dimensionality or “large $p$, small $n$” paradigms when $p$ can be as large as an exponential of the sample size $n$. In this paper we propose a robust rank correlation screening (RRCS) method to deal with ultra-high dimensional data. The new procedure is based on the Kendall $\tau$ correlation coefficient…