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处理效应异质性依赖未观测固定效应时的面板平均处理效应估计AI 解读

Summary This paper proposes a new panel data approach to identify and estimate the time‐varying average treatment effect (ATE). The approach allows for treatment effect heterogeneity that depends on unobserved fixed effects. In the presence of this type of heterogeneity, existing panel data approaches identify the ATE for limited subpopulations only. In contrast, the proposed approach identifies and estimates the ATE for the entire population. The approach relies on the…

发达与新兴经济体实际活动的共同波动：全球与群组特有国际因子的检验AI 解读

Summary Although globalization has shaped the world economy in recent decades, emerging economies have experienced impressive growth compared to developed economies, suggesting specific comovements within developed and emerging business cycles. Using observed developed and emerging real economy activity variables, we investigate whether the latter assertion can be supported by observed data. Based on a two‐level factor model, we assume these activity variables can be…

《粒子滤波中的方差估计》勘误AI 解读

Biometrika (2018), 105, pp. 609–25. There are missing curly braces in (4), which should read The displays in Remark 1 should consequently be changed to and In addition the sentence immediately after (5) should be changed to: Observe the resemblance between the right-hand sides of (4) and (5): the role of |$X_{i}$| is played by |$\sum_{j:E_{n}^{j}=i}\varphi(\zeta_{n}^{j})$|⁠, the sum of |$\varphi$| evaluated at the descendants of |$\zeta_{0}^{i}$|⁠. The consequences of this…

贝叶斯约束松弛AI 解读

Summary Prior information often takes the form of parameter constraints. Bayesian methods include such information through prior distributions having constrained support. By using posterior sampling algorithms, one can quantify uncertainty without relying on asymptotic approximations. However, sharply constrained priors are not necessary in some settings and tend to limit modelling scope to a narrow set of distributions that are tractable computationally. We propose to…

无模型方法量化替代标志物解释的治疗效应比例AI 解读

Summary In randomized clinical trials, the primary outcome, $Y$, often requires long-term follow-up and/or is costly to measure. For such settings, it is desirable to use a surrogate marker, $S$, to infer the treatment effect on $Y$, $\Delta$. Identifying such an $S$ and quantifying the proportion of treatment effect on $Y$ explained by the effect on $S$ are thus of great importance. Most existing methods for quantifying the proportion of treatment effect are model based and…

多层网络数据中的一致社区检测AI 解读

Summary We consider multi-layer network data where the relationships between pairs of elements are reflected in multiple modalities, and may be described by multivariate or even high-dimensional vectors. Under the multi-layer stochastic block model framework we derive consistency results for a least squares estimation of memberships. Our theorems show that, as compared to single-layer community detection, a multi-layer network provides much richer information that allows for…

宏基因组数据中细菌组成矩阵的多样本估计AI 解读

Summary Metagenomics sequencing is routinely applied to quantify bacterial abundances in microbiome studies, where bacterial composition is estimated based on the sequencing read counts. Due to limited sequencing depth and DNA dropouts, many rare bacterial taxa might not be captured in the final sequencing reads, which results in many zero counts. Naive composition estimation using count normalization leads to many zero proportions, which tend to result in inaccurate…

利用逻辑高斯过程的条件密度分区估计模型AI 解读

Summary Conditional density estimation seeks to model the distribution of a response variable conditional on covariates. We propose a Bayesian partition model using logistic Gaussian processes to perform conditional density estimation. The partition takes the form of a Voronoi tessellation and is learned from the data using a reversible jump Markov chain Monte Carlo algorithm. The methodology models data in which the density changes sharply throughout the covariate space,…

模型误设与高维数据下倾向得分的正则化校准估计AI 解读

Summary Propensity scores are widely used with inverse probability weighting to estimate treatment effects in observational studies. We study calibrated estimation as an alternative to maximum likelihood estimation for fitting logistic propensity score models. We show that, with possible model misspecification, minimizing the expected calibration loss underlying the calibrated estimators involves reducing both the expected likelihood loss and a measure of relative errors…

同时实现降秩与变量选择的贝叶斯稀疏多响应回归AI 解读

Summary We develop a Bayesian methodology aimed at simultaneously estimating low-rank and row-sparse matrices in a high-dimensional multiple-response linear regression model. We consider a carefully devised shrinkage prior on the matrix of regression coefficients which obviates the need to specify a prior on the rank, and shrinks the regression matrix towards low-rank and row-sparse structures. We provide theoretical support to the proposed methodology by proving minimax…

二元工具变量模型中的测量误差AI 解读

Summary Instrumental variable methods can identify causal effects even when the treatment and outcome are confounded. We study the problem of imperfect measurements of the binary instrumental variable, treatment and outcome. We first consider nondifferential measurement errors, that is, the mismeasured variable does not depend on other variables given its true value. We show that the measurement error of the instrumental variable does not bias the estimate, that the…

含滞后因变量的离散选择模型的非参数识别AI 解读

Variation in covariates can be used to nonparametrically identify a discrete choice model with a lagged dependent variable and discrete unobserved heterogeneity (Kasahara and Shimotsu, 2009; Browning and Carro, 2014). In some cases the number of support points of the unobserved heterogeneity distribution is restricted only by the number of points of support in the distribution of the covariates. This paper provides conditions under which these models can be identified with…

混合随机局部单位根模型AI 解读

Two approaches have dominated formulations designed to capture small departures from unit root autoregressions. The first involves deterministic departures that include local-to-unity (LUR) and mildly (or moderately) integrated (MI) specifications where departures shrink to zero as the sample size n→∞. The second approach allows for stochastic departures from unity, leading to stochastic unit root (STUR) specifications. This paper introduces a hybrid local stochastic unit…

从平稳到轻度非平稳变化的序贯监测AI 解读

We develop and study sequential testing procedures á la Chu et al. (1996) for on-line detection of changes in a time series from stationarity to mild forms of non-stationarity. The proposed tests are based on sequential CUSUM and KPSS-type detector processes, and are shown to provide consistent detection under a wide range of change point models, including changes in the parameters of ARMA and GARCH series from values within the model’s stationarity parameter region to…

含内生性的删失回归模型的半参数估计AI 解读

Censoring and endogeneity are common in empirical applications. However, the existing semiparametric estimation methods for the censored regression model with endogeneity under an independence restriction are associated with some drawbacks. In this paper we propose a new semiparametric estimator that overcomes these drawbacks. We derive conditional quantile moment conditions for all the conditional quantiles and propose a moment-based estimator. In particular, we construct…

无原地不变个体的时变非可分面板模型识别与估计AI 解读

This paper explores the identification and estimation of nonseparable panel data models. We show that the structural function is nonparametrically identified when it is strictly increasing in a scalar unobservable variable, the conditional distributions of unobservable variables do not change over time, and the joint support of explanatory variables satisfies some weak assumptions. To identify the target parameters, existing studies assume that the structural function does…

测量误差下的分布函数推断AI 解读

This paper is concerned with inference on the cumulative distribution function (cdf) FX∗ in the classical measurement error model X=X∗+ϵ. We consider the case where the density of the measurement error ϵ is unknown and estimated by repeated measurements, and show validity of a bootstrap approximation for the distribution of the deviation in the sup-norm between the deconvolution cdf estimator and FX∗. We allow the density of ϵ to be ordinary or super smooth. We also provide…

含零波动系数的增广双重自回归模型的非标准推断AI 解读

This paper considers an augmented double autoregressive (DAR) model, which allows null volatility coefficients to circumvent the over-parameterization problem in the DAR model. Since the volatility coefficients might be on the boundary, the statistical inference methods based on the Gaussian quasi-maximum likelihood estimation (GQMLE) become non-standard, and their asymptotics require the data to have a finite sixth moment, which narrows the applicable scope in studying…

基于重拟合交叉验证的超高维精度矩阵估计AI 解读

This paper develops a new estimation procedure for ultrahigh dimensional sparse precision matrix, the inverse of covariance matrix. Regularization methods have been proposed for sparse precision matrix estimation, but they may not perform well with ultrahigh dimensional data due to the spurious correlation. We propose a refitted cross validation (RCV) method for sparse precision matrix estimation based on its Cholesky decomposition, which does not require the Gaussian…

基于鞅变换的Copula拟合优度检验AI 解读

This paper proposes an asymptotically distribution-free test for copulas with dynamic marginal distributions, such as GARCH and ARMA processes. The test is based on the empirical copula process with parametrically estimated marginal distributions. By applying the Khmaladze (1982, 1988, 1993) martingale transformation method, the transformed empirical process converges to a standard Gaussian process, so the resulting test statistics are asymptotically distribution-free. Monte…

面板数据模型中个体效应与时间效应的判定AI 解读

In this paper we propose a jackknife method to determine individual and time effects in linear panel data models. We first show that when both the serial and cross-sectional correlations among the idiosyncratic error terms are weak, our jackknife method can pick up the correct model with probability approaching one (w.p.a.1). In the presence of moderate or strong degree of serial correlation, we modify our jackknife criterion function and show that the modified jackknife…

建模结构突变能否提高预测准确性？AI 解读

Mean square forecast error loss implies a bias–variance trade-off that suggests that structural breaks of small magnitude should be ignored. In this paper, we provide a test to determine whether modeling a structural break improves forecast accuracy. The test is near optimal even when the date of a local-to-zero break is not consistently estimable. The results extend to forecast combinations that weight the post-break sample and the full sample forecasts by our test…

高频局部分布推断：自助法方法AI 解读

We study inference for the local innovations of Itô semimartingales. Specifically, we construct a resampling procedure for the empirical CDF of high-frequency innovations that have been standardized using a nonparametric estimate of its stochastic scale (volatility) and truncated to rid the effect of “large” jumps. Our locally dependent wild bootstrap (LDWB) accommodate issues related to the stochastic scale and jumps as well as account for a special block-wise dependence…

大都市住房市场均衡模型估计的新方法AI 解读

We provide a new estimator for a broad class of equilibrium models of metropolitan housing markets with housing differentiated by quality. Quality is a latent variable that captures all features of a dwelling and its environment. We estimate the model for Chicago and New York, obtaining hedonic housing price functions for each quality level for each metropolitan area, stocks of each quality, and compensating variations required for a household of a given income in Chicago to…