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回归调整近似贝叶斯计算的收敛性AI 解读

We present asymptotic results for the regression-adjusted version of approximate Bayesian computation introduced by Beaumont et al. (2002). We show that for an appropriate choice of the bandwidth, regression adjustment will lead to a posterior that, asymptotically, correctly quantifies uncertainty. Furthermore, for such a choice of bandwidth we can implement an importance sampling algorithm to sample from the posterior whose acceptance probability tends to unity as the data…

估计的 Wold 表示与谱密度驱动的时间序列自助法AI 解读

Summary The second-order dependence structure of purely non-deterministic stationary processes is described by the coefficients of the famous Wold representation. These coefficients can be obtained by factorizing the spectral density of the process. This relationship together with some spectral density estimator is used to obtain consistent estimators of these coefficients. A spectral-density-driven bootstrap for time series is then developed which uses the entire sequence…

《多元回归中降秩估计量的自由度》证明缺陷说明AI 解读

Biometrika (2015), 102, pp. 457–77 The argument given in the paper that Stein’s formula can be applied to the reduced-rank regression estimator is unfortunately incomplete. A full proof may be found in Hansen (2017). We thank Niels Richard Hansen for pointing out a deficiency in the proof in the paper.

《可逆指数模型的简单估计量》讨论AI 解读

This is an interesting article that considers the question of inference on unknown linear index coefficients in a general class of models where reduced form parameters are invertible function of one or more linear index. Interpretable sufficient conditions such as monotonicity and or smoothness for the invertibility condition are provided. The results generalize some work in the previous literature by allowing the number of reduced form parameters to exceed the number of…

可逆指标模型的简易估计量AI 解读

This article considers estimation of the unknown linear index coefficients of a model in which a number of nonparametrically identified reduced form parameters are assumed to be smooth and invertible function of one or more linear indices. The results extend the previous literature by allowing the number of reduced form parameters to exceed the number of indices (i.e., the indices are “overdetermined” by the reduced form parameters. The estimator of the unknown index…

对《可逆指标模型的简易估计量》的评论AI 解读

The approach described by Ahn et al. (this issue) (henceforth AIPR) constitutes an elegant contribution to the “control function” literature, which focuses on semiparametric models where endogeneit...

序列相关数据下 ROC 曲线的置信带AI 解读

We propose serial correlation-robust asymptotic confidence bands for the receiver operating characteristic (ROC) curve and its functional, viz., the area under ROC curve (AUC), estimated by quasi-maximum likelihood in the binormal model. Our simulation experiments confirm that this new method performs fairly well in finite samples, and confers an additional measure of robustness to nonnormality. The conventional procedure is found to be markedly undersized in terms of…

利用自距离相关矩阵检验多元时间序列的独立性AI 解读

SUMMARYWe introduce the matrix multivariate auto-distance covariance and correlation functions for time series, discuss their interpretation and develop consistent estimators for practical implementation. We also develop a test of the independent and identically distributed hypothesis for multivariate time series data and show that it performs better than the multivariate Ljung–Box test. We discuss computational aspects and present a data example to illustrate the method.

近似贝叶斯计算估计量的渐近效率AI 解读

Many statistical applications involve models for which it is difficult to evaluate the likelihood, but from which it is relatively easy to sample. Approximate Bayesian computation is a likelihood-free method for implementing Bayesian inference in such cases. We present results on the asymptotic variance of estimators obtained using approximate Bayesian computation in a large data limit. Our key assumption is that the data are summarized by a fixed-dimensional summary…

对偶回归AI 解读

We propose dual regression as an alternative to quantile regression for the global estimation of conditional distribution functions. Dual regression provides the interpretational power of quantile regression while avoiding the need to repair intersecting conditional quantile surfaces. We introduce a mathematical programming characterization of conditional distribution functions which, in its simplest form, is the dual program of a simultaneous estimator for linear…

基于软阈值高斯过程的标量对图像回归AI 解读

This work concerns spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models and develop an efficient posterior computational aigorithm. The proposed soft-thresholded Gaussian process provides large prior support over the class of piecewise-smooth, sparse, and continuous spatially-varying regression coefficient functions. In addition, under some mild regularity conditions the soft-thresholded Gaussian proess prior…

随机网络、图模型与可交换性AI 解读

Summary We study conditional independence relationships for random networks and their interplay with exchangeability. We show that, for finitely exchangeable network models, the empirical subgraph densities are maximum likelihood estimates of their theoretical counterparts. We then characterize all possible Markov structures for finitely exchangeable random graphs, thereby identifying a new class of Markov network models corresponding to bidirected Kneser graphs. In…

多层因子模型：识别、渐近理论与应用AI 解读

Summary This paper studies a multilevel factor model with global and country factors. The global factors affect all individuals, whereas the country factors affect only those within each specific country. A sequential procedure to identify the global and country factors separately is proposed. In the initial step, the global factors are estimated by canonical correlation analysis. Using this initial estimator, the principal component estimators (PCEs) of the global and…

用于GMM的广义聚焦信息准则AI 解读

Summary This paper proposes a criterion for simultaneous generalized method of moments model and moment selection: the generalized focused information criterion (GFIC). Rather than attempting to identify the “true” specification, the GFIC chooses from a set of potentially misspecified moment conditions and parameter restrictions to minimize the mean squared error (MSE) of a user‐specified target parameter. The intent of the GFIC is to formalize a situation common in applied…

已实现极端分位数：结合极值理论的条件分位数与波动率测度联合模型AI 解读

Summary We propose a new framework exploiting realized measures of volatility to estimate and forecast extreme quantiles. Our realized extreme quantile (REQ) combines quantile regression with extreme value theory and uses a measurement equation that relates the realized measure to the latent conditional quantile. Model estimation is performed by quasi maximum likelihood, and a simulation experiment validates this estimator in finite samples. An extensive empirical analysis…

Summary Many contemporary large-scale applications involve building interpretable models linking a large set of potential covariates to a response in a non-linear fashion, such as when the response is binary. Although this modelling problem has been extensively studied, it remains unclear how to control the fraction of false discoveries effectively even in high dimensional logistic regression, not to mention general high dimensional non-linear models. To address such a…

如何选择合并 p 值的方法AI 解读

Combining |$p$|-values from independent statistical tests is a popular approach to meta-analysis, particularly when the data underlying the tests are either no longer available or are difficult to combine. Numerous |$p$|-value combination methods appear in the literature, each with different statistical properties, yet often the final choice used in a meta-analysis can seem arbitrary, as if all effort has been expended in building the models that gave rise to the |$p$|-values…

偏差减少与附带参数AI 解读

Firth (1993) introduced a method for reducing the bias of the maximum likelihood estimator. Here it is shown that the approach is also effective in reducing the sensitivity of inferential procedures to incidental parameters.

高频数据中的相互激发跳跃与金融避险迁移检验AI 解读

We propose a new nonparametric test to identify mutually exciting jumps in high frequency data. We derive the asymptotic properties of the test statistics and show that the tests have good size and reasonable power in finite sample cases. Using our mutual excitation tests, we empirically characterize the dynamics of financial flights in forms of flight-to-safety and flight-to-quality. The results indicate that mutually exciting jumps and risk-off trades mostly occur in…

ZD-GARCH 模型：研究异方差的新方法AI 解读

This paper proposes a first-order zero-drift GARCH (ZD-GARCH(1, 1)) model to study conditional heteroscedasticity and heteroscedasticity together. Unlike the classical GARCH model, the ZD-GARCH(1, 1) model is always non-stationary regardless of the sign of the Lyapunov exponent γ0, but interestingly it is stable with its sample path oscillating randomly between zero and infinity over time when γ0=0. Furthermore, this paper studies the generalized quasi-maximum likelihood…

检验两个对称正定矩阵相等的勾股式推广AI 解读

We provide a new test for equality of two symmetric positive-definite matrices that leads to a convenient mechanism for testing specification using the information matrix equality or the sandwich asymptotic covariance matrix of the GMM estimator. The test relies on a new characterization of equality between two k dimensional symmetric positive-definite matrices A and B: the traces of AB−1 and BA−1 are equal to k if and only if A=B. Using this simple criterion, we introduce a…

增长维度无条件矩模型的指数倾斜似然推断AI 解读

Growing-dimensional data with likelihood function unavailable are often encountered in various fields. This paper presents a penalized exponentially tilted (PET) likelihood for variable selection and parameter estimation for growing dimensional unconditional moment models in the presence of correlation among variables and model misspecification. Under some regularity conditions, we investigate the consistent and oracle properties of the PET estimators of parameters, and show…

基于 ε 污染的稳健线性静态面板模型AI 解读

The paper develops a general Bayesian framework for robust linear static panel data models usingε-contamination. A two-step approach is employed to derive the conditional type-II maximum likelihood (ML-II) posterior distribution of the coefficients and individual effects. The ML-II posterior means are weighted averages of the Bayes estimator under a base prior and the data-dependent empirical Bayes estimator. Two-stage and three stage hierarchy estimators are developed and…

丰富数据下向量自回归移动平均模型的估计与预测AI 解读

We address the issue of modelling and forecasting macroeconomic variables using rich datasets by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (IOLS) estimator. We establish the consistency and asymptotic distribution of the estimator for weak and strong VARMA(p,q) models. Monte Carlo results show that IOLS is consistent and…