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高维协变量非线性交互的广义加性系数模型估计与推断AI 解读

In the low-dimensional case, the generalized additive coefficient model (GACM) proposed by Xue and Yang [Statist. Sinica 16 (2006) 1423–1446] has been demonstrated to be a powerful tool for studying nonlinear interaction effects of variables. In this paper, we propose estimation and inference procedures for the GACM when the dimension of the variables is high. Specifically, we propose a groupwise penalization based procedure to distinguish significant covariates for the…

回归函数单点的自适应检验AI 解读

We consider the problem of inference on a regression function at a point when the entire function satisfies a sign or shape restriction under the null. We propose a test that achieves the optimal minimax rate adaptively over a range of Hölder classes, up to a $\log\log n$ term, which we show to be necessary for adaptation. We apply the results to adaptive one-sided tests for the regression discontinuity parameter under a monotonicity restriction, the value of a monotone…

单调函数的非参数置信区间AI 解读

We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the treatment of other models with monotone functions, and demonstrate our method with a new proof of the results of Banerjee–Wellner [Ann. Statist. 29 (2001) 1699–1731] and also by…

采用稀疏先验的贝叶斯线性回归AI 解读

We study full Bayesian procedures for high-dimensional linear regression under sparsity constraints. The prior is a mixture of point masses at zero and continuous distributions. Under compatibility conditions on the design matrix, the posterior distribution is shown to contract at the optimal rate for recovery of the unknown sparse vector, and to give optimal prediction of the response vector. It is also shown to select the correct sparse model, or at least the coefficients…

重复测量结果的函数单指标广义部分线性模型：核与样条融合平滑AI 解读

We propose a generalized partially linear functional single index risk score model for repeatedly measured outcomes where the index itself is a function of time. We fuse the nonparametric kernel method and regression spline method, and modify the generalized estimating equation to facilitate estimation and inference. We use local smoothing kernel to estimate the unspecified coefficient functions of time, and use B-splines to estimate the unspecified function of the single…

密度脊的渐近理论AI 解读

The large sample theory of estimators for density modes is well understood. In this paper we consider density ridges, which are a higher-dimensional extension of modes. Modes correspond to zero-dimensional, local high-density regions in point clouds. Density ridges correspond to $s$-dimensional, local high-density regions in point clouds. We establish three main results. First we show that under appropriate regularity conditions, the local variation of the estimated ridge…

多样本对齐稀疏信号的最优检测AI 解读

We describe, in the detection of multi-sample aligned sparse signals, the critical boundary separating detectable from nondetectable signals, and construct tests that achieve optimal detectability: penalized versions of the Berk–Jones and the higher-criticism test statistics evaluated over pooled scans, and an average likelihood ratio over the critical boundary. We show in our results an inter-play between the scale of the sequence length to signal length ratio, and the…

通过 Knockoff 变量控制错误发现率AI 解读

In many fields of science, we observe a response variable together with a large number of potential explanatory variables, and would like to be able to discover which variables are truly associated with the response. At the same time, we need to know that the false discovery rate (FDR)—the expected fraction of false discoveries among all discoveries—is not too high, in order to assure the scientist that most of the discoveries are indeed true and replicable. This paper…

数据驱动的平滑对称性检验AI 解读

In this paper we propose a data driven smooth test of symmetry. We first transform the raw data via the probability integral transformation according to a symmetrized empirical distribution, and show that under the null hypothesis of symmetry, the transformed data has a limiting uniform distribution, reducing testing for symmetry to testing for uniformity. Employing Neyman’s smooth test of uniformity, we show that only odd-ordered orthogonal moments of the transformed data…

利用分位数进行面板非参数识别AI 解读

This paper considers identification and estimation of ceteris paribus effects of continuous regressors in nonseparable panel models with time homogeneity. The effects of interest are derivatives of the average and quantile structural functions of the model. We find that these derivatives are identified with two time periods for “stayers”, i.e. for individuals with the same regressor values in two time periods. We show that the identification results carry over to models that…

具有固定效应的异质面板半参数模型AI 解读

This paper develops methodology for semiparametric panel data models in a setting where both the time series and the cross section are large. Such settings are common in finance and other areas of economics. Our model allows for heterogeneous nonparametric covariate effects as well as unobserved time and individual specific effects that may depend on the covariates in an arbitrary way. To model the covariate effects parsimoniously, we impose a dimensionality reducing common…

弱依赖与弱条件下级数估计量的最优一致收敛率及渐近正态性AI 解读

We show that spline and wavelet series regression estimators for weakly dependent regressors attain the optimal uniform (i.e. sup-norm) convergence rate (n/logn)−p/(2p+d) of Stone (1982), where d is the number of regressors and p is the smoothness of the regression function. The optimal rate is achieved even for heavy-tailed martingale difference errors with finite (2+(d/p))th absolute moment for d/p<2. We also establish the asymptotic normality of t statistics for possibly…

处理效应估计：中国2008年经济刺激计划案例AI 解读

Researchers often face the challenge of estimating the counterfactuals to evaluate the treatment effects. Hsiao et al. (2012) propose a method that offers more flexibilities by allowing the influence of the unobservable latent factors to vary cross-section. This paper relaxes the linear conditional mean assumption in their method by extending it to a semi-parametric setting. The asymptotic distribution properties of the average treatment effect estimator is derived and…

含弱外生解释变量的异质动态面板共同相关效应估计AI 解读

This paper extends the Common Correlated Effects (CCE) approach developed by Pesaran (2006) to heterogeneous panel data models with lagged dependent variables and/or weakly exogenous regressors. We show that the CCE mean group estimator continues to be valid but the following two conditions must be satisfied to deal with the dynamics: a sufficient number of lags of cross section averages must be included in individual equations of the panel, and the number of cross section…

短面板固定效应删失分位数回归模型的集合识别AI 解读

This paper studies identification and estimation of a censored quantile regression model for short panel data with fixed effects. Using the redistribution-of-mass idea, we obtain bounds on the conditional distribution of differences of the model across periods, under conditional quantile restrictions together with a weak conditional independence assumption along the lines of Rosen (2012). The inversion of the distribution bounds characterizes the sharp identified set via a…

固定效应非参数面板模型中的误差序列相关检验AI 解读

In this paper we consider the problem of testing serial correlation in fixed effects panel data model in a nonparametric framework. Using asymptotic results developed in Su and Lu (2013), we show that our test statistic has a standard normal distribution under the null hypothesis of zero serial correlation. The test statistic diverges to infinity at the rate of N under the alternative hypothesis that error is serially correlated, where N is the cross sectional sample size.…

二元响应相关随机系数面板模型AI 解读

In this paper, we consider binary response correlated random coefficient (CRC) panel data models which are frequently used in the analysis of treatment effects and demand of products. We focus on the nonparametric identification and estimation of panel data models under unobserved heterogeneity which is captured by random coefficients and when these random coefficients are correlated with regressors. Our identification conditions and estimation are based on the framework of…

消费中的双侧幂律及其对欧拉方程检验的影响AI 解读

We provide evidence suggesting that the cross-sectional distributions of US consumption and its growth rate obey the power law in both the upper and lower tails, with exponents approximately equal to four. Consequently, high-order moments are unlikely to exist, and the generalized method of moments estimation of Euler equations that employs cross-sectional moments may be inconsistent. Through bootstrap studies, we find that the power law appears to generate spurious…

动态 Copula 的隐马尔可夫结构AI 解读

Understanding the time series dynamics of a multi-dimensional dependency structure is a challenging task. Multivariate covariance driven Gaussian or mixed normal time varying models have only a limited ability to capture important features of the data such as heavy tails, asymmetry, and nonlinear dependencies. The present paper tackles this problem by proposing and analyzing a hidden Markov model (HMM) for hierarchical Archimedean copulae (HAC). The HAC constitute a wide…

连续处理效应是否随处理水平变化的检验AI 解读

This paper provides nonparametric tests for hypotheses about the effects of a continuous treatment variable in a nonseparable structural equation. Specifically, we consider local average responses and average partial effects and test whether these effects are identical across different levels of treatment, including testing whether they are zero for all treatment levels as a special case. Our tests are based on consistent procedures of Bierens (1982, Journal of Econometrics…

非平稳解释变量预测回归中的不稳定性检验AI 解读

It is well known that allowing the coefficients to be time-varying in a predictive model with possibly nonstationary regressors can help to deal with instability in predictability associated with linear predictive models. In this paper, an L 2 -type test statistic is proposed to test the stability of the coefficient vector, and the asymptotic distributions of the proposed test statistic are developed under both null and alternative hypotheses. A Monte Carlo experiment is…

非线性自回归模型的参数设定检验AI 解读

The paper considers testing parametric assumptions on the conditional mean and variance functions for nonlinear autoregressive models. To this end, we compare the kernel density estimate of the marginal density of the process with a convolution-type density estimate. It is shown that, interestingly, the latter estimate has a parametric $\left( {\sqrt n } \right)$ rate of convergence, thus substantially improving the classical kernel density estimates whose rates of…

稳健广义矩估计的最优带宽选择AI 解读

A two-step generalized method of moments estimation procedure can be made robust to heteroskedasticity and autocorrelation in the data by using a nonparametric estimator of the optimal weighting matrix. This paper addresses the issue of choosing the corresponding smoothing parameter (or bandwidth) so that the resulting point estimate is optimal in a certain sense. We derive an asymptotically optimal bandwidth that minimizes a higher-order approximation to the asymptotic…

动态因子模型中的参数不稳定性检验AI 解读

In this paper, we develop tests for structural breaks of factor loadings in dynamic factor models. We focus on the joint null hypothesis that all factor loadings are constant over time. Because the number of factor loading parameters goes to infinity as the sample size grows, conventional tests cannot be used. Based on the fact that the presence of a structural change in factor loadings yields a structural change in second moments of factors obtained from the full sample…