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不可忽略缺失数据的半参数逆倾向加权AI 解读

Abstract To estimate unknown population parameters based on data having nonignorable missing values with a semiparametric exponential tilting propensity, Kim & Yu (2011) assumed that the tilting parameter is known or can be estimated from external data, in order to avoid the identifiability issue. To remove this serious limitation on the methodology, we use an instrument, i.e., a covariate related to the study variable but unrelated to the missing data propensity, to…

高维时间序列中多个图模型的联合估计AI 解读

Summary We consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which consists of T possibly dependent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of closeness between subjects. We propose a kernel-based method for jointly estimating all graphical models. Theoretically, under a double asymptotic framework,…

通过数据扰动改善非参数密度估计的形态与精度AI 解读

Summary Motivated by both the shortcomings of high order density estimators, and the increasingly large data sets in many areas of modern science, we introduce new high order, non-parametric density estimators that are guaranteed to be positive and do not have highly oscillatory tails. Our approach is based on data perturbation, e.g. by tilting or data sharpening. It leads to new estimators that are more accurate than conventional kernel techniques that use positive kernels,…

函数系数模型的筛工具变量分位数回归估计AI 解读

In this paper we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We estimate the functional coefficients by the sieve-IVQR technique and establish the uniform consistency and asymptotic normality of the estimators. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the…

方程两侧均存在测量误差的非参数模型AI 解读

Measurement errors are often correlated, as in surveys where respondent’s biases or tendencies to err affect multiple reported variables. We extend Schennach (2007) to identify moments of the conditional distribution of a true Y given a true X when both are measured with error, the measurement errors in Y and X are correlated, and the true unknown model of Y given X has nonseparable model errors. After showing nonparametric identification, we provide a sieve generalized…

用分位数检验多元经济限制：斯勒茨基矩阵负半定性AI 解读

This paper is concerned with testing a core economic restriction, negative semidefiniteness of the Slutsky matrix. We consider a system of nonseparable structural equations with infinite dimensional unobservables, and employ quantile regression methods because they allow us to utilize the entire distribution of the data. Difficulties arise because the restriction involves several equations, while the quantile is a univariate concept. We establish that we may use quantiles of…

样条估计量的统一框架AI 解读

This article develops a unified framework to study the asymptotic properties of all periodic spline-based estimators, that is, of regression, penalized and smoothing splines. The explicit form of the periodic Demmler–Reinsch basis in terms of exponential splines allows the derivation of an expression for the asymptotic equivalent kernel on the real line for all spline estimators simultaneously. The corresponding bandwidth, which drives the asymptotic behaviour of spline…

识别消费变动的独立来源AI 解读

Summary By representing a system of budget shares as an approximate factor model we determine its rank, i.e. the number of common functional forms or factors, and we estimate a base of the factor space by means of approximate principal components. We assume that the extracted factors span the same space of basic Engel curves representing the fundamental forces driving consumers' behaviour. We identify these curves by imposing statistical independence and by studying their…

定价核单调性的实证检验AI 解读

Summary A large class of asset pricing models predicts that securities which have high payoffs when market returns are low tend to be more valuable than those with high payoffs when market returns are high. More generally, we expect the projection of the stochastic discount factor on the market portfolio—that is, the discounted pricing kernel evaluated at the market portfolio—to be a monotonically decreasing function of the market portfolio. Numerous recent empirical studies…

超高维部分线性可加分位数回归AI 解读

We consider a flexible semiparametric quantile regression model for analyzing high dimensional heterogeneous data. This model has several appealing features: (1) By considering different conditional quantiles, we may obtain a more complete picture of the conditional distribution of a response variable given high dimensional covariates. (2) The sparsity level is allowed to be different at different quantile levels. (3) The partially linear additive structure accommodates…

因子模型中的投影主成分分析AI 解读

This paper introduces a Projected Principal Component Analysis (Projected-PCA), which employees principal component analysis to the projected (smoothed) data matrix onto a given linear space spanned by covariates. When it applies to high-dimensional factor analysis, the projection removes noise components. We show that the unobserved latent factors can be more accurately estimated than the conventional PCA if the projection is genuine, or more precisely, when the factor…

低密度区域的自适应估计及其支撑恢复应用AI 解读

A scheme for locally adaptive bandwidth selection is proposed which sensitively shrinks the bandwidth of a kernel estimator at lowest density regions such as the support boundary which are unknown to the statistician. In case of a Hölder continuous density, this locally minimax-optimal bandwidth is shown to be smaller than the usual rate, even in case of homogeneous smoothness. Some new type of risk bound with respect to a density-dependent standardized loss of this…

存在内生选择与固定效应的变系数面板模型AI 解读

This paper considers a flexible panel data sample selection model in which (i) the outcome equation is permitted to take a semiparametric, varying coefficient form to capture potential parameter heterogeneity in the relationship of interest, (ii) both the outcome and (parametric) selection equations contain unobserved fixed effects and (iii) selection is generalized to a polychotomous case. We propose a two-stage estimator. Given consistent parameter estimates from the…

空间依赖稳健非参数协方差矩阵估计的 fixed-b 渐近理论AI 解读

This paper develops a method for performing inference using spatially dependent data. We consider test statistics formed using nonparametric covariance matrix estimators that account for heteroskedasticity and spatial correlation (spatial HAC). We provide distributions of commonly used test statistics under “fixed- b ” asymptotics, in which HAC smoothing parameters are proportional to the sample size. Under this sequence, spatial HAC estimators are not consistent but…

非平稳数据的非参数变换回归AI 解读

We examine a kernel regression estimator for time series that takes account of the error correlation structure as proposed by Xiao et al. (2003, Journal of the American Statistical Association 98, 980–992). We show that this method continues to improve estimation in the case where the regressor is a unit root or a near unit root process.

多个二元密度函数的联合估计及其在蛋白质环建模中的应用AI 解读

This article develops a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs using a data-driven, shared basis that is constructed by bivariate spline functions defined on a triangulation of the bivariate domain. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles. Maximum penalized likelihood is used to fit the model and…

分位数回归中变系数的估计与检验AI 解读

In this article, we establish a novel connection between the null hypothesis H0 on the coefficients and a rank-reducible form of the varying coefficient model in quantile regression. We use B-splines to approximate the varying coefficients in the rank-reducible model, and make use of the fact that the null hypothesis H0 implies a unidimensional structure of a transformed coefficient matrix for the B-spline basis functions. By evaluating the unidimensional structure, we…

高维线性回归中的样条 LassoAI 解读

We consider a high-dimensional linear regression problem, where the covariates (features) are ordered in some meaningful way, and the number of covariates p can be much larger than the sample size n. The fused lasso of Tibshirani et al. is designed especially to tackle this type of problems; it yields sparse coefficients and selects grouped variables, and encourages local constant coefficient profile within each group. However, in some applications, the effects of different…

非同步含噪资产价格的平顶已实现核二次协变差估计AI 解读

This article develops a general multivariate additive noise model for synchronized asset prices and provides a multivariate extension of the generalized flat-top realized kernel estimators, analyzed earlier by Varneskov (2014), to estimate its quadratic covariation. The additive noise model allows for α-mixing dependent exogenous noise, random sampling, and an endogenous noise component that encompasses synchronization errors, lead-lag relations, and diurnal…

密度众数的非参数推断AI 解读

Summary We derive non-parametric confidence intervals for the eigenvalues of the Hessian at modes of a density estimate. This provides information about the strength and shape of modes and can also be used as a significance test. We use a data-splitting approach in which potential modes are identified by using the first half of the data and inference is done with the second half of the data. To obtain valid confidence sets for the eigenvalues, we use a bootstrap based on an…

中等高维支持向量机的变量选择AI 解读

Summary The support vector machine (SVM) is a powerful binary classification tool with high accuracy and great flexibility. It has achieved great success, but its performance can be seriously impaired if many redundant covariates are included. Some efforts have been devoted to studying variable selection for SVMs, but asymptotic properties, such as variable selection consistency, are largely unknown when the number of predictors diverges to ∞. We establish a unified theory…

使用受约束多项式样条拟合数据包络AI 解读

Summary Estimation of support frontiers and boundaries often involves monotone and/or concave edge data smoothing. This estimation problem arises in various unrelated contexts, such as optimal cost and production assessments in econometrics and master curve prediction in the reliability programmes of nuclear reactors. Very few constrained estimators of the support boundary of a bivariate distribution have been introduced in the literature. They are based on simple…

两个期权市场的定价核与波动率风险AI 解读

Using both S&P 500 option and recently introduced VIX option prices, we study pricing kernels and their dependence on multiple volatility factors. We first propose nonparametric estimates of marginal pricing kernels, conditional on the VIX and the slope of the variance swap term structure. Our estimates highlight the state-dependence nature of the pricing kernels. In particular, conditioning on volatility factors, the pricing kernel of market returns exhibit a downward…

价格与波动率的共同跳跃AI 解读

The nature of the dependence between discontinuities in prices and contemporaneous discontinuities in volatility (co-jumps) has been reported by many as being elusive, in terms of sign, magnitude, and statistical significance. Using a novel identification strategy in continuous time relying on trade-level information for spot variance estimation, as well as infinitesimal cross-moments, we document that a sizeable proportion of discontinuous changes in prices are associated…