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基于矩条件的金融收益 Copula 检验AI 解读

We propose a class of moment-based tests for copulas in a parametric multivariate dynamic context for financial returns. The proposed method takes into account the effect of estimation uncertainty. This effect is quite important but often is ignored in related studies. Our method can be applied to generate various tests to detect copula misspecification in different directions. In particular, on the basis of the conditional probabilities of quantile exceedances (Kendall's…

贝叶斯Probit回归MCMC算法的收敛速度与渐近标准误AI 解读

Summary Consider a probit regression problem in which Y1, …, Yn are independent Bernoulli random variables such that Pr(Yi=1)=Φ(xiTβ) where xi is a p-dimensional vector of known covariates that are associated with Yi, β is a p-dimensional vector of unknown regression coefficients and Φ(·) denotes the standard normal distribution function. We study Markov chain Monte Carlo algorithms for exploring the intractable posterior density that results when the probit regression…

通过最小二乘近似统一 LASSO 估计AI 解读

We propose a method of least squares approximation (LSA) for unified yet simple LASSO estimation. Our general theoretical framework includes ordinary least squares, generalized linear models, quantile regression, and many others as special cases. Specifically, LSA can transfer many different types of LASSO objective functions into their asymptotically equivalent least squares problems. Thereafter, the standard asymptotic theory can be established and the LARS algorithm can…

无单调性非可分模型中边际效应的识别AI 解读

Nonseparable models do not impose any type of additivity between the unobserved part and the observable regressors, and are therefore ideal for many economic applications. To identify these models using the entire joint distribution of the data as summarized in regression quantiles, monotonicity in unobservables has frequently been assumed. This paper establishes that in the absence of monotonicity, the quantiles identify local average structural derivatives of nonseparable…

计量经济模型中参数集合的估计与置信区域AI 解读

This paper develops a framework for performing estimation and inference in econometric models with partial identification, focusing particularly on models characterized by moment inequalities and equalities. Applications of this framework include the analysis of game-theoretic models, revealed preference restrictions, regressions with missing and corrupted data, auction models, structural quantile regressions, and asset pricing models. Copyright The Econometric Society 2007.

英国男性的永久与暂时工资：1975—2001年的年份、年龄和队列效应AI 解读

Abstract We examine the mean and variance–covariance structure of log‐wages over calendar time and the life cycle of British men, hereby controlling for birth cohort effects. We attribute the strong increase in mean log‐wage during the 1980s and 1990s to a rise in mean log‐wage with the year of birth. This rise is diminishing with the year of birth, which implies lower wage inequality between cohorts with the year of birth. Wage inequality has increased during the 1980s and…

竞争风险数据的非参数分位数推断AI 解读

A conceptually simple quantile inference procedure is proposed for cause-specific failure probabilities with competing risks data. The quantiles are defined using the cumulative incidence function, which is intuitively meaningful in the competing–risks set–up. We establish the uniform consistency and weak convergence of a nonparametric estimator of this quantile function. These results form the theoretical basis for extensions of standard one–sample and two–sample quantile…

依赖性与错误发现率：渐近分析AI 解读

Some effort has been undertaken over the last decade to provide conditions for the control of the false discovery rate by the linear step-up procedure (LSU) for testing $n$ hypotheses when test statistics are dependent. In this paper we investigate the expected error rate (EER) and the false discovery rate (FDR) in some extreme parameter configurations when $n$ tends to infinity for test statistics being exchangeable under null hypotheses. All results are derived in terms of…

非对称幂分布：理论及风险度量应用AI 解读

Abstract Theoretical literature in finance has shown that the risk of financial time series can be well quantified by their expected shortfall, also known as the tail value‐at‐risk. In this paper, I construct a parametric estimator for the expected shortfall based on a flexible family of densities, called the asymmetric power distribution (APD). The APD family extends the generalized power distribution to cases where the data exhibits asymmetry. The first contribution of the…

国际福利比较与多变量随机占优的非参数检验AI 解读

Abstract This paper outlines a class of statistical procedures that permit testing of a broad range of multidimensional stochastic dominance hypotheses and, more generally, welfare hypotheses that rely upon multiple stochastic dominance conditions. We apply the procedures to data on income and leisure hours for individuals in Germany, the UK, and the USA. We find that no country first‐order stochastically dominates the others in both dimensions for all years of comparison.…

无需条件独立假设的倾向得分匹配：英国性别工资差距应用AI 解读

Propensity score matching is frequently used for estimating average treatment effects. Its applicability, however, is not confined to treatment evaluation. In this paper, it is shown that propensity score matching does not hinge on a selection on observables assumption and can be used to estimate not only adjusted means but also their distributions, even with non-i.i.d. sampling. Propensity score matching is used to analyze the gender wage gap among graduates in the UK. It…

分位数回归模型的非参数工具变量估计AI 解读

We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression “error” conditional on an instrumental variable to be zero. The resulting estimating equation is a nonlinear integral equation of the first kind, which generates an ill-posed inverse problem. The integral operator and distribution of the instrumental variable are unknown and must be estimated nonparametrically. We show that the estimator is…

从不可比调查计算可比统计量：以印度贫困为例AI 解读

Applied economists are often interested in studying trends in economic indicators such as inequality or poverty; however, comparisons over time can be made impossible by changes in data collection methodology. We describe an easily implemented procedure, based on inverse probability weighting, that allows recovery of comparability of estimated parameters identified implicitly by a moment condition. The validity of the procedure requires the existence of a set of auxiliary…

通过变换回归构造分位数置信区间的修正代入法AI 解读

We propose a corrected plug-in method for constructing confidence intervals of the conditional quantiles of an original response variable through a transformed regression with heteroscedastic errors. The interval is easy to compute. Factors affecting the magnitude of the correction are examined analytically through the special case of Box–Cox regression. Monte Carlo simulations show that the new method works well in general and is superior over the commonly used delta method…

删失数据的平滑秩回归AI 解读

A weighted rank estimating function is proposed to estimate the regression parameter vector in an accelerated failure time model with right censored data. In general, rank estimating functions are discontinuous in the regression parameter, creating difficulties in determining the asymptotic distribution of the estimator. A local distribution function is used to create a rank based estimating function that is continuous and monotone in the regression parameter vector. A…

贝叶斯密度回归AI 解读

Summary The paper considers Bayesian methods for density regression, allowing a random probability distribution to change flexibly with multiple predictors. The conditional response distribution is expressed as a non-parametric mixture of regression models, with the mixture distribution changing with predictors. A class of weighted mixture of Dirichlet process priors is proposed for the uncountable collection of mixture distributions. It is shown that this specification…

概率预测、校准与集中度AI 解读

Summary Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive distributions subject to calibration. Calibration refers to the statistical consistency between the distributional forecasts and the observations and is a joint property of the…

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通过幂变换构建线性分位数回归AI 解读

In this article we consider the linear quantile regression model with a power transformation on the dependent variable. Like the classical Box–Cox transformation approach, it extends the applicability of linear models without resorting to nonparametric smoothing, but transformations on the quantile models are more natural due to the equivariance property of the quantiles under monotone transformations. We propose an estimation procedure and establish its consistency and…

再生核希尔伯特空间中的分位数回归AI 解读

In this article we consider quantile regression in reproducing kernel Hilbert spaces, which we call kernel quantile regression (KQR). We make three contributions: (1) we propose an efficient algorithm that computes the entire solution path of the KQR, with essentially the same computational cost as fitting one KQR model; (2) we derive a simple formula for the effective dimension of the KQR model, which allows convenient selection of the regularization parameter; and (3) we…