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半参数多元 Copula 模型的有效估计AI 解读

We propose a sieve maximum likelihood estimation procedure for a broad class of semiparametric multivariate distributions. A joint distribution in this class is characterized by a parametric copula function evaluated at nonparametric marginal distributions. This class of distributions has gained popularity in diverse fields due to its flexibility in separately modeling the dependence structure and the marginal behaviors of a multivariate random variable, and its…

参数条件分布的非参数 Bootstrap 检验AI 解读

This paper proposes a bootstrap test for the correct specification of parametric conditional distributions. It extends Zheng's test (Zheng, 2000, Econometric Theory 16, 667–691) to allow for discrete dependent variables and for mixed discrete and continuous conditional variables. We establish the asymptotic null distribution of the test statistic with data-driven stochastic smoothing parameters. By smoothing both the discrete and continuous variables via the method of…

基于 Copula 的半参数时间序列模型估计AI 解读

This paper studies the estimation of a class of copula-based semiparametric stationary Markov models. These models are characterized by nonparametric invariant (or marginal) distributions and parametric copula functions that capture the temporal dependence of the processes; the implied transition distributions are all semiparametric, and a member in this class can be expressed as a generalized semiparametric regression transformation model. One advantage of this copula…

似然推断中的分部最大化AI 解读

This article presents and examines a new algorithm for solving a score equation for the maximum likelihood estimate in certain problems of practical interest. The method circumvents the need to compute second-order derivatives of the full likelihood function. It exploits the structure of certain models that yield a natural decomposition of a very complicated likelihood function. In this decomposition, the first part is a log-likelihood from a simply analyzed model, and the…

一致的模型设定检验AI 解读

We point out the close relationship between the integrated conditional moment tests in Bierens (1982, Journal of Econometrics 20, 105–134) and Bierens and Ploberger (1997, Econometrica 65, 1129–1152) with the complex-valued exponential weight function and the kernel-based tests in Härdle and Mammen (1993, Annals of Statistics 21, 1926–1947), Li and Wang (1998, Journal of Econometrics 87, 145–165), and Zheng (1996, Journal of Econometrics 75, 263–289). It is well established…

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基于固定平滑参数核密度估计量的拟合优度检验AI 解读

In this paper, we study the bias-corrected test developed in Fan (1994). It is based on the integrated squared difference between a kernel estimator of the unknown density function of a random vector and a kernel smoothed estimator of the parametric density function to be tested under the null hypothesis. We provide an alternative asymptotic approximation of the finite-sample distribution of this test by fixing the smoothing parameter. In contrast to the normal approximation…

随机生产前沿模型的半参数估计AI 解读

This paper extends the linear stochastic frontier model proposed by D. J. Aigner, C. A. K. Lovell, and P. Schmidt (1977) to a semiparametric frontier model in which the functional form of the production frontier is unspecified and the distributions of the composite error terms are of known form. Pseudo-likelihood estimators of the parameters characterizing the two error terms of the model are constructed based on kernel estimation of the conditional mean function. The Monte…

一致模型设定检验：遗漏变量与半参数函数形式AI 解读

By using nonparametric kernel estimation method and a central limit theorem for degenerate U-statistics of order higher than two, the authors develop several consistent model specification tests in the context of a nonparametric regression model. These include tests for omitted variables, tests for a partially linear model, and tests for a semiparametric single index model. The asymptotic normality of the test statistics are established under the respective null hypotheses…

线性回归模型中对称性的一致非参数检验AI 解读

This article proposes a consistent nonparametric test of the hypothesis that the disturbance in a linear regression model is distributed symmetrically around zero. Simulation results show that the test has good size and power properties for sample sizes as small as 50. We illustrate the use of the test in a cross-country model of inflation and monetary growth.

基于核方法的参数密度拟合优度检验AI 解读

Let F denote a distribution function defined on the probability space (Ω, , P ), which is absolutely continuous with respect to the Lebesgue measure in R d with probability density function f . Let f 0 (·,β) be a parametric density function that depends on an unknown p × 1 vector β. In this paper, we consider tests of the goodness-of-fit of f 0 (·,β) for f (·) for some β based on (i) the integrated squared difference between a kernel estimate of f (·) and the quasimaximum…