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弱 ARMA 模型综合相关性检验的 Bootstrap 方法AI 解读

Summary The paper uses a random-weighting (RW) method to bootstrap the critical values for the Ljung–Box or Monti portmanteau tests and weighted Ljung–Box or Monti portmanteau tests in weak auto-regressive moving average models. Unlike the existing methods, no user-chosen parameter is needed to implement the RW method. As an application, these four tests are used to check the model adequacy in power generalized auto-regressive conditional heteroscedasticity models.…

非高斯与异方差误差下高维时间序列模型的ℓ₁正则化AI 解读

We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. The adaLASSO is a one-step implementation of the family of folded concave penalized least-squares. We assume that both the number of covariates in the model and the number of candidate variables can increase with the sample size (polynomially or geometrically). In other words, we let the number of candidate variables to be larger than the number of…

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…

基于幂变差的波动率模型误设检验AI 解读

We provide a simple and easy to use goodness-of-fit test for the misspecification of the volatility function in diffusion models. The test uses power variations constructed as functionals of discretely observed diffusion processes. We introduce an orthogonality condition which stabilizes the limit law in the presence of parameter estimation and avoids the necessity for a bootstrap procedure that reduces performance and leads to complications associated with the structure of…

存在结构突变的异质面板估计AI 解读

This paper extends Pesaran’s (2006) work on common correlated effects (CCE) estimators for large heterogeneous panels with a general multifactor error structure by allowing for unknown common structural breaks. Structural breaks due to new policy implementation or major technological shocks, are more likely to occur over a longer time span. Consequently, ignoring structural breaks may lead to inconsistent estimation and invalid inference. We propose a general framework that…

非参数与半参数分位数模型推断的直接方法AI 解读

We construct a generic confidence interval for a conditional quantile via the direct approach. It avoids estimating the conditional density function of the dependent variable given the covariate and is asymptotically valid for any conditional quantile, any conditional quantile estimator, and any data structure, provided that certain weak convergence of the conditional quantile process holds for the original quantile estimator. We also construct a generic confidence band for…

通过自适应组融合LASSO估计面板共同突变AI 解读

In this paper we consider estimation and inference of common breaks in panel data models via adaptive group fused Lasso. We consider two approaches—penalized least squares (PLS) for first-differenced models without endogenous regressors, and penalized GMM (PGMM) for first-differenced models with endogeneity. We show that with probability tending to one, both methods can correctly determine the unknown number of breaks and estimate the common break dates consistently. We…

检验随机变量的矩是否有限AI 解读

This paper proposes a test to verify whether the kth moment of a random variable is finite. We use the fact that, under general assumptions, sample moments either converge to a finite number or diverge to infinity according as the corresponding population moment is finite or not. Building on this, we propose a test for the null that the kth moment does not exist. Since, by construction, our test statistic diverges under the null and converges under the alternative, we…

扩散模型最大似然估计的信息理论AI 解读

We develop an information theoretic framework for maximum likelihood estimation of diffusion models. Two information criteria that measure the divergence of a diffusion process from the true diffusion are defined. The maximum likelihood estimator (MLE) converges asymptotically to the limit that minimizes the criteria when sampling interval decreases as sampling span increases. When both drift and diffusion specifications are correct, the criteria become zero and the inverse…

未知形式条件异方差下的VAR推断AI 解读

We consider a framework for asymptotically valid inference in stable vector autoregressive (VAR) models with conditional heteroskedasticity of unknown form. A joint central limit theorem for the LS estimators of both the VAR slope parameters as well as the unconditional innovation variance parameters is obtained from a weak vector autoregressive moving average model set-up recently proposed in the literature. Our results are important for correct inference on VAR statistics…

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…

经验贝叶斯反卷积估计AI 解读

An unknown prior density $g(\\theta )$ has yielded realizations $\\Theta _1,\\ldots ,\\Theta _N$ . They are unobservable, but each $\\Theta _i$ produces an observable value $X_i$ according to a known probability mechanism, such as $X_i\\sim {\\rm Po}(\\Theta _i)$ . We wish to estimate $g(\\theta )$ from the observed sample $X_1,\\ldots ,X_N$ . Traditional asymptotic calculations are discouraging, indicating very slow nonparametric rates of convergence. In this article we…

纵向数据充分降维的估计方程方法AI 解读

Sufficient dimension reduction has been extensively explored in the context of independent and identically distributed data. In this article we generalize sufficient dimension reduction to longitudinal data and propose an estimating equation approach to estimating the central mean subspace. The proposed method accounts for the covariance structure within each subject and improves estimation efficiency when the covariance structure is correctly specified. Even if the…

脱离规则网格：对数高斯 Cox 过程的高效推断AI 解读

This paper introduces a new method for performing computational inference on log-Gaussian Cox processes. The likelihood is approximated directly by making use of a continuously specified Gaussian random field. We show that for sufficiently smooth Gaussian random field prior distributions, the approximation can converge with arbitrarily high order, whereas an approximation based on a counting process on a partition of the domain achieves only first-order convergence. The…

关于矩估计中多重插补的一则说明AI 解读

Multiple imputation is widely used for estimation in situations where there are missing data. Rubin (1987) provided an easily applicable formula for multiple imputation variance estimation, but its validity requires the congeniality condition of Meng (1994), which may not be satisfied for method of moments estimation. We give the asymptotic bias of Rubin's variance estimator when method of moments estimation is used in the complete-sample analysis for each imputed dataset. A…

基于误差分布估计的参数混合效应模型拟合优度检验AI 解读

We address the problem of testing for a parametric function of fixed effects in mixed models. We propose a test based on the distance between two empirical error distribution functions, which are constructed from residuals calculated under the opposing hypotheses. The proposed test statistic has power against all alternatives, and its asymptotic distribution is derived. A simulation study shows that the test outperforms others in the literature. The test is applied to…

基于非参数经验贝叶斯与极大似然推断的高维分类AI 解读

We propose new nonparametric empirical Bayes methods for high-dimensional classification. Our classifiers are designed to approximate the Bayes classifier in a hypothesized hierarchical model, where the prior distributions for the model parameters are estimated nonparametrically from the training data. As is common with nonparametric empirical Bayes, the proposed classifiers are effective in high-dimensional settings even when the underlying model parameters are in fact…

含检测限协变量的回归半参数方法AI 解读

We consider generalized linear regression with a covariate left-censored at a lower detection limit. Complete-case analysis, where observations with values below the limit are eliminated, yields valid estimates for regression coefficients but loses efficiency, ad hoc substitution methods are biased, and parametric maximum likelihood estimation relies on parametric models for the unobservable tail probability distribution and may suffer from model misspecification. To obtain…

条件独立性的非参数贝叶斯推断AI 解读

In many application areas, a primary focus is on assessing evidence in the data refuting the assumption of independence of |$Y$| and |$X$| conditionally on |$Z$|⁠, with |$Y$| response variables, |$X$| predictors of interest, and |$Z$| covariates. Ideally, one would have methods available that avoid parametric assumptions, allow |$Y, X, Z$| to be random variables on arbitrary spaces with arbitrary dimension, and accommodate rapid consideration of different candidate…

似然与边际复合似然二阶理论的统一：表1AI 解读

An adjustment for marginal composite likelihoods is derived to match the second-order theory of the likelihood when inference is for a vector-valued parameter in the absence of nuisance components. The adjustment overcomes the failure of Bartlett identities for marginal composite likelihoods and leads to a Bartlett-correctable marginal composite likelihood ratio statistic.

随机变量比值的分布AI 解读

Inversion formulae are derived that express the density and distribution function of a ratio of random variables in terms of the joint characteristic function of the numerator and denominator. The resulting expressions are amenable to numerical evaluation and lead to simple asymptotic expansions. The expansions reduce to known results when the denominator is almost surely positive. Their accuracy is demonstrated with numerical examples.

J检验向空间面板数据框架的扩展AI 解读

Summary Kelejian ( Letters in Spatial and Resources Sciences ; 1 : 3–11) extended the J ‐test procedure to a spatial framework. Although his suggested test was computationally simple and intuitive, it did not use the available information in an efficient manner. Kelejian and Piras ( Regional Science and Urban Economics ; 41 : 281–292) generalized and modified Kelejian's test to account for all the available information. However, neither Kelejian ( ) nor Kelejian & Piras ( )…

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…