论文检索

最近矩估计量与 GMM 估计量的渐近等价性AI 解读

This note considers an asymptotic property of the class of closest moments estimators. Each such estimator is obtained by setting a vector of sample moments close to corresponding population moments. It is shown that each such estimator is asymptotically equivalent to a GMM estimator, which has a quadratic distance function. An implication of this result is that the estimator that is asymptotically efficient in the GMM class is also asymptotically efficient in the wider…

OLS 与可行 GLS 估计量的方差比较AI 解读

Second-order approximations to the variances of OLS and GLS estimators are compared when the covariance matrix is locally nonscalar. Using a result of Rothenberg, the comparison of OLS and GLS variances is shown to be asymptotically equivalent to a weighted mean square error comparison of the error covariance parameter estimators used in those two procedures. When there is only one covariance parameter, this comparison depends only on the noncentrality parameter of a…

回归系数非线性约束检验统计量零假设分布的高阶近似AI 解读

Asymptotic expansions of the distributions of likelihood ratio and Lagrange multiplier test statistics for nonlinear restrictions on regression coefficients are derived under the null hypothesis. Nonlinear restrictions include, as a special case, the identifiability restrictions in the simultaneous equations models. Our analyses of simultaneous equations deal not only with single equations but also subsystems and complete systems. The asymptotic expansions we derive are…

联立方程系统中过度识别约束检验的近似分布与功效AI 解读

We derive asymptotic expansions of the distributions of test statistics for over-identifying restrictions in a system of simultaneous equations under the null and the non-null hypotheses. We investigate the effects of the normality assumption for disturbances on the test statistics based on their asymptotic expansions. We also study the power functions of test statistics based on their asymptotic expansions. After modifying their critical regions to the same significance…

离散观测下广义 Itô 过程的最大似然估计AI 解读

This paper considers the parametric estimation problem for continuous-time stochastic processes described by first-order nonlinear stochastic differential equations of the generalized Itô type (containing both jump and diffusion components). We derive a particular functional partial differential equation which characterizes the exact likelihood function of a discretely sampled Itô process. In addition, we show by a simple counterexample that the common approach of estimating…

相依异质过程的不变性原理与中心极限定理AI 解读

Building on work of McLeish, we present a number of invariance principles for doubly indexed arrays of stochastic processes which may exhibit considerable dependence, heterogeneity, and/or trending moments. In particular, we consider possibly time-varying functions of infinite histories of heterogeneous mixing processes and obtain general invariance results, with central limit theorems following as corollaries. These results are formulated so as to apply to economic time…

摘要暂缺

摘要暂缺

摘要暂缺

摘要暂缺

摘要暂缺

摘要暂缺

摘要暂缺

联立回归方程系统参数的最大似然估计AI 解读

Procedures for computing the full information maximum likelihood (FIML) estimates of the parameters of a system of simultaneous regression equations have been described by Koopmans, Rubin, and Leipnik, Chernoff and Divinsky, Brown, and Eisenpress. However, all of these methods are rather complicated since they are based on estimating equations that are expressed in an inconvenient form. In this paper, a transformation of the maximum likelihood (ML) equations is developed…

异频存量与流量数据下连续时间 ARMAX 模型的高斯似然AI 解读

For purposes of maximum likelihood estimation, we show how to compute the Gaussian likelihood function when the data are generated by a higher-order continuous-time vector ARMAX model and are observed as stocks and flows at different frequencies. Continuous-time ARMAX models are analogous to discrete-time autoregressive moving-average models with distributed-lag exogenous variables. Stocks are variables observed at points in time and flows are variables observed as integrals…

点最优 Cox 检验AI 解读

This paper is concerned with the general problem of testing one form of covariance structure against another in a normal linear regression. It is shown that all the point-optimal tests recently proposed by King and his associates can be interpreted as special cases of a Cox test for non-nested hypotheses. This provides a synthesis of a whole range of point-optimal tests as well as demonstrating that King and his associates have exposed a class of Cox tests which have an…

存在结构变化时单个结构方程的估计AI 解读

Estimation of a single structural equation when there exists structural change is considered. Equality of structural variances in different samples is shown to affect the identification condition and asymptotic efficiency of best asymptotically normal estimators when the reduced-form covariance matrices differ by the structural change. The limited information maximum likelihood estimator is presented with its asymptotic property and compared with an alternative estimator.

估计向量自回归过程的移动平均系数的渐近分布AI 解读

The coefficients of the moving average (MA) representation of a vector autoregressive (VAR) process are the dynamic multipliers of the system. These quantities are often used to analyze the relationships between the variables involved. Assuming that the actual data generation process is stationary and has a VAR representation of unknown and possibly infinite order, the asymptotic distribution of the MA coefficients is derived. A computationally simple formula for the…

评论：Bierens 方法与时间序列模型设定检验AI 解读

Professor Bierens' approach is definitely original and interesting. We think it might become important, in particular, for testing correct specification of models (even nonlinear) in the time series context. For example, his test in Section 6 is (to our knowledge) the first test of correct specification of a model which is consistent against all stationary alternatives. Usually, specification tests checking the structure of dependence are only designed to test against a…

摘要暂缺

摘要暂缺

经济时间序列的 ARMA 记忆指标建模AI 解读

In this paper, it will be shown that if we condition a k -variate rational-valued time series process on its entire past, it is possible to capture all relevant information on the past of the process by a single random variable. This scalar random variable can be formed as an autoregressive moving average of past observations; Since economic data are usually reported in a finite number of digits, this result applies to virtually all economic time series. Therefore, economic…

摘要暂缺

摘要暂缺