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有序 Probit 模型的图形解释AI 解读

The use of probit and logit models has become quite common whenever the dependent variable in a regression is qualitative. These models have been used to explain either/or choices and decisions involving multiple alternatives. A two-dimensional graphical interpretation of these different models has been provided by Johnson [3]. The purpose of this paper is to provide a three-dimensional graphical exposition of the ordered probit model, which was first estimated by McKelvey…

短自回归时间序列均值的最佳线性无偏估计AI 解读

A simple formula for computing the best linear unbiased estimate of the mean of an autoregressive process as well as its variance is given. Numerical results show that the estimate can have much lower variance than that of the usual sample mean.

含误差分量的联立方程估计札记AI 解读

Baltagi [3] derived 2SLS and 3SLS analogues for a simultaneous equation model with error components. These were denoted by EC2SLS and EC3SLS. More recently, Balestra and Varadharajan-Krishnakumar [1] derived alternative 2SLS and 3SLS analogues; these were denoted by G2SLS and G3SLS. This note explains the relationship between these estimators and shows that the set of instruments employed by Balestra and Varadharajan-Krishnakumar is a subset of those used in Baltagi. In…

含 ARMA 误差的线性回归：GLS 变换矩阵与半递归估计AI 解读

For a general stationary ARMA( p,q ) process u we derive the exact form of the orthogonalizing matrix R such that R ′ R = Σ −1 , where Σ = E ( uu ′) is the covariance matrix of u , generalizing the known formulae for AR ( p ) processes. In a linear regression model with an ARMA( p,q ) error process, transforming the data by R yields a regression model with white-noise errors. We also consider an application to semi-recursive (being recursive for the model parameters, but not…

截断回归模型的半参数非线性最小二乘估计AI 解读

This article provides a semiparametric method for the estimation of truncated regression models where the disturbances are independent of the regressors before truncation. This independence property provides useful information on model identification and estimation. Our estimate is shown to be -consistent and asymptotically normal. A consistent estimate of the asymptotic covariance matrix of the estimator is provided. Monte Carlo experiments are performed to investigate some…

随机微分方程系统的连续记录渐近理论AI 解读

This paper considers estimation based on a set of T + 1 discrete observations, y (0), y ( h ), y (2 h ),…, y ( Th ) = y ( N ), where h is the sampling frequency and N is the span of the data. In contrast to the standard approach of driving N to infinity for a fixed sampling frequency, the current paper follows Phillips [35,36] and Perron [29] and examines the “dual” asymptotics implied by letting h tend to zero while the span N remains fixed. We suggest a way of explicitly…

通过自回归近似估计与检验协整系统AI 解读

This paper studies the estimation and testing of general cointegrated systems by using an autoregressive approximation. Simple estimators for both the cointegration vectors and their weight matrix in the autoregressive error correction model representation of the system are developed. Since these estimators assume that the number of cointegration vectors and their normalization are fixed in advance, convenient specification tests for checking the validity of these…

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用于估计一般线性连续时间系统的离散模型AI 解读

This paper derives discrete models for estimating systems of both first- and second-order linear differential equations in which derivatives of the exogenous variables appear in addition to their levels.

从特征函数到分布函数：一个简明理论框架AI 解读

A unified framework is established for the study of the computation of the distribution function from the characteristic function. A new approach to the proof of Gurland's and Gil-Pelaez's univariate inversion theorem is suggested. A multivariate inversion theorem is then derived using this technique.

AR(1) 模型中预测误差的联合分布AI 解读

Second-order asymptotic expansion approximations to the joint distributions of dynamic forecast errors and of static forecast errors in the stationary Gaussian pure AR(1) model are derived. The approximation to the dynamic forecast errors distribution can be expressed as a multivariate normal distribution with modified mean vector and covariance matrix, thus generalizing the results of Phillips [12]. However, the approximation to the static forecast errors distribution…

通过向量自回归模型估计正交化脉冲响应AI 解读

Impulse response functions from time series models are standard tools for analyzing the relationship between economic variables. The asymptotic distribution of orthogonalized impulse responses is derived under the assumption that finite order vector autoregressive (VAR) models are fitted to time series generated by possibly infinite order processes. The resulting asymptotic distributions of forecast error variance decompositions are also given.

一个实证求职模型的精确似然函数AI 解读

The exact likelihood function for a prototypal job search model is analyzed. The optimality condition implied by the dynamic programming framework is fully imposed. Using the optimality condition allows identification of an offer arrival probability separately from an offer acceptance probability. The estimation problem is nonstandard. The geometry of the likelihood function in finite samples is considered, along with asymptotic properties of the maximum likelihood estimator.

最小绝对偏差估计量渐近分析的简捷方法AI 解读

Using generalized functions of random variables and generalized Taylor series expansions, we provide quick demonstrations of the asymptotic theory for the LAD estimator in a regression model setting. The approach is justified by the smoothing that is delivered in the limit by the asymptotics, whereby the generalized functions are forced to appear as linear functionals wherein they become real valued. Models with fixed and random regressors, and autoregressions with infinite…

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