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连续时间长记忆时间序列模型的估计AI 解读

A class of univariate fractional ARIMA models with a continuous time parameter is developed for the purpose of modeling long-memory time series. The spectral density of discretely observed data is derived for both point observations (stock variables) and integral observations (flow variables). A frequency domain maximum likelihood method is proposed for estimating the longmemory parameter and is shown to be consistent and asymptotically normally distributed, and some issues…

具有共同随机趋势的连续时间动态模型的高斯估计AI 解读

We derive the exact discrete model and the Gaussian likelihood function of a first-order system of linear stochastic differential equations driven by an observable vector of stochastic trends and a vector of stationary innovations.

无界、相依且异质数组的随机等度连续性AI 解读

This paper establishes stochastic equicontinuity for classes of mixingales. Attention is restricted to Lipschitz-continuous parametric functions. Unlike some other empirical process theory for dependent data, our results do not require bounded functions, stationary processes, or restrictive dependence conditions. Applications are given to martingale difference arrays, strong mixing arrays, and near-epoch dependent arrays.

弱相依误差回归模型中 M 估计量的有效 Edgeworth 展开AI 解读

Consider a linear regression model y 1 = x 1 β + u 1 , where the u 1 'S afe weakly dependent random variables, the x 1 's are known design nonrandom variables, and β is an unknown parameter. We define an M-estimator β n of) β corresponding to a smooth score function. Then, the second-order Edgeworth expansion for β n is derived. Here we do not assume the normality of (u 1 ), and (u 1 ) includes the usual ARMA processes. Second, we give the second-order Edgeworth expansion…

含受限内生解释变量的联立方程非参数两阶段估计AI 解读

Estimation of simultaneous equations with limited (or transformed) endogenous regressors has been difficult in the parametric literature for various reasons. In this paper, we propose a nonparametric two-stage method that is analogous to two-stage least-squares estimation. A simultaneous censored model is used to illustrate our approach, and then its generalization to other cases is developed. The technical highlight is in handling a nondifferentiable second-stage minimand…

Hilbert 空间值 mixingale 的大数定律及其应用AI 解读

To obtain consistency results for nonparametric estimators based on stochastic processes relevant in econometrics, we introduce the notions of Hilbert space-valued L p mixingales and near-epoch dependent arrays, and we prove weak and strong laws of large numbers by using a new exponential inequality for Hilbert ( H ) space-valued martingale difference arrays. We follow Andrews (1988, Econometric Theory 4, 458–467), Hansen (1991, Econometric Theory 7, 213–221; 1992,…

Lp 回归估计量的 Bahadur–Kiefer 表示AI 解读

We consider the following linear regression model: where are independent and identically distributed random variables, Y i , is real, Z i has values in R m , U i , is independent of Z i , and θ 0 is an m -dimensional parameter to be estimated. The L p estimator of θ 0 is the value 6n such that Here, we will give the exact Bahadur-Kiefer representation of θn, for each p ≥ 1. Explicitly, we will see that, under regularity conditions, where and c is a positive constant, which…

连续时间模型中的非因果性AI 解读

In this paper, we study new definitions of noncausality, set in a continuous time framework, illustrated by the intuitive example of stochastic volatility models. Then, we define CIMA processes (i.e., processes admitting a continuous time invertible moving average representation), for which canonical representations and sufficient conditions of invertibility are given. We can provide for those CIMA processes parametric characterizations of noncausality relations as well as…

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