论文标题
依赖于国家的自回旋模型：属性，估计和预测
State-Dependent Autoregressive Models: Properties, Estimation and Forecasting
论文作者
论文摘要
本文研究了一些时间依赖性属性，并解决了非线性时间序列的一类国家依赖性自回归模型的参数估计问题，我们假设根据过程本身的第一个滞后值，我们假设随机自回归系数。我们称这种模型依赖于状态的一阶自回归过程（SDAR）。我们介绍了一些假设，根据这些假设，该类别的模型严格固定且均匀地呈现，并且我们建立了参数的准最大可能性估计量的一致性和渐近正态性。为了捕捉模型的潜力，我们向非线性时间序列提出了经验应用，该非线性时间序列是由每周实现的波动性从一些欧洲金融指数的回报中提取的。考虑到两极分子setar模型提供的替代方法，预测精度的比较是进行了比较
This paper studies some temporal dependence properties and addresses the issue of parametric estimation for a class of state-dependent autoregressive models for nonlinear time series in which we assume a stochastic autoregressive coefficient depending on the first lagged value of the process itself. We call such a model state-dependent first-order autoregressive process, (SDAR). We introduce some assumptions under which this class of models is strictly stationary and uniformly ergodic and we establish consistency and asymptotic normality of the quasi-maximum likelihood estimator of the parameters. In order to capture the potentiality of the model, we present an empirical application to nonlinear time series provided by the weekly realized volatility extracted from returns of some European financial indices. The comparison of forecasting accuracy is made considering an alternative approach provided by a two-regime SETAR model
