基于双阀值LSTAR模型的人民币汇率均值回复分析

为研究人民币汇率均值回复现象,文章提出一个双阀值LSTAR模型,并对该模型的非线性检验进行了研究。结果表明,不同于单阀值STAR模型需要对转换函数进行三阶Taylor展开,双阀值LSTAR模型只需对转换函数进行一阶Taylor展开即可,这大大节省了自由度。随后,该双阀值LSTAR模型被应用于人民币汇率均值回复实证检验。     

STAR模型的滞后阶数选择与稳健性研究

本文讨论了六种信息准则在STAR模型滞后阶数选择中的适应性及稳健性问题。Monte Carlo模拟结果显示,在多数情况下,数据生成过程中的误差项分布并不影响信息准则正确识别模型最大滞后阶数的能力;对于短STAR模型,ACC准则具有较高的正确识别率,并且对不同平滑转移系数及不同门限值具有很好的稳健性;而对于长STAR模型,SC准则及ACC准则具有更高的正确率及良好的稳健性。     

基于STAR模型的人民币实际汇率走势研究

文章采用平滑转移自回归(STAR)模型来揭示中国实际汇率的动态行为,对其进行分析和预测,检验结果表明,以logistic函数作为过渡函数的STAR模型能很好地描述人民币实际汇率的行为,中国实际汇率走势是非线性的并体现了非对称性.基于此模型得出的汇率预测显示,2009年人民币汇率将基本保持稳定,会出现小幅贬值,波动区间为6.79-6.96.     

人民币实际有效汇率波动的特征

在过去的20年里,非线性时间序列模型发展迅速,正日益广泛的被应用于各种经济时间序列特别是金融时间序列变量的研究.其中,STAR(Smooth Transition AR)模型,即平滑转换自回归模型就是非常重要的非线性领域转换模型.STAR模型首先是由Terasvirta& Anderson(1992)提出,后经Granger& Terasvirta(1993)和Terasvirta(1994)对模型的估计及统计特征进行了考察.尽管如此,在模型的应用方面,国内外都很少有将STAR模型应用于实际汇率特别是实际有效汇率的研究,而Michael(1997),N.Sarantis(1999)在这方面的研究也主要是针对发达国家.     

基于Monte Carlo模拟的STAR模型样本矩性质研究

采用Monte Carlo模拟方法对STAR模型样本矩的统计特性进行研究。分析结果表明:STAR模型的样本均值、样本方差、样本偏度及样本峰度都渐近服从正态分布;即使STAR模型的数据生成过程中不含有常数项,其总体均值可能也不是0,这与线性ARMA模型有显著区别;即使STAR模型数据生成过程中的误差项服从正态分布,数据仍有可能是有偏分布。     

基于STAR模型的外汇储备数据非线性性质研究

文章利用1993年1月至2008年9月的月度数据,使用非线性STAR模型技术对我国的外汇储备数据进行实证研究.研究发现我国外汇储备月度数据具有明显非线性特征,可以使用ESTAR模型表达;外汇储备数据具有显著阶段性特征,并且外汇储备数据在两种不同的机制下相互转换,转换速度缓慢.最后,基于ESTAR模型的实证分析,给出一些相关政策建议,认为充分的外汇储备有助于维护国民经济稳定运行,抑制金融危机的影响,削减外汇储备应当审慎.     

ESTAR模型单位根检验的性质研究

文章以转移函数为指数形式的平滑转移自回归模型(ESTAR)作为典型代表,通过模拟实验考察了非线性单位根检验(包括ADF检验和PP检验)的小样本性质;以MA(1)和GARCH(1,1)过程为代表,研究了误差项服从序列相关和异方差时,非线性参数和滞后阶数对两类检验统计量实际水平和功效的影响,从而为非平稳STAR族模型的应用研究提供一定的理论支持.     

基于样本数据正态性转换的VaR估测

VaR的发展就在于不断寻求解决风险测度的真实性,是一种克服或降低风险的方法。在揭示Del-ta-Gamma非线性模型计算VaR正态假设局限性的基础上,通过对经典转换函数Delta-Gamma-Johnson转换函数以及基于Delta-Gamma-Cornish-Fisher扩展方法构造的转换函数的梳理,从实证分析的角度考察了中国股票市场VaR的估值问题。实证结果表明,Delta-Gamma-Johnson转换函数中的SU型转换基本适宜于作为中国股票市场样本数据正态化处理的转换函数,利用SU型转换后的样本数据所计算的VaR值能明显改善中国股票市场风险测度水平。     

函数型变量倾斜分位回归模型及其应用

本文考虑函数型数据的结构特征,针对两类函数型变量分位回归模型（函数型因变量对标量自变量和函数型因变量对函数型自变量）,基于函数型倾斜分位曲线的定义构建新型函数型倾斜分位回归模型。对于第二类模型,本文分别考虑样条基函数对模型系数展开和函数型主成分基函数对函数型自变量展开,得到倾斜分位回归模型的基本形式。参数估计采用成分梯度Boosting算法最小化加权非对称损失函数,提高计算效率。在理论上证明了倾斜分位回归模型的系数估计量均服从渐近正态分布。模拟和实证研究结果显示,倾斜分位回归模型比已有的逐点分位回归模型具有更好的拟合效果。根据积分均方预测误差准则,本文提出的模型有一致较好的预测能力。     

基于STAR模型的我国出口波动非线性研究

文章采用非线性STAR模型对1991年1季度至2014年3季度我国出口同比增长率数据进行实证分析,研究结果为:(1)我国出口以9.12％的均衡增长率实现在“波动中增长”与“波动中缩减”,此均衡水平与我国首次制定的《对外贸易发展“十二五”规划》10％左右的增长目标相吻合.(2)我国出口波动具有非对称性,且较多年份的出口额增长率位于高转换区域;区域之间转换速度较快,国内外冲击容易导致我国出口增长发生非线性转换,这与我国出口周期特征是一致的.     

Robust Tests Against Smooth Transition Autoregressive Models

In this paper, we consider testing for linearity against a well-known class of regime switching models known as the smooth transition autoregressive (STAR) models. Apart from the model selection issues, one reason for interest in testing for linearity in time-series models is that non-linear models such as the STAR are considerably more difficult to use. This testing problem is non-standard because a nuisance parameter becomes unidentified under the null hypothesis. In this paper, we further explore the class of tests proposed by Luukkonen, Saikonnen and Terasvirta (1988). Luukkonen et al . (1988) proposed LM tests for linearity against STAR models. A potential difficulty here is that the linear approximation introduces high leverage points, and hence outliers are likely to be quite influential. To overcome this difficulty, we use the same approximating linear model of Luukkonen et al . (1988), but we apply Wald and F -tests based on l 1 - and bounded influence estimates. The efficiency gains of this procedure cannot be easily deduced from the existing theoretical results because the test is based on a misspecified model under H 1 . Therefore, we carried out a simulation study, in which we observed that the robust tests have desirable properties compared to the test of Luukkonen et al . (1988) for a range of error distributions in the STAR model, in particular the robust tests have power advantages over the LM test.     

SMOOTH TRANSITION AUTOREGRESSIVE MODELS — A SURVEY OF RECENT DEVELOPMENTS

This paper surveys recent developments related to the smooth transition autoregressive (STAR) time series model and several of its variants. We put emphasis on new methods for testing for STAR nonlinearity, model evaluation, and forecasting. Several useful extensions of the basic STAR model, which concern multiple regimes, time-varying non-linear properties, and models for vector time series, are also reviewed.     

Modeling exchange rate dynamics: Non-linear dependence and thick tails

This paper illustrates a new approach to the statistical modeling of non-linear dependence and leptokurtosis in exchange rate data. The student's t autoregressive model withdynamic heteroskedasticity (STAR) of spanos (1992) is shown to provide a parsimonious and statistically adequate representation of the probabilistic information in exchange rate data. For the STAR model, volatility predictions are formed via a sequentially updated weighting scheme which uses all the past history of the series. The estimated STAR models are shown to statistically dominate alternative ARCH-type formulations and suggest that volatility predictions are not necessarily as large or as variable as other models indicate.     

Modeling exchange rate dynamics: Non-linear dependence and thick tails

This paper illustrates a new approach to the statistical modeling of non-linear dependence and leptokurtosis in exchange rate data. The student's t autoregressive model withdynamic heteroskedasticity (STAR) of spanos (1992) is shown to provide a parsimonious and statistically adequate representation of the probabilistic information in exchange rate data. For the STAR model, volatility predictions are formed via a sequentially updated weighting scheme which uses all the past history of the series. The estimated STAR models are shown to statistically dominate alternative ARCH-type formulations and suggest that volatility predictions are not necessarily as large or as variable as other models indicate.     

Bayesian inference for smooth transition autoregressive (STAR) model: A prior sensitivity analysis

The main aim of this paper is to perform sensitivity analysis to the specification of prior distributions in a Bayesian analysis setting of STAR models. To achieve this aim, the joint posterior distribution of model order, coefficient, and implicit parameters in the logistic STAR model is first being presented. The conditional posterior distributions are then shown, followed by the design of a posterior simulator using a combination of Metropolis-Hastings, Gibbs Sampler, RJMCMC, and Multiple Try Metropolis algorithms, respectively. Following this, simulation studies and a case study on the prior sensitivity for the implicit parameters are being detailed at the end.     

Estimation of sample selection bias models

Econometric models with sample selection biases are widely used in various fields of economics, such as labor economics. The Maximum Likelihood Estimator (MLE) is seldom used to estimate models because of computational difficulty, while Heckman's two-step estimator is widely used to estimate these models. However, Heckman's two-step estimator sometimes performs poorly. In this paper, methods of calculating the MLE are analysed, and finite sample properties of the MLE and Heckman's two-step estimator are compared using Monte Carlo experiments and empirical examples.     

Robust Bayesian inference in STAR models with neighbourhood effects

In this paper we propose a robust Bayesian procedure of estimation, testing, validation and selection of spatio-temporal autoregressive models (STAR) with neighbourhood effects applied to the appraisal of dwelling prices. The methodology does not depend on asymptotic results and, unlike previously procedures proposed in the literature, takes into account the uncertainty associated to the estimation of the neighbourhood parameters of the model, giving more realism to the analysis. Moreover, a sequential algorithm to elaborate fast on-line forecast, is provided. The methodology is illustrated by means of a practical case of the real estate market of Zaragoza.     

Estimation of sample selection bias models

Econometric models with sample selection biases are widely used in various fields of economics, such as labor economics. The Maximum Likelihood Estimator (MLE) is seldom used to estimate models because of computational difficulty, while Heckman's two-step estimator is widely used to estimate these models. However, Heckman's two-step estimator sometimes performs poorly. In this paper, methods of calculating the MLE are analysed, and finite sample properties of the MLE and Heckman's two-step estimator are compared using Monte Carlo experiments and empirical examples.