HYPOTHESIS TESTING FOR ARCH MODELS: A MULTIPLE QUANTILE REGRESSIONS APPROACH

We propose a quantile regression‐based test to detect the presence of autoregressive conditional heteroscedasticity by combining distributional information across multiple quantiles. A chi‐square‐type test statistic based on the weighted average of distinct regression quantile estimators is formed. Unlike the widely used likelihood‐based tests, the proposed test does not make any distributional assumptions on the underlying errors. Monte Carlo simulation studies show that the proposed test outperforms the likelihood‐based tests in several aspects.     

Estimation and forecasting of machine health condition using ARMA/GARCH model

This paper proposes the hybrid model of autoregressive moving average (ARMA) and generalized autoregressive conditional heteroscedasticity (GARCH) to estimate and forecast the machine state based on vibration signal. The main idea in this study is to employ the linear ARMA model and the nonlinear GARCH model to explain the wear and fault condition of machine, respectively. The successful outcomes of the ARMA/GARCH prediction model can give obvious explanation for future states of machine, which enhance the worth of machine condition monitoring as well as condition-based maintenance in practical applications. The advance of the proposed model is verified in empirical results as applying for a real system of a methane compressor in a petrochemical plant.     

计量经济模型中的异方差和序列相关问题

通过分析两种违背计量经济模型基本假设的异方差和序列相关问题,以最小二乘法为主要方法,提出多种新型参数检验和参数修正的方法,从而提高计量模型的合理性和预测的有效性.     

Comparing meta-analytic moderator estimation techniques under realistic conditions.

One of the most problematic issues in contemporary meta-analysis is the estimation and interpretation of moderating effects. Monte Carlo analyses are developed in this article that compare bivariate correlations, ordinary least squares and weighted least squares (WLS) multiple regression, and hierarchical subgroup (HS) analysis for assessing the influence of continuous moderators under conditions of multicollinearity and skewed distribution of study sample sizes (heteroscedasticity). The results show that only WLS is largely unaffected by multicollinearity and heteroscedasticity, whereas the other techniques are substantially weakened. Of note, HS, one of the most popular methods, typically provides the most inaccurate results, whereas WLS, one of the least popular methods, typically provides the most accurate results. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

时变异方差不确定影响下的疲劳裂纹在线评估

主动导波结构健康监测能够实时在线地监测结构状态。 但时变因素影响会增加裂纹评估难度,降低评估准确性。 时变 因素的影响使得导波监测信号特征呈现明显的异方差特征,即导波信号特征分布方差随时间变化而改变。 针对此问题,提出了 异方差分位数回归辅助的裂纹在线评估方法,利用 3 条特殊的分位数回归曲线估计时变因素影响下信号特征随时间的分布变 化情况,实现对监测数据中的异方差不确定性的处理。 基于批量缺口梁结构的疲劳与导波监测试验数据对提出方法的有效性 进行验证,实验结果表明,提出的方法能够实现时变影响下的损伤评估,评估最大绝对误差为 1. 1 mm,均方根误差为 0. 4 mm, 并且可以有效处理时变异方差影响,量化其不确定度,为评估结果提供参考价值。     

Diagnostic Checking in a Flexible Nonlinear Time Series Model

This paper considers a sequence of misspecification tests for a flexible nonlinear time series model. The model is a generalization of both the smooth transition autoregressive (STAR) and the autoregressive artificial neural network (AR-ANN) models. The tests are Lagrange multiplier (LM) type tests of parameter constancy against the alternative of smoothly changing ones, of serial independence, and of constant variance of the error term against the hypothesis that the variance changes smoothly between regimes. The small sample behaviour of the proposed tests is evaluated by a Monte-Carlo study and the results show that the tests have size close to the nominal one and a good power.     

Effects of outliers on the identification and estimation of GARCH models

Abstract. This paper analyses how outliers affect the identification of conditional heteroscedasticity and the estimation of generalized autoregressive conditionally heteroscedastic (GARCH) models. First, we derive the asymptotic biases of the sample autocorrelations of squared observations generated by stationary processes and show that the properties of some conditional homoscedasticity tests can be distorted. Second, we obtain the asymptotic and finite sample biases of the ordinary least squares (OLS) estimator of ARCH(p) models. The finite sample results are extended to generalized least squares (GLS), maximum likelihood (ML) and quasi‐maximum likelihood (QML) estimators of ARCH(p) and GARCH(1,1) models. Finally, we show that the estimated asymptotic standard deviations are biased estimates of the sample standard deviations.     

基于理性预期的宏观经济预警系统研究

宏观经济循环波动理论是预警的重要基础,对经济波动的研究应采用非线性的原理和方法,并以量化的数学模型为基础;对西方理性预期宏观经济波动的ＡＤ－ＡＳ模型做了改进,建立了一种经济波动的非线性模型（ＡＤ－ＡＳ－ＡＲＣＨ）;在此基础上,提出了ＡＲＣＨ预警方法,并分析了ＡＲＣＨ预警方法的特点,着重研究了ＡＲＣＨ预警警限的界定,给出了一种具有ＡＲＣＨ特征的警限界定方法     

Dynamic planned safety stocks in supply networks

Safety stocks are commonly used in inventory management for tactically planning against uncertainty in demand and/or supply. The usual approach is to plan a single safety stock value for the entire planning horizon. More advanced methods allow for dynamically updating this value. We introduce a new line of research in inventory management: the notion of planning time-phased safety stocks. We assert that planning a time-phased set of safety stocks over a planning horizon makes sense because larger safety stocks are appropriate in times of greater uncertainty while lower safety stocks are more appropriate when demand and/or supply are more predictable. Projecting a vector of safety stock values is necessary to assure upstream members in the supply network have advanced warning of changes. We perform an empirical study of U.S. industry, which demonstrates that significant savings can be achieved by employing dynamic planned safety stocks, confirming recent case study reports. We provide a simple optimisation model for the problem of minimising inventory given a vector of safety stock targets. We propose a computationally efficient solution procedure and demonstrate its implementation in an MRP/ERP system. We then illustrate an MRP/ERP planning system feature, which employs a dynamic planned safety stock module that supports a production planner by showing the inventory implications of safety stock plans.     

流域变化环境下水文非平稳异方差序列随机建模研究进展

气候变化和高强度人类活动的显著影响使流域水文序列出现异方差性,破坏了Box-Jekins法ARMA模型建立的前提条件。基于国内外相关研究进展和存在的主要科学问题,提出了目前几个主要研究内容和研究途径。金融学、经济学和信号处理等领域的观测序列与流域变化环境下水文非平稳序列异方差性波动特性相似,而且这些领域中已取得了许多成功的案例。应用金融学、经济学和信号处理等领域的异方差性原理和方法,结合流域水文特性,开展变化环境下水文非平稳异方差序列随机建模是可行的,与分布式水文模型相比,该方法是一种实用的建模途径。文中提出的一些思路可为变化环境下精确描述流域水文要素值的变化规律、涉水工程的规划设计与科学管理以及干旱风险调控提供依据。