Further Evidence on the Great Crash,the Oil-Price Shock,and the Unit-Root Hypothesis

Recently, Perron has carried out tests of the unit-root hypothesis against the alternative hypothesis of trend stationarity with a break in the trend occurring at the Great Crash of 1929 or at the 1973 oil-price shock. His analysis covers the Nelson–Plosser macroeconomic data series as well as a postwar quarterly real gross national product (GNP) series. His tests reject the unit-root null hypothesis for most of the series. This article takes issue with the assumption used by Perron that the Great Crash and the oil-price shock can be treated as exogenous events. A variation of Perron's test is considered in which the breakpoint is estimated rather than fixed. We argue that this test is more appropriate than Perron's because it circumvents the problem of data-mining. The asymptotic distribution of the estimated breakpoint test statistic is determined. The data series considered by Perron are reanalyzed using this test statistic. The empirical results make use of the asymptotics developed for the test statistic as well as extensive finite-sample corrections obtained by simulation. The effect on the empirical results of fat-tailed and temporally dependent innovations is investigated, in brief, by treating the breakpoint as endogenous, we find that there is less evidence against the unit-root hypothesis than Perron finds for many of the data series but stronger evidence against it for several of the series, including the Nelson-Plosser industrial-production, nominal-GNP, and real-GNP series.     

HAC法在平稳过程之间伪回归中的适用性研究

通过蒙特卡罗模拟技术揭示各种HAC法在平稳过程伪回归中的适用性.研究发现,与核权函数HAC相比,预白化HAC法具有明显的优势;进一步的研究表明相对于被解释变量的持久性,解释变量的持久性对HAC的影响较大;当数据过程是高阶自回归过程时,在样本容量不是很大的情况下预白化方法的拒绝率会随着阶数增加而增大,只有在样本容量较大和BIC信息准则情况下预白化HAC的拒绝率才接近检验水平.     

Change Point Estimation with Independent Observations and Piece-Wise Continuous Variance Function

Grégoire and Hamrouni use locally linear smoothers to find jumps in the mean of a regression function under a set of weak assumptions. This paper extends this work to find jumps in the variance function of a mean zero series of independent observations. We transform this problem into the problem considered by Grégoire and Hamrouni by means of a log transform. We also demonstrate that a bootstrap technique proposed by Gijbels and Goderniaux is valid in this setting.     

Evidence of ARCH(1) Errors in the Context of Spurious Regressions

The spurious regression phenomenon is related to first-order serially correlated errors. This study, using a Monte Carlo analysis, finds that this phenomenon is also related to ARCH(1) type errors.     

On nonlinear models for time series

The paper is a review of nonlinear processes used in time series analysis and presents some new original results about stationary distribution of a nonlinear autoregres-sive process of the first order. The following models are considered: nonlinear autoregessive processes, threshold AR processes, threshold MA processes, bilinear models, auto-regressive models with random parameters including double stochastic models, exponential AR models, generalized threshold models and smooth transition autoregressive models, Some tests for linearity of processes are also presented.     

Robust parameter estimation of regression model with AR(p) error terms

In this article, we consider a linear regression model with AR(p) error terms with the assumption that the error terms have a t distribution as a heavy-tailed alternative to the normal distribution. We obtain the estimators for the model parameters by using the conditional maximum likelihood (CML) method. We conduct an iteratively reweighting algorithm (IRA) to find the estimates for the parameters of interest. We provide a simulation study and three real data examples to illustrate the performance of the proposed robust estimators based on t distribution.     

基于平稳Contourlet变换的极化SAR图像融合

提出一种新颖而有效的基于平稳Contourlet变换的极化SAR图像融合算法。平稳Contourlet变换是一种具有几何信息的灵活多尺度、多方向和平移不变性的图像分解变换,与小波变换相比,对图像分析很重要的沿曲面任意方向反映的细节更容易调整。采用平稳Contourlet变换对多个单极化强度图像进行分解,对于低频系数和方向高频系数采用最优加权算法实现极化图像的融合处理。实验结果表明,该算法与PWF算法相比在保留原始图像边缘和纹理信息同时,可以有效地抑制相干斑噪声的影响,取得较好的融合视觉效果。     

Time series,point processes,and hybrids

Techniques developed for the study of time series, point processes, and marked point processes can suggest corresponding techniques for each other, and common techniques can be recognized. In this paper connections are drawn based on conceptual foundations, basic parameters, analyses, displays, algorithms, problems, models. The definitions and techniques are brought out by specific scientific problems. The emphasis is on the single-realization stationary case and on the use of second- and third-order moments to help understand the realization. The tool of stacking, at a particular period, is employed in several of the examples.