Guaranteed‐content prediction intervals for non‐linear autoregressions

In this paper we present guaranteed‐content prediction intervals for time series data. These intervals are such that their content (or coverage) is guaranteed with a given high probability. They are thus more relevant for the observed time series at hand than classical prediction intervals, whose content is guaranteed merely on average over hypothetical repetitions of the prediction process. This type of prediction inference has, however, been ignored in the time series context because of a lack of results. This gap is filled by deriving asymptotic results for a general family of autoregressive models, thereby extending existing results in non‐linear regression. The actual construction of guaranteed‐content prediction intervals directly follows from this theory. Simulated and real data are used to illustrate the practical difference between classical and guaranteed‐content prediction intervals for ARCH models. Copyright © 2001 John Wiley & Sons, Ltd.     

浅谈工程造价的控制与管理

文章分别从建设工程的投资决策、设计、招投标、施工4个阶段分析了工程造价的控制与管理。     

R~n中不存在既开又闭的非空真子集

证明了R~n 中不存在即开又闭的非空真子集     

R3中紧致凸曲面的等周不等式

∑是R^3中紧致凸曲面,∑上有一定长为L的简单闭曲线C,由C围成的区域D的面积为A,利用△∑r^2=4＋4H（r,n）、散度定理和庞加莱不等式,得等周不等式为L^2≥4πA＋4πp0H0,其中H0=min{Hp,Hp为∑在p点的平均曲率},p0=max{（r,n〉p,（r,n）p为三在p点的支撑函数的值}。     

基于闭环DNA的边着色问题DNA算法

提出一种新的DNA计算模型——闭环DNA计算模型。引进了批删除实验。讨论了其实现过程;提出并证明了边着色问题的基本定理,设计并实现了闭环DNA计算算法．该算法将边的DNA编码分为两部分,一部分存储边和色位置的二维数据,另一部分存储色号值;在DNA计算的主体部分用批删除实验得到全部正常的边着色,并通过电泳实验和检测实验获得χ′^-正常边着色．举例说明了算法的有效性和可行性．     

Multi‐step forecasting in the presence of breaks

This paper analyzes the relative performance of multi‐step AR forecasting methods in the presence of breaks and data revisions. Our Monte Carlo simulations indicate that the type and timing of the break affect the relative accuracy of the methods. The iterated autoregressive method typically produces more accurate point and density forecasts than the alternative multi‐step AR methods in unstable environments, especially if the parameters are subject to small breaks. This result holds regardless of whether data revisions add news or reduce noise. Empirical analysis of real‐time US output and inflation series shows that the alternative multi‐step methods only episodically improve upon the iterated method.     

Predicting crypto‐currencies using sparse non‐Gaussian state space models

In this paper we forecast daily returns of crypto‐currencies using a wide variety of different econometric models. To capture salient features commonly observed in financial time series like rapid changes in the conditional variance, non‐normality of the measurement errors and sharply increasing trends, we develop a time‐varying parameter VAR with t‐distributed measurement errors and stochastic volatility. To control for overparametrization, we rely on the Bayesian literature on shrinkage priors, which enables us to shrink coefficients associated with irrelevant predictors and/or perform model specification in a flexible manner. Using around one year of daily data, we perform a real‐time forecasting exercise and investigate whether any of the proposed models is able to outperform the naive random walk benchmark. To assess the economic relevance of the forecasting gains produced by the proposed models we, moreover, run a simple trading exercise.     

Financial density forecasts: A comprehensive comparison of risk‐neutral and historical schemes

We investigate the forecasting ability of the most commonly used benchmarks in financial economics. We approach the usual caveats of probabilistic forecasts studies—small samples, limited models, and nonholistic validations—by performing a comprehensive comparison of 15 predictive schemes during a time period of over 21 years. All densities are evaluated in terms of their statistical consistency, local accuracy and forecasting errors. Using a new composite indicator, the integrated forecast score, we show that risk‐neutral densities outperform historical‐based predictions in terms of information content. We find that the variance gamma model generates the highest out‐of‐sample likelihood of observed prices and the lowest predictive errors, whereas the GARCH‐based GJR‐FHS delivers the most consistent forecasts across the entire density range. In contrast, lognormal densities, the Heston model, or the nonparametric Breeden–Litzenberger formula yield biased predictions and are rejected in statistical tests.     

关于近似强仿紧空间

引入了近似强仿紧空间的定义,给出了近似强仿紧空间的一些刻画,并且讨论了它们的映射性质.     

轿车后三角玻璃及包边的重构与拼接

为了检测轿车后三角包边玻璃与标准样件之间的误差,提出包边玻璃点云参数化重构和拼接方法.根据后三角包边玻璃的测量要求,在获得其点云已消噪的情况下,应用B样条法构造出玻璃双二次加权曲面参数方程.根据过渡曲面一阶导数连续的性质,导出交于公共边界的控制网格矢量之间的关系,研究其不同分片曲面之间的光滑拼接方法,并举例验证方法的有效性.检测结果表明,重构曲面经过分层切片,可以用于检验与标准样件之间的误差,而且该算法运算量较少,精度可控制.