Forecasting recessions using the yield curve

We compare forecasts of recessions using four different specifications of the probit model: a time invariant conditionally independent version; a business cycle specific conditionally independent model; a time invariant probit with autocorrelated errors; and a business cycle specific probit with autocorrelated errors. The more sophisticated versions of the model take into account some of the potential underlying causes of the documented predictive instability of the yield curve. We find strong evidence in favour of the more sophisticated specification, which allows for multiple breakpoints across business cycles and autocorrelation. We also develop a new approach to the construction of real time forecasting of recession probabilities. Copyright © 2005 John Wiley & Sons, Ltd.     

The role of repair in radiobiology

Summary Apart from cancer and mutation induction, radiobiological effects on mammals are mostly attributable to cell death, defined as loss of proliferative capacity. Survival curves relate retention of that capacity to radiation dose, and often manifest a quasi-threshold (shoulder). The shoulder is attributable to an initial mechanism of repair (Q-repair) which is gradually depleted as dose increases. Another form of repair, which is not depleted (P-repair), increases the dose required to deliver an average of one lethal event per cell (dose D₀). Neither form of repair can unambiguously be linked with repair of defects in isolated DNA. An important initial lesion may well be disruption of the complex structural relationship between the DNA, nuclear membrane and associated proteins. One form of P-repair may be restoration of that structural relationship.     

Forecasting in the presence of level shifts

This article addresses the problem of forecasting time series that are subject to level shifts. Processes with level shifts possess a nonlinear dependence structure. Using the stochastic permanent breaks (STOPBREAK) model, I model this nonlinearity in a direct and flexible way that avoids imposing a discrete regime structure. I apply this model to the rate of price inflation in the United States, which I show is subject to level shifts. These shifts significantly affect the accuracy of out‐of‐sample forecasts, causing models that assume covariance stationarity to be substantially biased. Models that do not assume covariance stationarity, such as the random walk, are unbiased but lack precision in periods without shifts. I show that the STOPBREAK model outperforms several alternative models in an out‐of‐sample inflation forecasting experiment. Copyright © 2005 John Wiley & Sons, Ltd.     

Forecast robustness in macroeconometric models

This paper investigates potential invariance of mean forecast errors to structural breaks in the data generating process. From the general forecasting literature, such robustness is expected to be a rare occurrence. With the aid of a stylized macro model we are able to identify some economically relevant cases of robustness and to interpret them economically. We give an interpretation in terms of co‐breaking. The analytical results resound well with the forecasting record of a medium‐scale econometric model of the Norwegian economy.     

混凝土(RC/PC)开孔梁裂缝开展特性的研究

现代建筑结构中有许多辅助设施 ,目前这些辅助设施的管道一般都从梁下交错穿过 ,这样 ,势必增加了层高 ,尤其对高层建筑来讲 ,就大大增加了建筑物的总高度和自重 为了解决这个问题就出现了开孔梁 ,让管道从孔中穿过 目前对混凝土开孔梁的研究 ,主要集中在强度方面 ,对使用性能如裂缝的研究涉及较少 笔者通过对十根混凝土开孔梁的对比试验分析 ,对开孔梁抗裂特点及裂缝开展的规律进行了定性分析及定量研究 ,并建议采用名义拉应力法计算开孔梁裂缝宽度 ,计算结果表明 ,本文所示方法简便、适用 ,可直接应用于工程设计     

Break Detectability and Mean Square Forecast Error Ratios for Selecting Estimation Windows

It has been suggested that a major problem for window selection when we estimate models for forecasting is to empirically determine the timing of the break. However, if the window choice between post‐break or full sample is based on mean square forecast error ratios, it is difficult to understand why such a problem arises since break detectability and these ratios seem to have the same determinants. This paper analyses this issue first for the expected values in conditional models and then by Monte Carlo simulations for more general cases. Results show similar behaviour between rejection frequencies and the ratios but only for break tests that do not take into account forecasting error covariances, as is the case with mean square forecast error measures. Moreover, the asymmetric shape of the frequency distribution of the ratios could help us to better grasp empirical problems. An illustration using actual data is given. Copyright © 2011 John Wiley & Sons, Ltd.     

基于跳扩散模型的石油价格长期趋势分析

分析了国际石油市场1986至2012年周价格形成机制的长期演变趋势.在讨论均衡理论基础上,以长期市场供求关系解释了国际油价长期波动现象.基于跳扩散模型拟合石油价格动态过程,利用结构变点检验和累积量估计方法进行了实证研究.历史数据分析表明石油价格具有高波动性、高强度跳跃性和上升漂移特征.此外,模型预测即使当前大幅增加石油投资,未来几年内石油价格变化仍会处于一种高频跳跃的上行阶段.     

细胞周期末期促进复合物亚基CDC16Hs与DNA双链断端修复蛋白Ku80相互作用

CDC16Hs是细胞周期末期促进复合物(APC)的亚基．利用基于LexA的酵母双杂交系统,把它作为诱饵蛋白筛选人胎脑文库,发现它与DNA双链断端修复蛋白Ku80的羧基端相互作用．CDC16Hs和全长Ku80的结合通过pull down实验在体外得到验证．     

基于倒变异高阶谱的周期性突变识别方法

分析了带有周期性突变成分信号的频域特征,提出了识别周期性突变的倒变异高阶谱方法,该方法首先对信号的FFT所得数据进行调整,并根据周期性突变的特点设计了新的高阶谱频率函数,由频率函数可得到变异高阶谱,进而对变异高阶谱的某些频率取纵向切片计算倒谱,得到倒变异高阶谱.基于倒变异高阶谱的周期性突变识别方法可充分利用FFT所得的正负频率域的所有数据,扩充了高阶谱的生成空间,提高了对周期性突变的识别效果.试验表明所提方法可准确识别周期性突变和突变的周期,具有一定的实际应用价值.     

A state-dependent linear recurrent formula with application to time series with structural breaks

An underlying assumption in Multivariate Singular Spectrum Analysis (MSSA) is that the time series are governed by a linear recurrent continuation. However, in the presence of a structural break, multiple series can be transferred from one homogeneous state to another over a comparatively short time breaking this assumption. As a consequence, forecasting performance can degrade significantly. In this paper, we propose a state-dependent model to incorporate the movement of states in the linear recurrent formula called a State-Dependent Multivariate SSA (SD-MSSA) model. The proposed model is examined for its reliability in the presence of a structural break by conducting an empirical analysis covering both synthetic and real data. Comparison with standard MSSA, BVAR, VAR and VECM models shows the proposed model outperforms all three models significantly.