最小二乘法对多变点检验的性能研究

给出了衡量最小二乘法识别多变点能力的方法,模拟研究了最小二乘法对不同数据生成过程的多变点检测效果,指出了最小二乘法的适用性,最后应用最小二乘法检测了中国主要经济部门的GDP变点.     

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.     

Realized Volatility Forecast of Stock Index Under Structural Breaks

We investigate the realized volatility forecast of stock indices under the structural breaks. We utilize a pure multiple mean break model to identify the possibility of structural breaks in the daily realized volatility series by employing the intraday high‐frequency data of the Shanghai Stock Exchange Composite Index and the five sectoral stock indices in Chinese stock markets for the period 4 January 2000 to 30 December 2011. We then conduct both in‐sample tests and out‐of‐sample forecasts to examine the effects of structural breaks on the performance of ARFIMAX‐FIGARCH models for the realized volatility forecast by utilizing a variety of estimation window sizes designed to accommodate potential structural breaks. The results of the in‐sample tests show that there are multiple breaks in all realized volatility series. The results of the out‐of‐sample point forecasts indicate that the combination forecasts with time‐varying weights across individual forecast models estimated with different estimation windows perform well. In particular, nonlinear combination forecasts with the weights chosen based on a non‐parametric kernel regression and linear combination forecasts with the weights chosen based on the non‐negative restricted least squares and Schwarz information criterion appear to be the most accurate methods in point forecasting for realized volatility under structural breaks. We also conduct an interval forecast of the realized volatility for the combination approaches, and find that the interval forecast for nonlinear combination approaches with the weights chosen according to a non‐parametric kernel regression performs best among the competing models. Copyright © 2014 John Wiley & Sons, Ltd.     

Spurious Forecasts?

P. C. B. Phillips (1998) demonstrated that deterministic trends are a valid representation of an otherwise stochastic trending mechanism; he remained skeptic, however, about the predictive power of such representations. In this paper we prove that forecasts built upon spurious regression may perform (asymptotically) as well as those issued from a correctly specified regression. We derive the order in probability of several in‐sample and out‐of‐sample predictability criteria ( test, root mean square error, Theil's U‐statistics and R²) using forecasts based upon a least squares‐estimated regression between independent variables generated by a variety of empirically relevant data‐generating processes. It is demonstrated that, when the variables are mean stationary or trend stationary, the order in probability of these criteria is the same whether the regression is spurious or not. Simulation experiments confirm our asymptotic results. Copyright © 2011 John Wiley & Sons, Ltd.     

基于杠杆效应和结构突变的HAR族模型及其对股市波动率的预测研究

近年来,基于高频交易数据的HAR族模型在对各类金融市场波动率的预测研究中展现出了良好的预测效果.本文在4个经典或前沿的HAR族模型的基础上,考虑杠杆效应和结构突变因素对波动率的预测作用,构建4个带杠杆效应和结构突变的HAR族模型.接着,以上证综指和深证成指的5分钟高频交易数据为研究样本,对上述模型进行样本内和样本外分析,以此检验各成分对股市波动率的预测作用以及比较各模型的预测能力.实证结果显示:已实现波动率,连续波动率,下行波动率,上行波动率,杠杆效应和结构突变成分对股市波动率的预测作用较强,而跳跃波动率,符号跳跃方差对股市波动率的预测作用较弱;带杠杆效应和结构突变的HAR族模型对股市波动率的样本内拟合效果和样本外预测能力都明显优于相对应的不带杠杆和结构突变的HAR族模型,其中大多数情况下LHAR-CJ-SB模型展现出最高的拟合效果和预测精度.以上结果表明,杠杆效应和结构突变因素能有效提高HAR族模型的预测精度,所以在HAR族模型的构建中这两个因素不能被忽视.     

Forecasting the Effects of a Canada–US Currency Union on Output and Prices: A Counterfactual Analysis

This paper is a counterfactual analysis investigating the consequences of the formation of a currency union for Canada and the USA: whether outputs increase and prices decrease if these countries form a currency union. We use a two‐country cointegrated model to conduct the counterfactual analysis, where the conditional forecasts are generated based on the Gaussian assumption. To deal with structural breaks and model uncertainty, conditional forecasts are generated from different models/estimation windows and the model‐averaging technique is used to combine the forecasts. We also examine the robustness of our results to parameter uncertainty using the wild bootstrap method. The results show that forming the currency union would probably boost the Canadian economy, whereas it would not have significant effects on US output or Canadian and US price levels. Copyright © 2013 John Wiley & Sons, Ltd.     

桥梁半平行钢丝索对称断丝的力学特性分析

桥梁用半平行钢丝索由于外层钢丝的螺旋外形和本身构造的特殊性,不能直接应用基于平行钢丝假定的并联模型或钢绞线模型进行力学分析.基于Love曲杆理论,考虑泊松效应、钢丝间接触变形以及钢丝间的接触力和摩擦力的影响,对多层半平行钢丝索的对称断丝模型进行了修正,通过数值计算讨论了拉索截面钢丝拉力分布规律、钢丝拉力增长系数和拉索轴向刚度及其影响因素.计算表明,考虑接触变形后,截面内钢丝拉力分布不均匀,拉索轴向刚度较单根钢丝有较大幅度的下降.     

结构突变条件下农产品期货市场波动率的预测

在检验农产品期货已实现波动率序列的结构突变等特征基础上,通过构造不同估计窗口大小的ARFIMAX-FIGARCH模型及其线性和非线性组合预测模型来预测农产品期货市场的已实现波动率,并采用基于自助法的MCS检验评价和比较各类预测模型的预测性能。研究结果表明:农产品期货的已实现波动率序列都表现出结构突变特征、不对称性和双长记忆性,并且结构突变点都与一连串的宏观面、政策面重大事件冲击有关;对基于不同估计窗口大小的ARFIMAX-FIGARCH模型所得的单项预测值进行时变加权组合通常能够提供更准确的波动率预测值,并且基于NKR的非参数组合预测模型和基于NRLS和SIC的线性组合预测模型是在结构突变条件下预测农产品期货市场波动率尤其有效的方法。     

混凝土结构裂缝状态及其扩展的分形几何解析

应用分形（ｆｒａｃｔａｌ）理论研究混凝土结构几何损伤形态，分析了分维解析的适用条件，对混凝土结构损伤开裂形态、裂缝覆盖率、总延长和分歧延伸作了定量化解析；讨论了分形特征值和混凝土强度、裂缝宽度的关系；为混凝土结构损伤几何形态及耐久性的定量评价提供新的解析手段