鸟卵重量经验公式的改进

对Ｂｕｒｇｅｒｄ等给出的关于鸟卵重量的经验公式进行了验证。根据线笥回归分析的原理，推导出用鸟卵的长径和短径来预测鸟卵重量误差较小的数学公式，从而推断鸟卵的新鲜程序。利用汉字Ｆｏｘｂａｓｅ＋语言编写出一个求预测公式中系数的程序（ｎｄｆｘ．ｐｒｇ）。程序ｎｄｆｘ．ｐｒｇ同样适用于各处鸟卵预测公式的建立。     

高速公路与下伏采空区相互作用分析

运用非线性有限元软件ADINA对高速公路下伏采空区进行模拟,模拟考虑了采空区覆岩体三带:冒落带、裂隙带和弯曲变形带.通过使采空区位置单元的"死"单元复活生成"生"单元,并赋以活单元新的计算参数来达到计算目的.模型的建立及参数的选取均以枣木高速公路木石段高速公路下伏采空区地层作为基础,并适当进行简化调整.建立三维模型对采空区与高速公路空间位置影响进行模拟,并对采空区倾角、内部充填效果、深厚比等的不同进行模拟,探讨规律,并编制MAT:LAB软件预测程序.     

Forecasting in long horizons using smoothed direct forecast

This paper constructs a forecast method that obtains long‐horizon forecasts with improved performance through modification of the direct forecast approach. Direct forecasts are more robust to model misspecification compared to iterated forecasts, which makes them preferable in long horizons. However, direct forecast estimates tend to have jagged shapes across horizons. Our forecast method aims to “smooth out” erratic estimates across horizons while maintaining the robust aspect of direct forecasts through ridge regression, which is a restricted regression on the first differences of regression coefficients. The forecasts are compared to the conventional iterated and direct forecasts in two empirical applications: real oil prices and US macroeconomic series. In both applications, our method shows improvement over direct forecasts.     

Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts

By linking measures of forecast accuracy as well as testing procedures with regard to forecast rationality this paper investigates aggregated survey forecasts with forecast horizons of 3, 12, and 24 months for the exchange rates of the Chinese yuan, the Hong Kong dollar, the Japanese yen, and the Singapore dollar vis-à-vis the US dollar and, hence, for four different currency regimes. The rationality of the exchange rate predictions is initially assessed utilizing tests for unbiasedness and efficiency which indicate that the investigated forecasts are irrational in the sense that the predictions are biased. As one major contribution of this paper, it is subsequently shown that these results are not consistent with an alternative, less restrictive, measure of rationality. Investigating the order of integration of the time series as well as cointegrating relationships, this empirical evidence supports the conclusion that the majority of forecasts are in fact rational. Regarding forerunning properties of the predictions, the results are rather mediocre, with shorter term forecasts for the tightly managed USD/CNY FX regime being one exception. As one additional important and novel evaluation result, it can be concluded, that the currency regime matters for the quality of exchange rate forecasts.     

Combining multivariate volatility forecasts using weighted losses

The ability to improve out-of-sample forecasting performance by combining forecasts is well established in the literature. This paper advances this literature in the area of multivariate volatility forecasts by developing two combination weighting schemes that exploit volatility persistence to emphasise certain losses within the combination estimation period. A comprehensive empirical analysis of the out-of-sample forecast performance across varying dimensions, loss functions, sub-samples and forecast horizons show that new approaches significantly outperform their counterparts in terms of statistical accuracy. Within the financial applications considered, significant benefits from combination forecasts relative to the individual candidate models are observed. Although the more sophisticated combination approaches consistently rank higher relative to the equally weighted approach, their performance is statistically indistinguishable given the relatively low power of these loss functions. Finally, within the applications, further analysis highlights how combination forecasts dramatically reduce the variability in the parameter of interest, namely the portfolio weight or beta.     

Model averaging estimation for conditional volatility models with an application to stock market volatility forecast

This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight-choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes the true Kullback–Leibler divergence, which is the distributional approximation error, as well as the Itakura–Saito distance, which is the distance between the true and estimated or forecast conditional volatility. Monte Carlo experiments support our newly proposed method. As for the empirical applications of our method, we investigate a total of nine major stock market indices and make a 1-day-ahead volatility forecast for each data set. Empirical results show that the model averaging forecast achieves the highest accuracy in terms of all types of loss functions in most cases, which captures the movement of the unknown true conditional volatility.     

Analysts’ Dynamic Decisions: Timeliness versus Accuracy

For analysts there is a tradeoff between the accuracy and the timeliness of their forecasts. Prior literature heavily investigates analyst forecast accuracy. Few papers investigate the importance of timeliness. To our best knowledge, there are no empirical papers to date to investigate the dynamic interplay between these key characteristics. We show that if analysts experience a period of high accuracy relative to their peers, they subsequently focus more on the timeliness of their forecasts in the subsequent period and thus issue their forecasts earlier than they did in the prior period. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

滑坡变形阶段的划分及短期预报研究

滑坡是一种重要的地质灾害,一旦发生,就会带来严重的后果,本文研究了滑坡变形阶段的划分问题,并在此基础上提出了每个阶段应该进行的相应工作及目的。当滑坡进入加速变形阶段后期,出于防灾减灾的需要,此时准确预报滑坡发生时间显得尤为重要,结合实际工程本文研究了滑坡进入加速变形阶段的短期预报问题,并取得了较好的效果。     

我国居民消费水平的中长期预测

根据全国1992－1999年居民消费水平的统计数据，采用灰色系统理论的数据预测及缓冲算子公理，结合定性分析，建立了GM（1，1）模型，预测了我国2000－2005年居民消费水平的发展前景。