Combining information for prediction in linear regression

This article deals with the prediction problem in linear regression where the measurements are obtained using k different devices or collected from k different independent sources. For the case of k=2, a Graybill-Deal type combined estimtor for the regression parameters is shown to dominate the individual least squares estimators under the covariance criterion. Two predictors ŷ _c and ŷ _p are proposed. ŷ _c is based on a combined estimator of the regression coefficient vector, and ŷ _p is obtained by combining the individual predictors from different models. Prediction mean square errors of both predictors are derived. It is shown that the predictor ŷ _p is better than the individual predictors for k≥2 and the predictor ŷ _c is better than the individual predictors for k=2. Numerical comparison between ŷ _c and ŷ _p shows that the former is superior to the latter for the case k=2.     

河南农村物流需求的组合预测及策略研究

河南作为农业大省大力发展农村物流对其率先实现中部崛起有着很重要的现实意义,预测河南农村物流需求对于制定发展战略显得尤为重要。文中以河南农村消费品零售总额为河南农村物流需求预测指标,综合一元线性回归、时间序列双指数平滑法、移动平均法,建立组合预测模型,追求预测误差平方和最小,预测出河南农村物流需求呈良性发展趋势,并就进一步发展河南农村物流提出建议。     

中国货币需求函数的协整分析

本文从目前中国货币需求现状出发,修正了1978年以来中国的货币需求函数,并运用协整分析对中国相关变量的年度数据(1978—2004年)进行实证分析,得出以下结论:在长期内,实际M1余额、实际GDP、一年期定期存款利率和货币化进程指数间存在协整关系;实际M2余额、实际GDP、商品零售物价指数和货币化指数间存在协整关系。在短期货币函数中,对货币需求的主要影响因素包括实际GDP和货币化进程,实际M1向均衡水平调整的速度要大于实际M2的调整速度,另外,短期货币需求函数不稳定。     

Introducing errors in progress ratios determined from experience curves

Progress ratios (PRs) derived from historical data in experience curves are used for forecasting development of many technologies as a means to model endogenous technical change in for instance climate–economy models. These forecasts are highly sensitive to uncertainties in the progress ratio. As a progress ratio is determined from fitting data, a coefficient of determination R² is frequently used to show the quality of the fit and accuracy of PR. Although this is instructive, we recommend using the error σ_PR in PR, which can be directly determined from fitting the data. In this paper we illustrate this approach for three renewable energy technologies, i.e., wind energy, bio-ethanol, and photovoltaics.     

Analysts' Forecasts Following Forced CEO Changes

This paper examines analysts' earnings forecasts during the period of uncertainty following a change of chief executive officer (CEO). It distinguishes between forced and non‐forced CEO changes, and examines whether analysts utilize their information advantage to reduce the heightened uncertainty of a forced change of CEO. Examining a sample of Australian companies followed by analysts between 1999 and 2009, we find that forecasting accuracy is lower and earnings forecasts are more optimistic for firms experiencing forced CEO turnover compared to firms not undergoing such a change. However, dispersion is not statistically different. The results suggest that forced CEO turnover events provide a challenge to the forecasting environment for analysts. During CEO changes, investors should be aware that forecasts are less accurate and have an optimistic bias.     

A panel data approach to economic forecasting: The bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zero-mean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise, based upon data from a well known survey is also presented. Overall, these results show promise for the feasible bias-corrected average forecast.     

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one model’s outperformance of another.     

野外施工中超声波探伤检测误差的影响因素分析及对策

上油气管道安装施工中,因现场条件限制,管理环境和技术环境的恶化,使超声波探伤质量变异性增加,检测误差增大,严重地影响超声波探伤的准确性和权威性,甚至危及油气输送管道的正常生产。就野外超声波探伤检测误差增大的诸因素进行较全面的分析,并根据帕累托原理提出了相应对策,以促使上超声波务检测误差降低。