A large factor model for forecasting macroeconomic variables in South Africa

This paper uses large Factor Models (FMs), which accommodate a large cross-section of macroeconomic time series for forecasting the per capita growth rate, inflation, and the nominal short-term interest rate for the South African economy. The FMs used in this study contain 267 quarterly series observed over the period 1980Q1-2006Q4. The results, based on the RMSEs of one- to four-quarter-ahead out-of-sample forecasts from 2001Q1 to 2006Q4, indicate that the FMs tend to outperform alternative models such as an unrestricted VAR, Bayesian VARs (BVARs) and a typical New Keynesian Dynamic Stochastic General Equilibrium (NKDSGE) model in forecasting the three variables under consideration, hence indicating the blessings of dimensionality.     

Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task

We conducted laboratory experiments for analyzing the accuracy of three structured approaches (nominal groups, Delphi, and prediction markets) relative to traditional face-to-face meetings (FTF). We recruited 227 participants (11 groups per method) who were required to solve a quantitative judgment task that did not involve distributed knowledge. This task consisted of ten factual questions, which required percentage estimates. While we did not find statistically significant differences in accuracy between the four methods overall, the results differed somewhat at the individual question level. Delphi was as accurate as FTF for eight questions and outperformed FTF for two questions. By comparison, prediction markets did not outperform FTF for any of the questions and were inferior for three questions. The relative performances of nominal groups and FTF were mixed and the differences were small. We also compared the results from the three structured approaches to prior individual estimates and staticized groups. The three structured approaches were more accurate than participants’ prior individual estimates. Delphi was also more accurate than staticized groups. Nominal groups and prediction markets provided little additional value relative to a simple average of the forecasts. In addition, we examined participants’ perceptions of the group and the group process. The participants rated personal communications more favorably than computer-mediated interactions. The group interactions in FTF and nominal groups were perceived as being highly cooperative and effective. Prediction markets were rated least favourably: prediction market participants were least satisfied with the group process and perceived their method as the most difficult.     

人工神经网络峰值识别理论及其在洪水预报中的应用

本文在总结大量洪水预报实践经验的基础上，提出了一种峰值识别理论及相应的改进BP算法(Error Back Propagation with Peak Recognizer,简称BPPR).该理论及算法在修改网络权重时，偏重大值误差，即大值误差对权重的修改起主要作用.这种BPPR算法使人工神经网络洪水预报模型对洪峰的预报精度显著提高，从而保证了洪峰预报的可靠性.     

Predictive financial models of the euro area: A new evaluation test

This paper investigates the predictive ability of financial variables for euro area growth. Our forecasts are built from univariate autoregressive and single equation models. Euro area aggregate forecasts are constructed both by employing aggregate variables and by aggregating country-specific forecasts. The forecast evaluation is based on a recently developed test for equal predictive ability between nested models. Employing a monthly dataset from the period between January 1988 and May 2005 and setting the out-of-sample period to be from 2001 onwards, we find that the single most powerful predictor on a country basis is the stock market returns, followed by money supply growth. However, for the euro area aggregate, the set of most powerful predictors includes interest rate variables as well. The forecasts from pooling individual country models outperform those from the aggregate itself for short run forecasts, while for longer horizons this pattern is reversed. Additional benefits are obtained when combining information from a range of variables or combining model forecasts.     

定量分析中准确报告最终结果的方法

准确地出具检测数据对于各种实验室都是至关重要的。本文简要介绍了国际标准ISO5725-6:1994中对报告最终结果的统计处理方法。     

珊溪水电站施工控制网的模拟计算和优化设计

本文从对网形、观测精度、网的精度、可靠性、费用及其相互关系等分析出发,结合珊溪水电站施工控制网进行了较深入的研究,并按基于观测值内部可靠性的方法对该网进行了模拟计算和优化设计。     

三基地雷达最佳空间定位算法

将三基地雷达二维定位最佳算法加以推广,在不增加设备量的前提下,充分利用各雷达站的测量信息,对三基地雷达三维空间直线布站的最佳算法进行推导和研究.仿真结果显示,空间相关数据压缩算法比独立数据压缩算法更加有效地提高了目标定位精度,特别是较好地改善了发射站附近区域的定位性能,有效解决了双基地雷达空间定位的基线盲区问题.     

On the selection of forecasting models

It is standard in applied work to select forecasting models by ranking candidate models by their prediction mean squared error (PMSE) in simulated out-of-sample (SOOS) forecasts. Alternatively, forecast models may be selected using information criteria (IC). We compare the asymptotic and finite-sample properties of these methods in terms of their ability to mimimize the true out-of-sample PMSE, allowing for possible misspecification of the forecast models under consideration. We show that under suitable conditions the IC method will be consistent for the best approximating model among the candidate models. In contrast, under standard assumptions the SOOS method, whether based on recursive or rolling regressions, will select overparameterized models with positive probability, resulting in excessive finite-sample PMSEs.     

直升机载雷达侦察系统目标定位精度分析

本文介绍了直升机载侦察雷达对目标的定位原理和定位过程,分析了定位过程中载机自身定位的方法和坐标转换的过程,并在此基础上给出了影响直升机载侦察雷达对目标定位误差的因素及误差组成。     

经贸文体翻译之生命——准确性

实现经贸文体翻译的准确性,必须准确把握词汇含义,理解文化内涵,表现文体风格。