大学英语考试研究二十年述评

大学英语四、六级考试在我国有着很高的权威性和关注度,二十年来,有关该项考试的研究无论在深度还是广度上都有长足的发展。但是对于该项考试的研究还存在着视角偏窄、实证性研究比例偏低、学习主体研究不够等问题。随着对该项考试研究的不断深入,研究方法将呈现多元化、理性化、规范化的趋势,研究内容涉及的视角将越来越广,试特针对测试行为的影响会受到更多关注,对于开发利用高科技、实现考试手段和考务管理的现代化研究将加强。     

Robust evaluation of fixed‐event forecast rationality

In this paper we introduce a new testing procedure for evaluating the rationality of fixed‐event forecasts based on a pseudo‐maximum likelihood estimator. The procedure is designed to be robust to departures in the normality assumption. A model is introduced to show that such departures are likely when forecasters experience a credibility loss when they make large changes to their forecasts. The test is illustrated using monthly fixed‐event forecasts produced by four UK institutions. Use of the robust test leads to the conclusion that certain forecasts are rational while use of the Gaussian‐based test implies that certain forecasts are irrational. The difference in the results is due to the nature of the underlying data. Copyright © 2001 John Wiley & Sons, Ltd.     

Information content of DSGE forecasts

This paper examines the question whether information is contained in forecasts from dynamic stochastic general equilibrium (DSGE) models beyond that contained in lagged values, which are extensively used in the models. Four sets of forecasts are examined. The results are encouraging for DSGE forecasts of real GDP. The results suggest that there is information in the DSGE forecasts not contained in forecasts based only on lagged values, and that there is no information in the lagged‐value forecasts not contained in the DSGE forecasts. The opposite is true for forecasts of the GDP deflator.     

The predictive value of temporally disaggregated volatility: evidence from index futures markets

This paper examines the benefits to forecasters of decomposing close-to-close return volatility into close-to-open (nighttime) and open-to-close (daytime) return volatility. Specifically, we consider whether close-to-close volatility forecasts based on the former type of (temporally aggregated) data are less accurate than corresponding forecasts based on the latter (temporally disaggregated) data. Results obtained from seven different US index futures markets reveal that significant increases in forecast accuracy are possible when using temporally disaggregated volatility data. This result is primarily driven by the fact that forecasts based on such data can be updated as more information becomes available (e.g., information flow from the preceding close-to-open/nighttime trading session). Finally, we demonstrate that the main findings of this paper are robust to the index futures market considered, the way in which return volatility is constructed, and the method used to assess forecast accuracy. Copyright © 2008 John Wiley & Sons, Ltd.     

构建中国的家庭理财调查体系--美英的经验比较及对中国的借鉴

西方一些发达国家定期进行全国性家庭理财调查,所收集的相关微观数据为政府的经济与社会决策和学术研究提供了重要的依据:中国目前尚未建立起全国性的家庭理财调查体系。本文通过比较分析美国的“消费者理财调查”和英国的“家庭资源调查”,就如何借鉴美英的经验、构建中国的家庭理财调查体系进行初步的探讨。     

Combination of forecasts using self‐organizing algorithms

Based on the theories and methods of self‐organizing data mining, a new forecasting method, called self‐organizing combining forecasting method, is proposed. Compared with optimal linear combining forecasting methods and neural networks combining forecasting methods, the new method can improve the forecasting capability of the model. The superiority of the new method is justified and demonstrated by real applications. Copyright © 2005 John Wiley & Sons, Ltd.     

Forecasting from non-linear models in practice

If a simple non-linear autoregressive time-series model is suggested for a series, it is not straightforward to produce multi-step forecasts from it. Several alternative theoretical approaches are discussed and then compared with a simulation study only for the two-step case. It is suggested that fitting a new model for each forecast horizon may be a satisfactory strategy.     

Estimating the strength of expert judgement: The case of US mortality forecasts

The use of expert judgement is an important part of demographic forecasting. However, because judgement enters into the forecasting process in an informal way, it has been very difficult to assess its role relative to the analysis of past data. The use of targets in demographic forecasts permits us to embed the subjective forecasting process into a simple time-series regression model, in which expert judgement is incorporated via mixed estimation. The strength of expert judgement is denned, and estimated using the official forecasts of cause-specific mortality in the United States. We show that the weight given to judgement varies in an improbable manner by age. Overall, the weight given to judgement appears too high. An alternative approach to combining expert judgement and past data is suggested.     

Judgmental adjustment of earnings forecasts

Earnings forecasts have received a great deal of attention, much of which has centered on the comparative accuracy of judgmental and objective forecasting methods. Recently, studies have focused on the use of combinations of subjective and objective forecasts to improve forecast accuracy. This research offers an extension on this theme by subjectively modifying an objective forecast. Specifically, ARIMA forecasts are judgmentally adjusted by analysts using a structured approach based on Saaty's (1980) analytic hierarchy process. The results show that the accuracy of the unadjusted objective forecasts can be improved when judgmentally adjusted.