Forecast intervals of net migration: the case of the Netherlands

This study addresses problems concerning the forecasting of net migration in the preparation of population forecasts. "As the width of forecast intervals for migration in single years differs strongly from that of an interval for average migration during the forecast period, it is important that the forecaster indicates which type of interval is presented. A comparison of forecast intervals for net migration obtained from an ARIMA model to intervals in official Dutch national population forecasts shows that the uncertainty on migration has been underestimated in past official forecasts."     

The importance of the forecast

Careful forecasts, as accurate as possible, are central to the successful implementation of policy. There are fundamental reasons why policy makers cannot ‘play by ear’, adjusting policy quickly to each unexpected deviation in economic outcomes. Specific incidents are described where economic policy went awry because of faulty forecasts. The policy process is described in detail to show precisely where the forecast enters. Forecasting as a validation tool for establishing credibility in policy formation is analysed and discussed. Some estimated measure of forecast accuracy is presented, together with commentary on the necessary degrees of precision for successful implementation of policy.     

Linear combination of forecasts with an intercept: A bayesian approach

The standard approach to combining n expert forecasts involves taking a weighted average. Granger and Ramanathan proposed introducing an intercept term and unnormalized weights. This paper deduces their proposal from Bayesian principles. We find that their formula is equivalent to taking a weighted average of the n expert forecasts plus the decision-maker's prior forecast.     

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.     

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.     

Revisions in analysts' earnings forecasts: evidence of non‐linear adaptive expectations

Prior studies use a linear adaptive expectations model to describe how analysts revise their forecasts of future earnings in response to current forecast errors. However, research shows that extreme forecast errors are less likely than small forecast errors to persist in future years. If analysts recognize this property, their marginal forecast revisions should decrease with the forecast error's magnitude. Therefore, a linear model is likely to be unsatisfactory at describing analysts' forecast revisions. We find that a non‐linear model better describes the relation between analysts' forecast revisions and their forecast errors, and provides a richer theoretical framework for explaining analysts' forecasting behaviour. Our results are consistent with analysts' recognizing the permanent and temporary nature of forecast errors of differing magnitudes. Copyright © 2000 John Wiley & Sons, Ltd.     

基于短时临近降水集合预报的中小河流洪水预报研究

为了延长中小河流洪水预报预见期,建立了基于短时临近精细化网格降水集合预报的中小河流洪水预报模型。模型采用百分位映射订正技术,发展数值模式降水预报场与实况场映射关系,结合Bayesian模型,构建基于GRAPES-3KM模式和Time-Lag-Ensemble融合技术的短时临近降水集合预报(最优集成、最大(95%分位数)、最小(5%分位数))格点场,作为GMKHM(Grid-and-Mixed-runoff-generation-and-Kinematic-wave-based Hydrological Model)的降水驱动,进行中小河流洪水逐小时实时滚动预报。选择新安江屯溪流域作为试验流域,对2020年汛期流域大洪水进行实时预报。检验结果表明,基于短时临近最优降水预报的中小河流洪水预报模型提前了7 h预报出屯溪断面洪峰,洪峰误差为5.6%,峰现时差为-1 h,比不考虑预见期降水的中小河流洪水预报提前了4 h;基于短时临近最大、最小降水预报的中小河流洪水预报模型提前了13 h预报出洪峰区间,并且自7月7日9时起滚动预报最大与最小预报跨度呈逐渐减少趋势。在中小河流洪水预报中引入短时临近集...     

Can forecasting performance be improved by considering the steady state? An application to Swedish inflation and interest rate

This paper investigates whether the forecasting performance of Bayesian autoregressive and vector autoregressive models can be improved by incorporating prior beliefs on the steady state of the time series in the system. Traditional methodology is compared to the new framework—in which a mean‐adjusted form of the models is employed—by estimating the models on Swedish inflation and interest rate data from 1980 to 2004. Results show that the out‐of‐sample forecasting ability of the models is practically unchanged for inflation but significantly improved for the interest rate when informative prior distributions on the steady state are provided. The findings in this paper imply that this new methodology could be useful since it allows us to sharpen our forecasts in the presence of potential pitfalls such as near unit root processes and structural breaks, in particular when relying on small samples. Copyright © 2008 John Wiley & Sons, Ltd.