广义加权比例平均组合预测技术研究

本文提出了一类广义加权比例平均组合预测技术。新的组合预测技术比传统的组合预测技术更具有优越性,能取得更好的组合预测效果。     

模型选择方法在传感器建模中的应用

建立差分方程的好坏，将直接影响到测量系统的动态参数估计，对分析和改善系统的动态特性产生影响。该文讨论了能同时确定差分方程模型阶数和参数的方法－最优模型选择方法，并把该方法应用到传感器的建模中。该方法计算量少，与传统方法建立的数学模型相比较，更为简单、实用。     

Organizational Pressures on Forecast Evaluation: Managerial,Political, and Procedural Influences

This paper proposes a theory to explain why some forecasting organizations institutionalize forecast accuracy evaluation while others do not. The theory considers internal and external aspects of managerial, political, and procedural factors as they affect forecasting organizations. The theory is then tested using data from a survey of the US Federal Forecasters Group. Though some support for the theory is developed, multiple alternative explanations for results and the ‘public’ nature of the sample organizations prevent wide-scale generalization. The results suggest that larger organizations are more likely to have some form of forecast evaluation than smaller units. The institutionalization of forecast accuracy evaluation is closely linked to internal managerial and procedural factors, while external political pressure tends to reduce the likelihood of institutionalization of evaluation of forecast accuracy.© 1997 John Wiley & Sons, Ltd.     

Combining expert‐adjusted forecasts

It is well known that a combination of model‐based forecasts can improve upon each of the individual constituent forecasts. Most forecasts available in practice are, however, not purely based on econometric models but entail adjustments, where experts with domain‐specific knowledge modify the original model forecasts. There is much evidence that expert‐adjusted forecasts do not necessarily improve the pure model‐based forecasts. In this paper we show, however, that combined expert‐adjusted model forecasts can improve on combined model forecasts, in the case when the individual expert‐adjusted forecasts are not better than their associated model‐based forecasts. We discuss various implications of this finding.     

Regional,individual and political determinants of FOMC members' key macroeconomic forecasts

We study Federal Open Market Committee members' individual forecasts of inflation and unemployment in the period 1992–2004. Our results imply that Governors and Bank presidents forecast differently, with Governors submitting lower inflation and higher unemployment rate forecasts than bank presidents. For Bank presidents we find a regional bias, with higher district unemployment rates being associated with lower inflation and higher unemployment rate forecasts. Bank presidents' regional bias is more pronounced during the year prior to their elections or for nonvoting bank presidents. Career backgrounds or political affiliations also affect individual forecast behavior.     

PARX model for football match predictions

We propose an innovative approach to model and predict the outcome of football matches based on the Poisson autoregression with exogenous covariates (PARX) model recently proposed by Agosto, Cavaliere, Kristensen, and Rahbek (Journal of Empirical Finance, 2016, 38(B), 640–663). We show that this methodology is particularly suited to model the goal distribution of a football team and provides a good forecast performance that can be exploited to develop a profitable betting strategy. This paper improves the strand of literature on Poisson‐based models, by proposing a specification able to capture the main characteristics of goal distribution. The betting strategy is based on the idea that the odds proposed by the market do not reflect the true probability of the match because they may also incorporate the betting volumes or strategic price settings in order to exploit betters' biases. The out‐of‐sample performance of the PARX model is better than the reference approach by Dixon and Coles (Applied Statistics, 1997, 46(2), 265–280). We also evaluate our approach in a simple betting strategy, which is applied to English football Premier League data for the 2013–2014, 2014–2015, and 2015–2016 seasons. The results show that the return from the betting strategy is larger than 30% in most of the cases considered and may even exceed 100% if we consider an alternative strategy based on a predetermined threshold, which makes it possible to exploit the inefficiency of the betting market.     

Forecast Combinations in a DSGE‐VAR Lab

We explore the benefits of forecast combinations based on forecast‐encompassing tests compared to simple averages and to Bates–Granger combinations. We also consider a new combination algorithm that fuses test‐based and Bates–Granger weighting. For a realistic simulation design, we generate multivariate time series samples from a macroeconomic DSGE‐VAR (dynamic stochastic general equilibrium–vector autoregressive) model. Results generally support Bates–Granger over uniform weighting, whereas benefits of test‐based weights depend on the sample size and on the prediction horizon. In a corresponding application to real‐world data, simple averaging performs best. Uniform averages may be the weighting scheme that is most robust to empirically observed irregularities. Copyright © 2016 John Wiley & Sons, Ltd.     

On the development of the GRAPES――A new generation of the national operational NWP system in China

Numerical weather prediction (NWP) has become one of the most important means for weather fore-casts in the world. It also mirrors a nation's comprehensive strength in meteorology. In 2000, China Meteorological Administration (CMA) established the National Innovative Base for Meteorological Numerical Prediction in the Chinese Academy of Meteorological Sciences (CAMS), to work on developing a new generation of the national operational NWP system――Global/Regional Assimilation and PrEdiction System (GRAPES), to enhance meteorological services in China in the new century. In recent years, the GRAPES has witnessed a fast development. The GRAPES has been set up as an integration of the model framework, data assimilation, regional and global NWP system, which can be commonly used for both operation and research. In this paper, a brief review is made for illustrating the GRAPES system, including the advanced designs of the GRAPES, its diverse applications in multifields, and efficiencies of the regional and global GRAPES in operational applications based on hindcast results.     

Forecasting the recent behavior of US business fixed investment spending: an analysis of competing models

We evaluate forecasting models of US business fixed investment spending growth over the recent 1995:1–2004:2 out‐of‐sample period. The forecasting models are based on the conventional Accelerator, Neoclassical, Average Q, and Cash‐Flow models of investment spending, as well as real stock prices and excess stock return predictors. The real stock price model typically generates the most accurate forecasts, and forecast‐encompassing tests indicate that this model contains most of the information useful for forecasting investment spending growth relative to the other models at longer horizons. In a robustness check, we also evaluate the forecasting performance of the models over two alternative out‐of‐sample periods: 1975:1–1984:4 and 1985:1–1994:4. A number of different models produce the most accurate forecasts over these alternative out‐of‐sample periods, indicating that while the real stock price model appears particularly useful for forecasting the recent behavior of investment spending growth, it may not continue to perform well in future periods. Copyright © 2007 John Wiley & Sons, Ltd.     

The uses and abuses of ‘consensus’ forecasts

Clemen's (1989) review of the forecast-combining literature amply illustrates both the interest in and the importance of this subject. This article stresses the tautological properties of various consensus measures that assure their success relative to most individual forecasts. It confirms the finding of earlier studies that for each specific macroeconomic variable roughly one-third of individual forecasters are more accurate than a consensus. However, each individual does relatively poorly for some variable while the consensus, in contrast, necessarily never fails relative to most individuals. These results, like most previous studies, describe consensus measures that are synthetic constructs derived from a pre-existing set of individual forecasts. Strictly speaking, this contemporaneous consensus is not available to individual forecasters when their forecasts are made. A prior consensus measure, which is in their information sets, was relatively much less accurate than the contemporaneous measure. Nevertheless, a small subset of individual forecasters were generally inferior to the known, prior consensus forecast.