Efficient Multistep Forecast Procedures for Multivariate Time Series

Upon the evidence that infinite‐order vector autoregression setting is more realistic in time series models, we propose new model selection procedures for producing efficient multistep forecasts. They consist of order selection criteria involving the sample analog of the asymptotic approximation of the h‐step‐ahead forecast mean squared error matrix, where h is the forecast horizon. These criteria are minimized over a truncation order n_T under the assumption that an infinite‐order vector autoregression can be approximated, under suitable conditions, with a sequence of truncated models, where n_T is increasing with sample size. Using finite‐order vector autoregressive models with various persistent levels and realistic sample sizes, Monte Carlo simulations show that, overall, our criteria outperform conventional competitors. Specifically, they tend to yield better small‐sample distribution of the lag‐order estimates around the true value, while estimating it with relatively satisfactory probabilities. They also produce more efficient multistep (and even stepwise) forecasts since they yield the lowest h‐step‐ahead forecast mean squared errors for the individual components of the holding pseudo‐data to forecast. Thus estimating the actual autoregressive order as well as the best forecasting model can be achieved with the same selection procedure. Such results stand in sharp contrast to the belief that parsimony is a virtue in itself, and state that the relative accuracy of strongly consistent criteria such as the Schwarz information criterion, as claimed in the literature, is overstated. Our criteria are new tools extending those previously existing in the literature and hence can suitably be used for various practical situations when necessary. Copyright © 2015 John Wiley & Sons, Ltd.     

Does Disagreement Amongst Forecasters Have Predictive Value?

In the present study we examine the predictive power of disagreement amongst forecasters. In our empirical work, we find that in some situations this variable can signal upcoming structural and temporal changes in an economic process and in the predictive power of the survey forecasts. We examine a variety of macroeconomic variables, and we use different measurements for the degree of disagreement, together with measures for location of the survey data and autoregressive components. Forecasts from simple linear models and forecasts from Markov regime‐switching models with constant and with time‐varying transition probabilities are constructed in real time and compared on forecast accuracy. Copyright © 2015 John Wiley & Sons, Ltd.     

我国居民储蓄存款变化成因分析

本文在影响居民储蓄存款变化的因素中,剔除文化背景、人文环境、政治和经济制度、消费和储蓄习惯、对今后收入和支出变化的判断等不易量化的影响因素后,就居民可支配收入、消费支出、利率、除储蓄以外的其他金融资产投资、房地产市场等因素,对居民储蓄存款变化成因的影响,采用线性回归分析方法,进行定量回归分析和判断,并引出对我国货币政策以及宏观调控手段取向的思考．     

N-step combinations of forecasts

While there is general agreement that a linear combination of forecasts can outperform the individual forecasts, there is controversy about the appropriateness of the combination method to be used in a given situation. Hence, in any given application it may be more beneficial to combine different sets of combined forecasts rather than picking one of them. This paper introduces the concept of N-step combinations of forecasts which involves combining the combined forecasts obtained from different combination procedures used at the preceding step. Using quarterly GNP data, evidence supporting the increase in the accuracy of the one-period-ahead ex-ante forecasts as the combination step increases is provided. The MSE, MAE, MAPE and their corresponding standard deviations are used to evaluate the accuracy of the forecasts obtained.     

Relative accuracy of judgemental and extrapolative methods in forecasting annual earnings

This paper identifies and analyses previously published studies on annual earnings forecasts. Comparisons of forecasts produced by management, analysts, and extrapolative techniques indicated that: (1) management forecasts were superior to professional analyst forecasts (the mean absolute percentage errors were 15.9 and 17.7, respectively, based on five studies using data from 1967–1974) and (2) judgemental forecasts (both management and analysts) were superior to extrapolation forecasts on 14 of 17 comparisons from 13 studies using data from 1964–1979 (the mean absolute percentage errors were 21.0 and 28.4 for judgement and extrapolation, respectively). These conclusions, based on recent research, differ from those reported in previous reviews, which commented on less than half of the studies identified here.     

市场需求Logit组合预测的研究

首先阐述了网络市场营销和传统市场营销的区别，分析了在该环境下市场需求的变化和相应的特点。其次，研究了一种利于计算机建模的市场需求组合预测方法，即采用Logit回归法训练数据的市场需求组合预测模型。最后，将该方法用于家用电冰箱市场需求趋势预测的示例研究，采用信息熵的方法，检验了组合预测模型的预测效果。实例表明，该方法可以通过调整组合权重提高预测精度。     

Optimal conditional ARIMA forecasts

An optimal univariate forecast, based on historical and additional information about the future, is obtained in this paper. Its statistical properties, as well as some inferential procedures derived from it, are indicated. Two main situations are considered explicitly: (1) when the additional information imposes a constraint to be fulfilled exactly by the forecasts and (2) when the information is only a conjecture about the future values of the series or a forecast from an alternative model. Theoretical and empirical illustrations are provided, and a unification of the existing methods is also attempted.     

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.