Forecasting contemporaneous aggregates and the combination of forecasts: The case of the U.K. monetary aggregates

A number of papers in recent years have investigated the problems of forecasting contemporaneously aggregated time series and of combining alternative forecasts of a time series. This paper considers the integration of both approaches within the example of assessing the forecasting performance of models for two of the U.K. monetary aggregates, £M3 and MO. It is found that forecasts from a time series model for aggregate £M3 are superior to aggregated forecasts from individual models fitted to either the components or counterparts of £M3 and that an even better forecast is obtained by forming a linear combination of the three alternatives. For MO, however, aggregated forecasts from its components prove superior to either the forecast from the aggregate itself or from a linear combination of the two.     

Some practical aspects of forecasting in organizations

This paper presents the writer's experience, over a period of 25 years, in analysing organizational systems and, in particular, concentrates on the overall forecasting activity. The paper first looks at the relationship between forecasting and decision taking–with emphasis on the fact that forecasting is a means to aid decision taking and not an end in itself. It states that there are many types of forecasting problems, each requiring different methods of treatment. The paper then discusses attitudes which are emerging about the relative advantages of different forecasting techniques. It suggests a model building process which requires‘experience’and‘craftsmanship’, extensive practical application, frequent interaction between theory and practice and a methodology that eventually leads to models that contain no detectable inadequacies. Furthermore, it argues that although models which forecast a time series from its past history have a very important role to play, for effective policy making it is necessary to augment the model by introducing policy variables, again in a systematic not an ‘ad hoc’ manner. Finally, the paper discusses how forecasting systems can be introduced into the management process in the first place and how they should be monitored and updated when found wanting.     

Estimating trend growth rates of the U.K. monetary aggregates

This paper constructs current trend growth rates for a variety of U.K. monetary aggregates. These rates are computed from decompositions of intervention-augmented ARIMA models, the interventions being identified and their magnitude estimated by an iterative detection procedure.     

Forecasting with latent structure time series models: an application to nominal interest rates

In this paper we develop a latent structure extension of a commonly used structural time series model and use the model as a basis for forecasting. Each unobserved regime has its own unique slope and variances to describe the process generating the data, and at any given time period the model predicts a priori which regime best characterizes the data. This is accomplished by using a multinomial logit model in which the primary explanatory variable is a measure of how consistent each regime has been with recent observations. The model is especially well suited to forecasting series which are subject to frequent and/or major shocks. An application to nominal interest rates shows that the behaviour of the three‐month US Treasury bill rate is adequately explained by three regimes. The forecasting accuracy is superior to that produced by a traditional single‐regime model and a standard ARIMA model with a conditionally heteroscedastic error. Copyright © 1999 John Wiley & Sons, Ltd.     

Sources of error in forecasts and expectations: U.K. economic models, 1984–8

This paper presents a comparative analysis of the sources of error in forecasts for the UK economy published over a recent four-year period by four independent groups. This analysis rests on the archiving at the ESRC Macroeconomic Modelling Bureau of the original forecasts together with all their accompanying assumptions and adjustments. A method of decomposing observed forecast errors so as to distinguish the contributions of forecaster and model is set out; the impact of future expectations treated in a ‘model-consistent’ or ‘rational’ manner is specifically considered. The results show that the forecaster's adjustments make a substantial contribution to forecast performance, a good part of which comes from adjustments that bring the model on track at the start of the forecast period. The published ex-ante forecasts are usually superior to pure model-based ex-post forecasts, whose performance indicates some misspecification of the underlying models.     

Forecasts of power-transformed series

Consider a time series transformed by an instantaneous power function of the Box-Cox type. For a wide range of fractional powers, this paper gives the relative bias in original metric forecasts due to use of the simple inverse retransformation when minimum mean squared error (conditional mean) forecasts are optimal. This bias varies widely according to the characteristics of the data. A fast algorithm is given to find this bias, or to find minimum mean squared error forecasts in the original metric. The results depend on the assumption that the forecast errors in the transformed metric are Gaussian. An example using real data is given.     

The combination of macroeconomic forecasts

The paper examines combined forecasts based on two components: forecasts produced by Chase Econometrics and those produced using the Box-Jenkins ARIMA technique. Six series of quarterly ex ante and simulated ex ante forecasts are used over 37 time periods and ten horizons. The forecasts are combined using seven different methods. The best combined forecasts, judged by average relative root-mean-square error, are superior to the Chase forecasts for three variables and inferior for two, though averaged over all six variables the Chase forecasts are slightly better. A two-step procedure produces forecasts for the last half of the sample which, on average, are slightly better than the Chase forecasts.     

The role of time series analysis in the evaluation of econometric models

This paper compares the properties of a structural model—the London Business School model of the U.K. economy—with a time series model. Information provided by this type of comparison is a useful diagnostic tool for detecting types of model misspecification. This is a more meaningful way of proceeding rather than attempting to establish the superiority of one type of model over another. In lieu of a better structural model, the effects of inappropriate dynamic specification can be reduced by combining the forecasts of both the structural and time series models. For many variables considered here these provide more accurate forecasts than each of the model types alone.     

美国登月火箭项目进展

研究美国登月火箭的项目管理有助于我国探月工程的规划。2004年美国公布重返月球计划。为实现这一目标,美国国家航空航天局决定以航天飞机推进系统为基础研制新型运载火箭。新火箭于2006年被命名为“战神”,包括“战神”-1乘员运载火箭和“战神”-5货物运载火箭两种型号。目前,“战神”-1火箭已完成系统需求评审,进入设计评审阶段。火箭第一级、上面级、上面级发动机以及电子设备的研制合同都已于2007年授出。预计2009年5月进行首次试验飞行,2015年3月进行首次载人飞行。而“战神”-5火箭还处于早期研究阶段。由于预算调整和航天项目的复杂性,“战神”火箭的研制已经有所拖延。虽然大型航天集团和国家航空航天局努力争取更多预算,但“战神”火箭未来的研制不排除进一步拖延的可能。     

从近年科技投入变化看美国科技政策的变化

美国能够成为世界头号科技大国,其科技政策功不可没。近年来,美国不断调整其科技政策以保证站在世界的最前列,这可以从美国在科技中投入的不断变化看出来。本文基于冷战结束至今美国在科技中投入的变化,分析了美国科技政策十几年来的变化特点。     

美国气候变化技术计划(CCTP)新战略规划及其对我国的启示

美国气候变化技术计划(CCTP)于2006年9月公布了新的气候变化技术计划战略规划,新规划将通过捕集、减少以及储存的方式来控制温室气体的排放量。该计划中包含的技术有氢能源、生物提炼、清洁煤、碳储存、核分裂和聚变能等,这些技术以最基础的方式转化为国民经济价值,不仅能够改善气候变化,而且可以保证能源安全、空气污染以及其他紧迫需求。本文对美国气候变化技术计划战略规划的任务、目标、方法进行概要介绍,并论述了其对我国气候变化技术发展的重要启示。     

How accurate are the U.S. Bureau of the Census projections of total live births

"The accuracy of total live birth forecasts issued by the U.S. Bureau of the Census was analysed. Forecast accuracy has not improved significantly since 1950. Further, the forecasts are not more accurate than several naive alternatives. Moving from a period methodology to a cohort methodology improved forecast accuracy for certain forecasts. [It is demonstrated that] the Bureau of the Census systematically underestimated total births in the upswing and overestimated in the downswing."     

Experiential and cosmopolitan knowledge: The transcontinental field practices of the U.S. Bureau of Biological Survey

Before many of the global environmental knowledge producing networks and technologies emerged later in the twentieth century, another spatially extended form of field science was implemented at a continental scale by the U.S. Bureau of Biological Survey, revealing similar tensions and dynamics. Specimens and observations from across continental spaces were integrated through railroad-based transportation and communications networks in order to map distributions of birds and mammals and delineate “life zones” stretching across the continent. At the same time that field zoologists of the Biological Survey produced this cosmopolitan scientific knowledge, they also developed an intimate, experiential knowledge of many of the places where they traveled. By following the travels of Biological Survey field parties, especially the agency's long-time chief field naturalist Vernon Bailey, during the late nineteenth and early twentieth centuries when the railroad was dominant, this paper traces the interconnections between the two ways of knowing in the Biological Survey's practice. However, the integration of these different forms of knowledge was ultimately partial and incomplete, as seen through the Survey's daily practices such as food consumption, the seasonality of survey field practice, and limitations on what types of knowledge were incorporated from lay network collaborators and field assistants.     

Estimation and prediction under structural instability: The case of the U.S. pulp and paper market

The objectives of this paper are: first, to show empirically the relevance of using adaptive estimation techniques over more traditional estimation approaches when economic systems are believed to be structurally unstable over time; and secondly, to compare in an empirical framework two adaptive estimation techniques: Kalman filtering and the Carbone–Longini filter. For that purpose, an econometric model for the U.S. pulp and paper market is examined under the assumption of structural instability and, hence, constitutes the basis for comparing forecasting performances and estimation accuracy achieved by each technique. A version of Kalman filtering, modified in line with the basic idea of ‘tracking’ characterizing the Carbone–Longini filter, is also presented and applied. The analysis of the results shows that it may be worth using adapative estimation methods to estimate structurally unstable models, even if there is no prior knowledge about the patterns of variation of the parameters. Also, it shows the Carbone–Longini filter and Kalman filtering as being complementary estimation techniques. An estimation/forecasting methodology involving a sequential application mode of these two techniques is suggested.     

Forecasting the economic recession in the U.K. 1979-1982: A comparison of model-based ex ante Forecasts

This paper compares the forecasts of recession and recovery made by five non-government U.K. teams modelling the economy (Cambridge Econometrics, the London Business School, the National Institute of Economic and Social Research, the Cambridge Economic Policy Group and the Liverpool Research Group). The paper concentrates on annual ex ante projections as published over the period 1978-1982, i.e. forecasts made, before the event, of the onset, length, depth and character of the economic recession in the U.K. which began in 1979. The comparison is in terms of year by year changes in production, unemployment, prices and other variables. It concludes that no group was systematically better or worse than other groups (confirming U.S. experience) and that the groups tended to perform better in their chosen areas of specialization, e.g. medium-term groups did better at forecasting the medium-term outcome.