摘要:

AbstractBusiness cycle forecasting involves several different methodological problems. Some of these are discussed in the current issue of this journal and are introduced in this paper. The forecasting approach itself often focuses on turning points in the business cycle and a number of papers in this issue examine this particular aspect of business cycle forecasting. For example, a Bayesian technique for detecting changing random slopes in leading composite indexes is discussed. Business survey data are often used in the process of forecasting business cycles. A Kalman filtering procedure is suggested to make business survey information useful in predicting changes in industrial production. Other data issues of relevance to this topic that are discussed include the preliminary data and revision problem. Methodology for using high‐frequency data and for converting high‐frequency to low‐frequency data is also presented. A number of the papers discuss the analysis of dynamic structures, such as the existence of time‐varying dynamics, and the use of vector‐autoregressive (VAR) models. Finally, a few comments are made on general structural variability aspects, related to business cycle fo

ISSN:0277-6693    

DOI:10.1002/for.3980120302

出版商:John Wiley&Sons, Ltd.    

年代:1993

数据来源: WILEY

摘要:

AbstractA Bayesian statistical method to detect turning points in the leading composite index is introduced. Under the assumption of causal priority of the leading composite index to the business cycle, the turning points in business cycles are predicted by detection of them in the index. The underlying process of the leading composite index is described by a dynamic linear model with random level and slope, where the random slope is distorted by a random shock at each turning point. The turning point is detected by obtaining a large value of the posterior probability that one of the previous slope components has undergone a major change. The intensity of the change causing a turn in the business cycle is quantified by estimating the size of the random shock. The application of the results to the US leading composite index are compared with results of earlier studies.

ISSN:0277-6693    

DOI:10.1002/for.3980120303

出版商:John Wiley&Sons, Ltd.    

年代:1993

数据来源: WILEY

摘要:

AbstractIn this paper the econometrics of latent variables in conjunction with leading economic indicators are used to predict turning points of the US industrial production variable for various forecasting horizons. The results reported here show that leading indicators used in regression models with a dichotomous dependent variable that marks periods of economic expansion and contraction forecast business cycle turning points accurately. Leading indicators produce the most reliable forecasts of turning points five months prior to cyclical changes and do not give any false signals. Moreover, the weights assigned to leading indicators for producing the index vary as the forecast horizon changes.

ISSN:0277-6693    

DOI:10.1002/for.3980120304

出版商:John Wiley&Sons, Ltd.    

年代:1993

数据来源: WILEY

摘要:

AbstractTo forecast the turnaround of an economy we do not usually take seasonal effects into account. Recently, the author showed that business cycle turning points as well as durations do not appear to be uniformly distributed throughout the year (see Ghysels, 1991a). In this paper we suggest improving the forecasting performance of turning‐point predictions by adopting periodic hazard models. Following Diebold and Rudebusch (1989), we construct several formal probability models and score their prediction performance. The results indicate that for sequential forecasting rules significant gains can be made by exploiting periodicities in turning‐point probabilit

ISSN:0277-6693    

DOI:10.1002/for.3980120305

出版商:John Wiley&Sons, Ltd.    

年代:1993

数据来源: WILEY

摘要:

AbstractQualitative survey data on changes in production, inventory, new order, and employment collected every month by the National Association of Purchasing Managers are analysed over 1948–90. The Probability method we use generates time‐series estimates of cross‐section variabilities across firms. It is shown that thesediffusionmeasures have additional explanatory power in the prediction of business cycle turning p

ISSN:0277-6693    

DOI:10.1002/for.3980120306

出版商:John Wiley&Sons, Ltd.    

年代:1993

数据来源: WILEY

摘要:

AbstractIn this paper forecasting the production volume of the Swedish and Finnish metal and engineering industries one quarter ahead is discussed. A practical way of making use of the predictive information in the answers of the quarterly business survey is presented which is based on the application of the Kalman filter. It is found that the most informative questions of the business survey from the point of view of forecasting are different in the two countries. However, for both Sweden and Finland, the improvement in prediction accuracy after taking account of relevant business survey information is significant when the precision of autoprojective forecasts is used as a baseline.

ISSN:0277-6693    

DOI:10.1002/for.3980120307

出版商:John Wiley&Sons, Ltd.    

年代:1993

数据来源: WILEY

摘要:

AbstractSurveys of the production expectations in the manufacturing industry are collected monthly or quarterly in many OECD countries. The producers are asked about expected production over the next three or four months. This paper tests the predictive performance of production expectations in the manufacturing industry of eight OECD countries. Evidence indicates that: (1) survey expectations of production have a predictive value with respect to production; (2) in most cases the forecast horizon is more significant at 3 or 4 months compared to 2 or 5 months; (3) inflow of orders, order books, stocks of finished goods, etc. contain information to predict production for most countries which are not embodied in survey production expectations.

ISSN:0277-6693    

DOI:10.1002/for.3980120308

出版商:John Wiley&Sons, Ltd.    

年代:1993

数据来源: WILEY

摘要:

AbstractIn this paper, using composite predictors we examine whether the use of survey information on consumer confidence would have helped to predict fluctuations in economic activity. We also consider the implications of the new literature on time‐series modelling when the underlying processes are not stationary. We then examine what implications this has for the construction of composite predictors. We find that it is essential that any forecast—used as part of a composite predictor—is co‐integrated with the outcome. It is likely that this will hold in practice, but if it does not then the forecast errors will be non‐stationary and the interpretation of the composite predictor

ISSN:0277-6693    

DOI:10.1002/for.3980120309

出版商:John Wiley&Sons, Ltd.    

年代:1993

数据来源: WILEY

摘要:

AbstractForecasting on the basis of the daily flow of monthly or more frequent statistical reports on the economy can enhance the predictive accuracy of quarterly structural models. The high degree of serial correlation in economic data can be used advantageously in quarterly forecasting for a horizon as long as 6 months—perhaps somewhat longer. The model used for high‐frequency (weekly) forecasting of the US economy has a national accounting structure and tries to follow the choice of indicators that are used in preparing early estimates of national income and product accounts (NIPA). Estimates are separately generated for the income side and the product side of NIPA. At the level of GDP and closely related aggregates a third prediction is also generated from estimates of the principal components of major monthly indicators. A simple average of three estimates of GDP, together with detail on NIPA components and scores of monthly indicators has been produced every weekend, summarizing the business week's flow of information. This procedure is followed not only for producing a steady stream of high‐frequency forecasts but also for providing adjustment factors that can be used for model recalibration, without judgemental input. The tracking of the US economy is illustrated for the period starting before the invasion of Kuwait until the end of the Gul

ISSN:0277-6693    

DOI:10.1002/for.3980120310

出版商:John Wiley&Sons, Ltd.    

年代:1993

数据来源: WILEY

摘要:

AbstractThere are occasions when researchers are interested in quarterly forecasts of variables that are released at higher frequencies. In these situations it is common for researchers to convert from the higher frequency to the lower frequency by some method, such as averaging or stock‐end, and then to model the low‐frequency data. This paper shows how to improve quarterly forecasts by using within‐quarter variations of monthly data. We compare the one‐step‐ahead and multi‐step‐ahead forecasts for real GNP generated using our approach with those of Fair and Shiller (1990). Our model is extremely simple and, yet, or perhaps because of, produces a lower RMSE than any model in Fair and S

ISSN:0277-6693    

DOI:10.1002/for.3980120311

出版商:John Wiley&Sons, Ltd.    

年代:1993

数据来源: WILEY