International interdependence of business cycles in the manufacturing industry: The use of leading indicators for forecasting and analysis

This article introduces new leading indicators for fifteen industrialized countries which enable the business cycle in manufacturing to be forecast fairly reliably between 4 and 6 months ahead. These indicators are based on an improved variant of the NBER method, yielding a composite leading indicator characterized by less erratic movements and clear turning points. The indicators are used to explore the international interdependence of business cycles and to examine the degree to which this interdependence is affected by growing economic integration, as in the EC. For each of the countries studied, the various foreign economies affecting the local business climate are identified. Since the business cycles of some countries clearly lead those of others, this international interdependence can be used to further improve the predictive power of the leading indicators in the lagging countries.     

The leading indicator approach to economic forecasting—retrospect and prospect

Our purpose in this paper is to explain briefly the theory and rationale underlying the leading, coincident and lagging indicators, describe the more important statistical procedures used, and review the evidence on how the indicators have performed in practice. The tests of performance concentrate on data not used in the selection of the indicators, in the United States and nine other countries. We conclude with some suggestions for future research and development, including the application of the approach to the analysis of inflation.     

An intelligent business forecaster for strategic business planning

In this paper an intelligent business forecaster for strategic business planning is presented. The forecaster is basically a multi‐layered fuzzy rule‐based neural network which integrates the basic elements and functions of a traditional fuzzy logic inference into a neural network structure. It has also been shown to be superior to two commercially available business forecasters in terms of learning speed and forecasting accuracy. This paper presents the architectural design of the intelligent business forecaster and the results of a study that has been carried out to compare its performance with that of the others. Copyright © 1999 John Wiley & Sons, Ltd.     

Evaluating early warning and coincident indicators of business cycles using smooth trends

We present a composite coincident indicator designed to capture the state of the Spanish economy. Our approach, based on smooth trends, guarantees that the resulting indicators are reasonably smooth and issue stable signals, reducing the uncertainty. The coincident indicator has been checked by comparing it with the one recently proposed by the Spanish Economic Association index. Both indexes show similar behavior and ours captures very well the beginning and end of the official recessions and expansion periods. Our coincident indicator also tracks very well alternative mass media indicators typically used in the political science literature. We also update our composite leading indicator (Bujosa et al., Journal of Forecasting, 2013, 32(6), 481–499). It systematically predicts the peaks and troughs of the new Spanish Economic Association index and provides significant aid in forecasting annual gross domestic product growth rates. Using only real data available at the beginning of each forecast period, our indicator one-step-ahead forecast shows improvements over other individual alternatives and different forecast combinations.     

A multi-country dynamic factor model with stochastic volatility for euro area business cycle analysis

This paper develops a dynamic factor model that uses euro area country-specific information on output and inflation to estimate an area-wide measure of the output gap. Our model assumes that output and inflation can be decomposed into country-specific stochastic trends and a common cyclical component. Comovement in the trends is introduced by imposing a factor structure on the shocks to the latent states. We moreover introduce flexible stochastic volatility specifications to control for heteroscedasticity in the measurement errors and innovations to the latent states. Carefully specified shrinkage priors allow for pushing the model towards a homoscedastic specification, if supported by the data. Our measure of the output gap closely tracks other commonly adopted measures, with small differences in magnitudes and timing. To assess whether the model-based output gap helps in forecasting inflation, we perform an out-of-sample forecasting exercise. The findings indicate that our approach yields superior inflation forecasts, both in terms of point and density predictions.     

The predictability of stock market volatility in emerging economies: Relative roles of local,regional, and global business cycles

This paper explores the role of business cycle proxies, measured by the output gap at the global, regional, and local levels, as potential predictors of stock market volatility in the emerging BRICS nations. We observe that the emerging BRICS nations display a rather heterogeneous pattern when it comes to the relative role of idiosyncratic factors as a predictor of stock market volatility. While domestic output gap is found to capture significant predictive information for India and China particularly, the business cycles associated with emerging economies and the world in general are strongly important for the BRIC countries and weakly for South Africa, especially in the postglobal financial crisis era. The findings suggest that despite the increase in the financial integration of world capital markets, emerging economies can still bear significant exposures to idiosyncratic risk factors, an issue of high importance for the profitability of global diversification strategies.     

Empirical analysis of systematic errors in Chilean GDP forecasts

This paper presents a statistical comparison between the actual and predicted evolution of the Chilean GDP for the period 1986 – 1998 made by several forecasters. We show that the forecasters systematically underestimate the true growth rate of the economy. The magnitude of this bias tends to be correlated with the phase of the business cycle. Copyright © 2001 John Wiley & Sons, Ltd.     

Expert judgments of political riskiness

Professional analysts' judgments of the political riskiness of 49 focal countries for the period 1983-1985 were studied. Data were collected on nine predictor variables; each was significantly correlated at the 0.01 level with ratings of political riskiness. The highest correlation was with infant mortality and life expectancy; either accounted for roughly 50% of the variance in ratings. Different variables were better predictors of political risk within different geographic regions. A factor analysis suggested the presence of three underlying factors. The predictor variable with the highest loading was chosen to represent each of the three factors. These were: exchange rate differential; estimated inflation rate; and infant mortality rate. Approximately 75% of the variance in ratings could be accounted for on the basis of a linear combination of the three predictor variables. These three variables were capable of good prediction even for various subsets of countries based on geographic region or other criteria. Using all nine variables as predictors resulted in only marginal improvement. A cluster analysis revealed little difference among clusters of judges. Ratings by undergraduate students closely paralled those of professional analysts. As in previous studies of expert predictions and forecasts, claims of expertise in political risk analysis were better supported by command of factual knowledge than by differentially superior predictive ability.     

The Euro‐Sting Revisited: The Usefulness of Financial Indicators to Obtain Euro Area GDP Forecasts

This paper uses an extension of the Euro‐Sting single‐index dynamic factor model to construct short‐term forecasts of quarterly GDP growth for the euro area by accounting for financial variables as leading indicators. From a simulated real‐time exercise, the model is used to investigate the forecasting accuracy across the different phases of the business cycle. Our extension is also used to evaluate the relative forecasting ability of the two most reliable business cycle surveys for the euro area: the PMI and the ESI. We show that the latter produces more accurate GDP forecasts than the former. Finally, the proposed model is also characterized by its great ability to capture the European business cycle, as well as the probabilities of expansion and/or contraction periods. Copyright © 2014 John Wiley & Sons, Ltd.     

Statistical surveillance of cyclical processes with application to turns in business cycles

On‐line monitoring of cyclical processes is studied. An important application is early prediction of the next turn in business cycles by an alarm for a turn in a leading index. Three likelihood‐based methods for detection of a turn are compared in detail. One of the methods is based on a hidden Markov model. The two others are based on the theory of statistical surveillance. One of these is free from parametric assumptions of the curve. Evaluations are made of the effect of different specifications of the curve and the transitions. The methods are made comparable by alarm limits, which give the same median time to the first false alarm, but also other approaches for comparability are discussed. Results are given on the expected delay time to a correct alarm, the probability of detection of a turning point within a specified time, and the predictive value of an alarm. Copyright © 2005 John Wiley & Sons, Ltd.     

Unemployment variation over the business cycles: a comparison of forecasting models

Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non‐linear and, therefore, the use of linear models to explain their behaviour and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non‐linearity in the unemployment series. Only recently have there been some developments in applying non‐linear models to estimate and forecast unemployment rates. A major concern of non‐linear modelling is the model specification problem; it is very hard to test all possible non‐linear specifications, and to select the most appropriate specification for a particular model. Artificial neural network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non‐linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back‐propagation model and a generalized regression neural network model to estimate and forecast post‐war aggregate unemployment rates in the USA, Canada, UK, France and Japan. We compare the out‐of‐sample forecast results obtained by the ANN models with those obtained by several linear and non‐linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases better than, the other univariate econometrics time series models in our test. Copyright © 2004 John Wiley & Sons, Ltd.     

Suppression of annual plasma testosterone and thyroxine cycles in the edible dormouseGlis glis under constant photoperiod at 24°C

Summary Exposure of male ebible dormice all year round to an unwarying photoperiod and warm temperature disrupted their biological cycles; hibernation was almost completely suppressed, and short lived infradian cycles of body weight, and of plasma testosterone and thyroxine were measured instead of the normal annual patten.     

From natural history to political economy: the enlightened mission of Domenico Vandelli in late eighteenth-century Portugal

This article presents the main features of the work of Domenico Vandelli (1735–1816), an Italian-born man of science who lived a large part of his life in Portugal. Vandelli’s scientific interests as a naturalist paved the way to his activities as a reformer and adviser on economic and financial issues. The topics covered in his writings are similar to those discussed by Linnaeus, with whom Vandelli corresponded. They clearly reveal that the scientific preparation indispensable for a better knowledge of natural resources was also a fundamental condition for correctly addressing problems of efficiency in their economic allocation. The key argument put forward in this article is that the relationship between natural history and the agenda for economic reform and development deserves to be further analysed. It is indeed a central element in the emergence of political economy as an autonomous scientific discourse during the last decades of the eighteenth century.     

Idealisation,genetic explanations and political behaviours: Notes on the anti-reductionist critique of genopolitics

The rapid development of genetic research, determined, among others, by the requirements of The Human Genome Project, and a gradual reorientation in the perception of the role of nature and culture in the process of shaping complex networks of human relations by some political scientists, result in the increasing application of genetic data and methods in research regarding political behaviours. One of the key philosophical objections against the studies of the genetic foundations of political behaviours is that of excessive reductionism. This is supposed to manifest itself in the inadequate selection of the level of analysis for the explained phenomenon, the incompleteness of explanations and their low utility. My findings show that this objection is not sufficiently supported by contemporary science. Both studies using classical behavioural genetic methodologies and studies using DNA-based methods show that genes most likely play a role in political behaviours. Emphasising the significance of genetic influences in the midst of multiple extra-genetic interactions generates highly idealised explanations. Using the conceptual apparatus of the deformational concept of culture, I have demonstrated that the omission of a number of important extra-genetic influences by researchers is a consequence of focusing on specific causal patterns. This omission, however, does not entail negating the influence of non-genetic factors and, importantly, it may not have to be permanent. Following this approach, if correct, the reductionism of research into the genetic foundations of political behaviours is a standard cognitive procedure applied in science.     

Seasonal prediction of European cereal prices: good forecasts using bad models?

Because of their natural adherence to the climate and pronounced seasonal cycles, prices of field crops constitute an interesting field for exploring seasonal time series models. We consider quarterly prices of two major cereals: barley and wheat. Using traditional in‐sample fit and moving‐window techniques, we investigate whether seasonality is deterministic or unit‐root stochastic and whether seasonal cycles have converged over time. We find that seasonal cycles in the data are mainly deterministic and that evidence on common cycles across countries differs for the two commodities. Out‐of‐sample prediction experiments, however, yield a ranking with respect to accuracy that does not match the statistical in‐sample evidence. Parametric bootstrap experiments establish that the observed mismatch is indeed an inherent and systematic feature. Copyright © 2008 John Wiley & Sons, Ltd.     

Forecasting for business planning: A case study of IBM product sales

This is a case study of a closely managed product. Its purpose is to determine whether time-series methods can be appropriate for business planning. By appropriate, we mean two things: whether these methods can model and estimate the special events or features that are often present in sales data; and whether they can forecast accurately enough one, two and four quarters ahead to be useful for business planning. We use two time-series methods, Box-Jenkins modeling and Holt-Winters adaptive forecasting, to obtain forecasts of shipments of a closely managed product. We show how Box-Jenkins transfer-function models can account for the special events in the data. We develop criteria for choosing a final model which differ from the usual methods and are specifically directed towards maximizing the accuracy of next-quarter, next-half-year and next-full-year forecasts. We find that the best Box-Jenkins models give forecasts which are clearly better than those obtained from Holt-Winters forecast functions, and are also better than the judgmental forecasts of IBM's own planners. In conclusion, we judge that Box-Jenkins models can be appropriate for business planning, in particular for determining at the end of the year baseline business-as-usual annual and monthly forecasts for the next year, and in mid-year for resetting the remaining monthly forecasts.     

The accuracy of individual and group forecasts from business outlook surveys

This paper reports on the accuracy of quarterly multiperiod predictions of inflation, real growth, unemployment and percentage changes in nominal GNP and two of its more volatile components. The survey data are highly differentiated; they cover 79 professional forecasters (mostly economists, analysts and corporate executives). Combining corresponding predictions from different sources can result in significant gains; thus the group mean forecasts are on the average over time more accurate than most of the corresponding sets of individual forecasts. But there is also a moderate degree of consistency in the relative performance of a sufficient number of the survey members, as evidenced in positive rank correlations among ratios of the individual to group root mean square errors.     

Composite leading indicators of underlying inflation for seven EU countries

This paper presents short‐ and long‐term composite leading indicators (CLIs) of underlying inflation for seven EU countries, namely Belgium, Germany, France, Italy, the Netherlands, Sweden and the UK. CLI and CPI reference series are calculated in terms of both growth rates and in deviations from its trend. The composite leading indicators are based on leading basic series, such as sources of inflation, series containing information on inflation expectations and prices of intermediate goods and services. Neftci's decision rule approach has been applied to transfer movements in the CLIs into a measure of the probability of a cyclical turning point, which enables the screening out of false turning point predictions. Finally, CLIs have been used to analyse the international coherence of price cycles. The forecast performance of CLIs of inflation over the past raises hope that this forecast instrument can be useful in predicting future price movements. Copyright © 1999 John Wiley & Sons, Ltd.     

Cycles of juvenile hormone esterase activity during the juvenile hormone-driven cycles of oxygen consumption in pupal diapause of flesh flies

Summary During diapause O₂ consumption in fly pupae is a cyclic event (4-day periodicity at 25°C) driven by cycles of juvenile hormone activity. Levels of juvenile hormone esterase activity change systematically during the cycle, with highest activity observed at the nadir of the O₂ consumption cycle.Supported in part by grant 88-37153-3473 from the USDA Competitive Grants Office and grant 23-088 from the USDA Forest Service. Thanks to Dr Ming Tu Chang for her helpful advice.