Abstract
One of the most striking aspects of the business cycle is that it is a phenomenon which, sooner or later, is reflected in similar patterns in almost every macroeconomic variable, thus illustrating their interdependence. Such interdependence is not restricted to national macroeconomic variables either; it is also a global phenomenon. The world’s economies are strongly integrated. At a national level, the cyclical movements of some economic variables are known to begin earlier than most of the others, because these variables themselves cause the economic tide to turn, or because they detect the ebb and flow at an early stage, or because they are quick to respond to other leading time series. Similarly, at an international level, it is known that the business cycle of some countries leads that of others, implying that the leading country acts as the engine of growth.
Reprinted from Journal ofForecasting, 14 (1), (1995), J.M. Berk and J.A. Bikker, “International Interdependence of Business Cycles in the Manufacturing Industry: The Use of Leading indicators for Forecasting and Analysis”, 1–23 .© 1995 with kind permission from John Wiley and Sons ltd, Chichester.
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Although heuristic by nature, Census X-11 is not entirely without a theoretical foundation. It can be shown that, in its reduced form, Census X-11 is based on a (close approximation of a) special case of a structural time series model (Den Butter and Mourik, 1990).
Stationary series such as the results of consumer and business surveys and interest rates do not, in principle, contain a trend (see Ă–ller, 1990). If a trend is nevertheless estimated, it in fact describes temporary medium-term movements. This does not have adverse consequences for the properties of the cyclical component calculated on the basis of such trends.
Alternatively, the order of the moving average could be variable and the result of a detailed analysis of individual cycles, following the methods developed by Burns and Mitchell (1946). Without denying the usefulness of the latter approach, we will be more interested in a general characterization of cyclical regularities.
Canova (1993, p.2) concludes that neither dynamic economic theory nor the empirical literature gives an indication of the precise relationship between cyclical and secular components.
Our moving-average based method yields results which are almost identical to those based on the HP- α trend with a very high value of α . A high value of α seems
The PAT method is less suitable for predicting trends. That is the only reason for not using this method in this chapter.
The first principal component is the best linear combination of the basic indicators, best in the sense that the particular combination of variables will account for more of the total variance of the selected basic indicators than any other linear combination of variables.
For a number of countries, quarterly figures were also used, from which quasi-monthly figures were constructed with the help of the method developed by Boot et al. (1967).
Portugal was excluded because of insufficient data and Greece because the necessary time series were too short.
Series which are inversely related to the business cycle have a negative factor loading. The inverse values of such series were used before weighting.
The German leading indicators constructed by the OECD and the IFO also perform relatively poorly, especially in the mid-80s.
The question of whether the cyclical movements induced by the oil crisis contain some structural elements in the sense that this crisis also produced a structural break in the trend is beyond the scope of this chapter.
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© 2001 Springer Science+Business Media Dordrecht
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Berk, J.M. (2001). International Interdependence of Business Cycles: The Use of Leading Indicators for Forecasing and Analysis. In: The Preparation of Monetary Policy. Financial and Monetary Policy Studies, vol 35. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3405-8_4
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DOI: https://doi.org/10.1007/978-1-4757-3405-8_4
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