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This paper deals with the existence and identification of a common European growth cycle. Univariate Markov switching autoregressions are used for individual countries in order to detect changes in the mean growth rate of industrial production. A Markov switching vector autoregression model is then used to identify a common cycle in Europe. Three important results are obtained: we find a common unobserved component governing European business cycle dynamics, suggesting the existence of a common business cycle; we propose a dating of the business cycle, both for an index of industrial protection and GDP, and both chronologies appear to be consistent; and finally we retrieve an important set of stylized facts and relate these with those reported for the US. Finally two further issues are investigated: first, the contribution of the European business cycle to the individual country cycles; and second, we undertake an impulse response analysis to investigate the response of each individual country to European expansions and recessions. sectors the relative importance of country-specific disturbances has declined in the 1980s. Norrbin and Schlaenhauf (1996) 3 extend this analysis to a dynamic setup 4 and analyze the behavior across countries and industries in terms of industry-specific factors, nation-specific factors and the common factor. The set of countries comprises nine industrial economies and the sample extends from 1956:1 to 1992:4. Their analysis suggests that, in this period, the nation-specific factor is the most relevant in explaining the variation of output. Another area of debate has been the extent to which the cyclical component of some measures of economic activity comoves across countries in the Union. An indication of a development of this type can be found in the cyclical crosscorrelation analysis offered by Artis and Zhang (1997) and Artis and Zhang (1999), who examine whether the correlation between the business cycles in ERM countries and the cycle in Germany has increased since the formation of the ERM. Their results show that the cycles in the ERM countries became more synchronized with the German one, suggesting the emergence of a European business cycle. Christodoulakis et al. (1995) focus on the 12 EU countries (as of 1994). They analyze the time series of a set of key macroeconomic variables since the 1960s and find no evidence of a core-periphery distinction. In their study they find that business cycles are similar for the variables they call endogenous (such as income and consumption), whereas this is not the case for those variables they refer to as exogenous (i.e., variables controlled by the government such as government spending or variables dependent on national institutions, such as labour market variables). Contrary to the results in Artis and Zhang (1997) and Artis and Zhang (1999), Dickerson et al. (1998) find no evidence that the business cycles in the EU 12 have become more correspondent after the formation of the ERM. They use data from 1960 to 1993 on GDP, private final co...

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We analyse the relative performance of the IMF, OECD and EC in forecasting the government deficit, as a ratio to GDP, for the G7 countries. Interesting differences across countries emerge, sometimes supporting the hypothesis of an asymmetric loss function (i.e. of a preference for underprediction or overprediction), and potential benefits from forecast pooling.

Data are now readily available for a very large number of macroeconomic variables that are potentially useful when forecasting. We argue that recent developments in the theory of dynamic factor models enable such large data sets to be summarized by relatively few estimated factors, which can then be used to improve forecast accuracy. In this paper we construct a large macroeconomic data set for the UK, with about 80 variables, model it using a dynamic factor model, and compare the resulting forecasts with those from a set of standard time-series models. We find that just six factors are sufficient to explain 50% of the variability of all the variables in the data set. These factors, which can be shown to be related to key variables in the economy, and their use leads to considerable improvements upon standard time-series benchmarks in terms of forecasting performance.