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Recent imperfect capital market theories predict the presence of asymmetries in the variation of small and large firms' risk over the economic cycle. Small firms with little collateral should be more strongly affected by tighter credit market conditions in a recession state than large, better collateralized ones. This paper adopts a f lexible econometric model to analyze these implications empirically. Consistent with theory, small firms display the highest degree of asymmetry in their risk across recession and expansion states, which translates into a higher sensitivity of their expected stock returns with respect to variables that measure credit market conditions.

We propose a comprehensive methodology to characterize the business cycle comovements across European economies and some industrialized countries, without imposing any given model but trying to 'leave the data speak'. We develop a novel method to show that there is no evidence of a 'European economy' that acts as an attractor to the other economies of the area. We show that the establishment of the Monetary Union has not significantly increased the level of comovements across Euro-area economies. Finally, we are able to explain an important proportion of the distances across their business cycles using macrovariables related to the structure of the economy, to the directions of trade, and to the size of the public sector. r

We set out a model to compute short-term forecasts of the euro area GDP growth in real-time. To allow for forecast evaluation, we construct a real-time data set that changes for each vintage date and includes the exact information that was available at the time of each forecast. With this data set, we show that our simple factor model algorithm, which uses a clear, easy-to-replicate methodology, is able to forecast the euro area GDP growth as well as professional forecasters who can combine the best forecasting tools with the possibility of incorporating their own judgement. In this context, we provide examples showing how data revisions and data availability a¤ect point forecasts and forecast uncertainty.Keywords: Business Cycles, Output Growth, Time Series. JEL Classi…cation: E32, C22, E27We th a n k th e e d ito r a n d th e tw o re fe re e s fo r e x tre m e ly va lu a b le c o m m e nts w h ich h ave g re a tly im p rove d th e c o nte nts o f o u r p a p e r. We a lso th a n k Fra n c is D ie b o ld , J a n J a c o b s, S im o n va n N o rd e n , K e n Wa llis a n d th e p a rtic ip a nts a t th e 5 th Wo rk sh o p o n Fo re c a stin g Te ch n iq u e s, a t th e 5 th C o llo q u iu m o n M o d e rn To o ls fo r B u sin e ss C y c le A n a ly sis, a n d a t th e inte rn a l se m in a r se rie s o f B a n c o d e E sp a ñ a , th e E u ro p e a n C e ntra l B a n k , C E M F I, th e U n ive rsity C a rlo s I I I, th e U n ive rsity o f A lic a nte a n d th e U n ive rsity o f N ava rra fo r h e lp fu l c o m m e nts a n d su g g e stio n s. We re c e ive d e x tre m e ly va lu a b le re se a rch a ssista n c e fro m C a m ilo U llo a . We a re a lso g ra te fu l to th e E u ro A re a U n it o f th e B a n k o f S p a in fo r a ll th e p re lim in a ry m e e tin g s th a t le a d to th is p a p e r. F in a lly, w e a re th a n k fu l to J u a n A y u so fo r h is c le ve r id e a c o n c e rn in g th e n a m e o f th e in d ic a to r. A ny re m a in in g e rro rs a re o u r ow n re sp o n sib ility. P a rt o f th e p a p e r w a s w ritte n w h e n th e …rst a u th o r w a s v isitin g th e B a n k o f S p a in . M a x im o C a m a ch o th a n k s th e …n a n c ia l su p p o rt o f th e p ro je c t S E J 2 0 0 6 -1 5 1 7 2 . T h e v ie w s in th is p a p e r a re th o se o f th e a u th o rs a n d d o n o t re p re se nt th e v ie w s o f th e B a n k o f S p a in o r th e E u ro S y ste m .

SUMMARYWe set out a model to compute short-term forecasts of the euro area GDP growth in real time. To allow for forecast evaluation, we construct a real-time dataset that changes for each vintage date and includes the exact information that was available at the time of each forecast. With this dataset we show that our simple factor model algorithm, which uses an easy-to-replicate methodology, is able to forecast the euro area GDP growth as well as professional forecasters who can combine the best forecasting tools with the possibility of incorporating their own judgement. In this context, we provide examples showing how data revisions and data availability affect point forecasts and forecast uncertainty.

This paper aims at contributing to the understanding of how the ECB conducts monetary policy as seen from a money market perspective. More specifically it covers two different issues. First, it looks at the 'learning period' for banks since the Eurosystem started implementing the single monetary policy. It shows that during the first three weeks of 1999 the narrow corridor in place during this period was effective in limiting daily volatility of the money market overnight rates. In addition, the behaviour of banks and market rates during this period provides evidence that learning was taking place. Second, it looks at how well money market participants have anticipated the monetary policy decisions taken by the ECB. To do so, the paper analyses whether the announcements of monetary policy decisions to maintain or change interest rates impact on the stochastic behaviour of interest rates. Looking at the EONIA rates within the reserve maintenance periods, we find that the announcement of monetary policy decisions does not change significantly the level or volatility of overnight rates.

This paper provides a comprehensive framework to analyze business cycle features other than synchronization. We use stationary bootstrap and model-based clustering methods to analyze similarities and differences among the European cycles. We find evidence that the length, deep and shape of cycles differ across European countries and that these differences are not decreasing over time. Finally, even though we find some correlation between business cycle synchronization and characteristics, there is important information in the characteristics that is not captured by the synchronization measures. r