In May 2018, the seasonally adjusted industrial production index is estimated to increase by 0.7% compared with April. On average for the March-May quarter, production decreased by 0.4% on the previous quarter.
The seasonally adjusted monthly index shows cyclical growth in all sectors; positive changes are recorded by energy (+1.2%), intermediate goods (+0.9%), capital goods (+0.4%) and consumer goods (+0.2%).
Corrected for calendar effects, in May 2018 the index increased in trend terms by 2.1% (the working days were 22 as in May 2017). On average for the first five months, production grew by 2.8% on an annual basis.
In May 2018, the indices corrected for calendar effects recorded positive trend changes in all groupings; instrumental goods (+3.1%), energy (+2.2%) and consumer goods (+2.1%) grew appreciably, while the increase in intermediate goods was more limited (+1.3%).
The manufacturing sectors that register the higher growth trend are the manufacture of electrical appliances and non-electrical household appliances (+7.3%), the production of basic pharmaceutical products and pharmaceutical preparations (+6.6%) and machinery and equipment nec (+5.1%). The greatest decreases were instead recorded in the manufacture of computers, electronic and optical products (-1.6%) and metallurgy and metal products, excluding machinery and plants (-1.0%).
FULL TEXT AND METHODOLOGICAL NOTE
Production of basic pharmaceutical products and pharmaceutical preparations – May 2018
Seasonally adjusted data, cyclical and trend percentage changes
May 18/Apr 18: -2,5
Tue 18-May 18/Dec 17-Feb 18: +0,9
Data corrected for calendar effects. Trend percentage changes
May 18/May 17: +6,6
Jan-May 18/Jan-May 17: +7,2
2017: +7,4
2016: +2,2
2015: +5,4
2014: -1,2
2013: +5,1
2012: -4,1
2011: +0,3
2010: +0,5
Note:
Seasonally adjusted data: data purged, using specific statistical techniques, of fluctuations attributable to the seasonal component (due to meteorological, customary, legislative factors, etc.) and, if significant, of calendar effects. This data transformation is the most suitable for capturing the cyclical evolution of an indicator.
