KOF Economic Barometer Stabilises at a High Level

In February 2018, the KOF Economic Barometer climbed from 107.6 in January (revised up from 106.9) by 0.4 points to a level of 108.0. Accordingly, the barometer at least partly corrected its decline in January. From a longer-term perspective, the early 2017 improved sentiment appears to continue.

The strongest positive contributions to this result come from the construction sector, followed by the hospitality industry and the indicators relating to domestic private consumption. The indicators from the financial sector and the exporting industry have practically remained unchanged. An overall slight negative signal stems from manufacturing.

Within the manufacturing industry, the slightly impaired total outlook can be attributed to diverging signals. A clearly deteriorating sentiment is visible in mechanical engineering. The electrical and the paper industries, on the other hand, reveal an improving sentiment and the wood and textile industries are somewhat more optimistic, too. The remaining industry branches record hardly any change.

KOF Economic Barometer and reference time series: annual update

In September 2017, the scheduled annual update of the KOF Economic Barometer took place. The annual update of the Barometer concerns the following steps: redefinition of the pool of indicators that enter the selection procedure, update of the reference time series and renewed execution of the variable selection procedure. The updated reference series is the smoothed continuous growth rate of the Swiss Gross Domestic Product (GDP) according to the new System of National Accounts ESVG 2010, released in early September 2017, which takes into account the release of the previous year’s annual GDP data by the Swiss Federal Statistical Office. As a result of the indicator variable selection procedure, the updated KOF Economic Barometer is now based on 273 indicators (instead of 272 as in the previous vintage) from a pool of almost 500 potential indicator series. They are combined using statistically determined weights.