GDP forecast based on semantic business cycle identification - release: 2015-11-11

    Table: Swiss real gross domestic product with forecast
Year-to-year growth (%) of Swiss real gross domestic product (GDP)
standard error
seco estimates*

Sources: Own calculations, forecast for 2015(3), fitted values otherwise, *seco releases (left: May 29, 2015, right: August 28, 2015).

Sample: 2000 (2) - 2015 (2), Forecast: 2015 (3)

Note: Forecasts obtained by best nowcasting model.

«home   Special Notes: In the summer, the Swiss Statistical Office (BfS) has revised the official GDP data. Accordingly, the quarterly estimates have been adjusted now featuring a lower overall GDP growth during the past two years. While the past two quarters have seen an increase in the annualised growth rate all previous quarters through to 2013 have been revised downward. Based on these revised data the new estimate of actual year-on-year growth of Swiss value added by means of the «KOF surprise indicator» value obtains a growth rate of 1.5 up from 0.6 in the previous period. The revised, now lower GDP figures of last year contribute to this optimistic outlook.
To the extent that flash estimates of GDP figures are based on quantitative output indicators such as the «KOF surprise indicator» downward revisions can be expected in the future. According to several newspapers articles many Swiss companies have responded to the appreciation of the Swiss Franc by keeping up their output level at the expense of their profit margins. If true output based indicators systematically overestimate the actual value added. Therefore, future revisions might draw a less sanguine picture.
«home   Published: Business cycle dynamics: A bottom-up approach with Markov-chain measurement now available from the OECD Journal: Journal of Business Cycle Measurement and Analysis. Discover the hidden potentials of the «KOF surprise indicator». Details»
    Figure: Business cycle indicator based on semantic identification in comparison to real GDP: Ex-ante forecast properties



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    *Standard error of regression refers to baseline model published in the first release.