Stats SA’s seasonal adjustment models are part of regular rebasing
By Michael Manamela - Oct 8th, 10:53
Every year, SA’s economic activity peaks in the fourth quarter (October-December). On the production (or industry) side of GDP, this pattern is driven mainly by three industries: manufacturing; trade, catering and accommodation; and transport, storage, and communication.
Underlying the upward surge in these industries is the festive season, which we see clearly on the expenditure side of GDP, with an annual peak in household expenditure in the fourth quarter.
On the production side, manufacturers know the big spending is coming, and ramp up production sharply in October and November, thereby helping lift fourth-quarter GDP. In the first quarter (January-March) there is a corresponding dip in GDP.
Because these and other seasonal patterns are relatively regular, their effect on GDP can be estimated and extracted from the data to arrive at a “seasonally adjusted” GDP (and seasonally adjusted agriculture, mining, and manufacturing figures). Showing economic growth excluding the seasonal effects is important for measuring quarter-on-quarter GDP performance.
In her recent column, Neva Makgetla raised the issue of negative first-quarter growth in the GDP figures Stats SA has published for the last four years since 2016 (“Skew first-quarter GDP data needs straightening”, September 30). She argues that Stats SA’s seasonal adjustment model is not doing enough, thereby resulting in negative first-quarter growth rates that are not a true reflection of the economy.
The pattern of economic activity does indeed change over time and Makgetla is therefore right to point out the importance of keeping estimation methods up to date. Identifying and distinguishing between changes that are structural, seasonal, short term, long term or simply ad hoc are important dimensions of estimating GDP and the vast range of economic indicators that are used in its compilation.
Stats SA rebases and benchmarks the country’s national accounts every five years, with the next round scheduled for September 2020; our seasonal adjustment models are very much part of this exercise. Regarding seasonal adjustment, which relies on many observations over the long term, it can take several years to diagnose which changes are seasonal and which are not.
An interesting empirical question that arises is which industries were behind the negative first-quarter growth over the last few years (measured quarter-on-quarter, seasonally adjusted). In 2016 the largest negative contributor by far was mining. In 2017 the main culprits were manufacturing and trade, which were both positive contributors in 2016. In 2018 the weakness was widespread, with negative contributions from agriculture, mining, manufacturing, and trade. In 2019 it was these four again but also the transport, storage and accommodation industry.
This mixture of results is evident at lower levels as well. For instance, of the 10 divisions in manufacturing production, two were consistently negative in the first quarter in the four years 2016-2019 (quarter-on-quarter, seasonally adjusted). The other eight were a mixture of positives and negatives. The more varied the results after seasonal adjustment, the trickier it becomes to identify whether the seasonal adjustment model requires an update.
Makgetla argues that the annualisation of growth rates can be misleading. There are a number of other options for the headline GDP growth rate, such as year-on-year using unadjusted values, year-on-year using seasonally adjusted values, or quarter-on-quarter (not annualised) using seasonally adjusted values. Each has its advantages and disadvantages, and different countries have made different choices (including whether to use the production or expenditure side of GDP).
Stats SA has been treating the seasonally adjusted annualised rate (on the production side) as the headline number for many years, but we are open to listening to our user community and considering all proposals.Business Live
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