On seasonal adjustment of quarterly statistics on investments in manufacturing
1. WHAT IS SEASONAL ADJUSTMENT?
5. QUALITY OF SEASONAL ADJUSTMENT
6. SPECIFIC ISSUES ON SEASONAL ADJUSTMENT
Monthly and quarterly time series are often characterised by considerable seasonal variations, which might complicate their interpretation. Such time series are therefore subjected to a process of seasonal adjustment in order to remove the effects of these seasonal fluctuations. Once data have been adjusted for seasonal effects by X-12-ARIMA or some other seasonal adjustment tool, a clearer picture of the time series emerges.
For more information on seasonal adjustment: metadata on methods: seasonal adjustment
The statistics on quarterly investments in manufacturing is part of a system of short-term statistics compiled to monitor the economy. The primary goal of the survey is to monitor the development in actual and estimated investments as they are important indicators of the demand of capital goods in the economy.
Historical data reveal that time series on quarterly investments in the manufacturing industry have a clear seasonal pattern (chart 1), and this might complicate their interpretation. However, it is possible to adjust for this type of seasonal variations (chart 2).
Chart 1: Estimated seasonal factors for 2009
Chart 2: Quarterly investments in manufacturing
The seasonal factors (chart 1) show that quarterly investments seem to be low in the first quarter and high in the fourth quarter. The estimates for the second and the third quarter lie in between the two extremes and more or less share the same value. The stable seasonal pattern in the unadjusted series indicates a low degree of white noise. This conclusion is supported by the small gap between the seasonally adjusted series and the trend series (chart 2).
No empirical studies are conducted to explain the seasonal pattern for quarterly investments in the manufacturing industry. However, years of experience has shown that the following factors are possible sources of seasonal variations.
Seasonal variations
Temperatures and weather conditions seem to influence investment behaviour.
The nature of projects
Projects often start at the beginning of the year and are scheduled to be finished within twelve months. Investments are most likely to be low in the initial phase of a project. The reason for this is that the groundwork often costs substantially less than new machinery and equipment.
Accounting principles
Some of the smaller establishments choose to report total investments in the fourth quarter of the year. This is against the guidelines, but can be explained by a lack of proper tools for making budgets and measure costs.
Seasonally adjusted series are published for estimated and actual quarterly investments in manufacturing.
Pre-treatment is an adjustment for variations caused by calendar effects and outliers.
Calendar adjustment involves adjusting for the effects of working days/trading days and for moving holidays. Working days/trading days are adjustment for both the number of working days/trading days and for that the composition of days can vary from one month to another
Outliers, or extreme values, are abnormal values of the series.
Pre-treatment requires choosing an ARIMA model, as well as deciding whether the data should be log-transformed or not.
The decomposition scheme specifies how the various components – basically trend-cycle, seasonal and irregular – combine to form the original series. The most frequently used decomposition schemes are the multiplicative, additive or log additive.
In some series, consistency between raw and seasonally adjusted series is imposed.
In some series, consistency between seasonally adjusted totals and the aggregate is imposed .For some series there is also a special relationship between the different series, e.g. GDP which equals production minus intermediate consumption.
Direct seasonal adjustment is performed if all time series, including aggregates, are seasonally adjusted on an individual basis. Indirect seasonal adjustment is performed if the seasonally adjusted estimate for a time series is derived by combining the estimates for two or more directly adjusted series.
When performing seasonal adjustment of a time series, it is possible to choose the period to be used in estimating the model and the correction factors. Correction factors are the factors used in the pre-treatment and seasonal adjustment of the series.
Seasonally adjusted data may change due to a revision of the unadjusted (raw) data or the addition of new data. Such changes are called revisions, and there are several ways to deal with the problem of revisions when publishing the seasonally adjusted statistics.
A table containing selected quality indicators for the seasonal adjustment is available here.
For more information on the quality indicator in the table see: metadata on methods: seasonal adjustment
Statistic Norway Metadata on methods: seasonal adjustment
The Committee for Monetary, Financial and Balance of Payments statistics: ESS-Guidelines on seasonal adjustment
EUROSTAT: Seasonal Adjustment. Methods and Practices
US census: X-12-ARIMA-manual
Joaquin Rodriguez: Sesongjustering i praksis – en innføring, Notater 97/29, Statistisk sentralbyrå
Dinh Quang Pham: Nye US Census-baserte metoder for ukedagseffekter for norske data, Notater 2008/58, Statistisk sentralbyrå
Dinh Quang Pham: Ny metode for påskekorrigering for norske data, Notater 2007/43, Statistisk sentralbyrå.
Ole Klungsøyr: Sesongjustering av tidsserier. Spektralanalyse og filtrering, Notat 2001/54, Statistisk sentralbyrå
Dinh Quang Pham: Innføring i tidsserier - sesongjustering og X-12-ARIMA, Notater 2001/2, Statistisk sentralbyrå