Appendix I: Scope of the survey

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The interest in and need for detailed statistics on the automobile industry arose years ago, when the industry began playing a prominent role in the Canadian economy. For over 50 years, Statistics Canada has been collecting and disseminating statistical information on the retail sales of new motor vehicles. Not only are these statistics required to produce estimates of Canadian consumer spending for the national accounts and the gross domestic product, but they have also served and continue to serve as one of the most important, most often requested indicators of the health of Canada's economy. ¹ 

However, estimates of the total retail sales volume of new motor vehicles were not sufficient to meet the incessant demand for more information. There came an increasing need for data on the characteristics of the fleet of new vehicles sold at retail, either to learn more about the preferences of Canadian consumers or to assess the penetration of the Canadian market by major foreign manufacturers. Statistics on new motor vehicle sales were expanded in the early 1970s to provide separate figures for Japanese vehicle sales.

The statistics in this report cover exclusively retail sales of new motor vehicles within Canada's borders. Data are derived from a postal survey (a census - see the methodology section at the end of the report for more details).

Other statistics connected with the automobile industry (sales of new and used car dealers, auto parts and accessories stores and so on) are available on the monthly Retail Trade survey (Catalogue No. 63-005). For further information, please contact the Retail Commodity Section, Distributive Trades Division, Statistics Canada, Ottawa, K1A 0T6, (Telephone: 613-951-3549 or toll free at 1-877-421-3067).

Data quality and methodology

This survey is a census of all motor vehicle manufacturers and importers known to be active in Canada.

The value of sales is reported at selling price to consumer (i.e. the manufacturer's invoice price plus estimated dealer's markup, charges for standard equipment and options, less provincial sales tax and federal goods and services tax (G.S.T.) and harmonized sales tax (H.S.T.).

The response rate for this survey is generally 100%. Minimal estimation is required so there is little bias resulting from non-response introduced in these data.

All attempts have been made to minimize the non-sampling errors: the questionnaires used in the survey have been carefully designed to minimize different interpretations; detailed acceptance testing has been carried out for the different stages of editing and processing.

Data confidentiality

Statistics Canada is prohibited by law from publishing any statistics which would divulge information relating to any identifiable business without the previous consent in writing of that business. ²  The disclosure of data likely to reveal specific information on the activity of a particular company is systematically and rigorously controlled and regulated to prevent the publication or disclosure of any information deemed confidential. The rules regarding confidentiality are therefore applied in such a way that no data are shown for cells (or statistical units). If information is withheld for reasons of confidentiality an "X" will appear in the appropriate cell of this publication.

Another way of preserving data confidentiality without having to enter an "X" is to combine (consolidate) the information from selected regions (or selected cells) for the same item. The data in question can be aggregated and published in a less detailed, but still confidential, form. For example, the data for the Northwest Territories, the Yukon and Nunavut are included with the British Columbia data.

Seasonal adjustment

Time series contain the elements essential to the description, explanation and forecasting of the behaviour of an economic phenomenon: "They are statistical records of the evolution of economic processes through time." ³  Economic time series such as the New Motor Vehicles Sales Survey can be broken down into five main components: the trend-cycle, seasonality, the trading-day effect, the Easter holiday effect and the irregular component.

The trend represents the long-term change in the series, whereas the cycle represents a smooth, quasi-periodical movement about the trend, showing a succession of growth and decline phases (e.g., the business cycle). These two components—the trend and the cycle—are estimated together, and the trend-cycle reflects the fundamental evolution of the series. The other components reflect short-term transient movements.

The seasonal component represents sub-annual, monthly or quarterly fluctuations that recur more or less regularly from one year to the next. Seasonal variations are caused by the direct and indirect effects of the climatic seasons and institutional factors (attributable to social conventions or administrative rules; e.g., Christmas).

The trading-day component originates from the fact that the relative importance of the days varies systematically within the week and that the number of each day of the week in a given month varies from year to year. This effect is present when activity varies with the day of the week. For instance, Sunday is typically less active than the other days, and the number of Sundays, Mondays, etc., in a given month changes from year to year.

The Easter holiday effect is the variation due to the shift of part of April's activity to March when Easter falls in March rather than April.

Lastly, the irregular component includes all other more or less erratic fluctuations not taken into account in the preceding components. It is a residual that includes errors of measurement on the variable itself as well as unusual events (e.g., strikes, drought, floods, major power blackout or other unexpected events causing variations in respondents' activities).

Thus, the latter four components—seasonal, irregular, trading-day and Easter holiday effect—all conceal the fundamental trend-cycle component of the series. Seasonal adjustment (correction of seasonal variation) consists in removing the seasonal, trading-day and Easter holiday effect components from the series, and it thus helps reveal the trend-cycle. While seasonal adjustment permits a better understanding of the underlying trend-cycle of a series, the seasonally adjusted series still contains an irregular component. Slight month-to-month variations in the seasonally adjusted series may be simple irregular movements. To get a better idea of the underlying trend, users should examine several months of the seasonally adjusted series.

Since April 2008, New Motor Vehicle Sales Survey data are seasonally adjusted using the X-12-ARIMA ⁴  software. The technique that is used essentially consists of first correcting the initial series for all sorts of undesirable effects, such as the trading-day and the Easter holiday effects, by a module called regARIMA. These effects are estimated using regression models with ARIMA errors (auto-regressive integrated moving average models). The series can also be extrapolated for at least one year by using the model. Subsequently, the raw series—pre-adjusted and extrapolated if applicable— is seasonally adjusted by the X-11 method.

The X-11 method is used for analysing monthly and quarterly series. It is based on an iterative principle applied in estimating the different components, with estimation being done at each stage using adequate moving averages. ⁵  The moving averages used to estimate the main components—the trend and seasonality—are primarily smoothing tools designed to eliminate an undesirable component from the series. Since moving averages react poorly to the presence of atypical values, the X-11 method includes a tool for detecting and correcting atypical points. This tool is used to clean up the series during the seasonal adjustment. Outlying data points can also be detected and corrected in advance, within the regARIMA module.

Lastly, the annual totals of the seasonally adjusted series are forced to the annual totals of the original series.

Unfortunately, seasonal adjustment removes the sub-annual additivity of a system of series; small discrepancies can be observed between the sum of seasonally adjusted series and the direct seasonal adjustment of their total. To insure or restore additivity in a system of series, a reconciliation process is applied or indirect seasonal adjustment is used, i.e. the seasonal adjustment of a total is derived by the summation of the individually seasonally adjusted series.

To assist the user, the Months of Cyclical Dominance number, MCD, is provided. ⁶  The MCD is the shortest monthly span for which a certain ratio becomes and remains less than one, that is, the ratio for which the absolute average percentage change of the trend-cycle of the seasonally adjusted series becomes greater than the absolute average per cent change of the irregular component. The MCD can be interpreted as the number of months over which a change in the seasonally adjusted series must move in a given direction before one can be reasonably certain that the trend-cycle of that series also has moved in the given direction. Clearly small MCD's are desirable and indicative of a smooth series. Applying a moving average to the seasonally adjusted data of MCD length tends to smooth irregular movements which may obscure the underlying trend-cycle. The MCD moving average provides a method of reducing all types of series to approximately the same degree of smoothness, whatever the size of the irregular component in the original series.

Revision procedures

The seasonally adjusted estimates are revised for the three months preceding the reference period. Monthly unadjusted data are revised for the previous month, when new information is made available to Statistics Canada. In addition, unadjusted new motor vehicle sales are revised each year, for the latest year, in the February issue of this publication. At that time, monthly seasonally adjusted estimates for the latest four years of data are revised.

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