United Nations Statistics Division

1. Backcasting United Nations Statistics Division

2. Overview • Any change in classifications creates a break in time series, since they are suddenly based on differently formed categories • Backcasting is a process to describe data collected before the “break” in terms of the new classification

3. Overview • There is no single “best method” • Factors influencing a decision include: • type of statistical series that requires backcasting (raw data, aggregates, indices, growth rates, ...) • statistical domain of the time series • availability of micro-data • availability of "dual coded" micro-data (i.e. businesses are classified according to both the old and the new classification) • length of the "dual coded" period • frequency of the existing time series • required level of detail of the backcast series • cost / resource considerations

4. Main methods • “Micro-data approach” (re-working of individual data) • “Macro-data approach” (proportional approach) • Hybrids thereof

5. Micro-data approach • Consists of assigning a new activity code (= new classification) to all units in every period in the past (as far back as backcasting is desired) • No other change is required • Statistics are then compiled by standard aggregation • Census vs. survey (weight adjustment issue)

6. Micro-data approach • Requires detailed information from past periods (for all units to be recoded) • More detailed than just the old code • If information is available, results are more reliable than those from macro-approaches

7. Micro-data approach • Issues: • Resource intensive • Need solutions if unit information is not available for a period (not collected, not responded) • Nearest neighbor vs. transition matrix approach

8. Macro-data approach • Also called “proportional method” • This method calculates a ratio (“proportion”, “conversion coefficients”) in a fixed dual coding period that is then applied to all previous periods • The ratios are calculated at the macro level • Could be based on number of units only • Low resource approach • Has a more approximate character

9. Macro-data approach • In simple form, applies growth rates of former time series to the revised level for the whole historical period • More sophisticated methods may use adjustments based on experts’ knowledge • Example: mobile phones

10. Macro-data approach • Assumes that the same set of coefficients applies to all periods • This means it is assumed that the distribution of the variable of interest has not changed between the old and the new classification • Applied to aggregates; does not consider micro-data • Relatively simple and cheap to implement

11. Macro-data approach • Steps: • 1 – estimation of conversion coefficients • Done for dual-coding period • Longer/multiple periods help in overcoming “infant problems’ of the new classification and allow for correction of data • Based on selection of specific variable • 2 – calculation of aggregates using the conversion coefficients • Weighted linear combination • 3 – linking the different segments • Old – overlap – new series • Breaks caused by mainly by change in field of observation • Simple factor or “wedging” • 4 – final adjustment • Seasonal etc.

12. Comparison • Micro-data approach better retains structural evolution of the economy • Micro-data approach does not require choice of a special variable • Macro-data approach reflects evolution based on fixed ratio for a fixed variable • Seasonal patterns may be distorted • Macro-data approach is more cost-efficient • No consideration of micro-data necessary • Assumptions underlying the macro-data approach become invalid over longer periods • “Benchmark years” might help to measure the effect, if data is available

13. Other options • Combinations of both approaches are possible • Ratios for the macro-data approach could be calculated for shorter periods only • Micro-data approach could be used for specific years and the macro-data approach for interpolation between these years • E.g. based on availability of census data • Many factors can influence the choice (see beginning) but data availability is a key practical factor