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International Workshop on Industrial Statistics Dalian, China June 2010

International Workshop on Industrial Statistics Dalian, China June 2010. Benchmarking of monthly/quarterly IIP. Shyam Upadhyaya UNIDO. Outline. Divergence of sub-annual and annual index series Reasons of divergence Purpose of benchmarking Benchmarking methods. What is benchmarking?.

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International Workshop on Industrial Statistics Dalian, China June 2010

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  1. International Workshop on Industrial StatisticsDalian, ChinaJune 2010 Benchmarking of monthly/quarterly IIP Shyam Upadhyaya UNIDO

  2. Outline • Divergence of sub-annual and annual index series • Reasons of divergence • Purpose of benchmarking • Benchmarking methods

  3. What is benchmarking? • Benchmarking refers to a statistical technique, which is aimed to correct the inconsistencies between estimates of the same variable obtained from the data collected with different frequencies • Underlying assumption is that low frequency data, such as results of annual surveys, are more comprehensive and accurate than high frequency data, such as monthly/quarterly survey results. • Therefore, in benchmarking process high frequency data (Indicators) are aligned to low frequency data (Benchmark) • Inconsistency is detected by the movement of ratio between Benchmarkvalue (B) and Indicator value (I).

  4. Divergence of data series by source Malaysia INDIA Data source: UNIDO MVA Database UNSD Monthly statistics

  5. Major reasons of divergence • Difference in coverage and sample Annual survey has broader coverage and more representative sample. There may also be the difference in frame. • Difference in definition and variables Output replaces the value added for growth measures • Accounting period Calendar year versus accounting year effect • Estimation method, non-response treatment, imputation etc.

  6. Purpose of benchmarking • Create a coherent high-frequency data series by correcting the difference between Benchmark and Indicator values • As mentioned in IIP 2010 “combine the relative strengths of low- and high-frequency data while preserving as much as possible the short-term movements” • Improve the quality of production data in terms of comparability and coherence in time series.

  7. Benchmarking methods • Existing recommendations including IIP 2010, OECD manuals and IMF Quarterly National Accounts Manual have mainly described following benchmarking methods: • Pro Rata distribution • Proportional Denton Method • ARIMA-model based methods • General Least-squares regression models Quarterly national accounts manual describes all these methods in detail and with examples

  8. Pro Rata distribution Direct distribution of annual value in proportion of quarterly indicator QVA – Estimated quarterly value added AVA – Annual value added I – Indicator value for quarter

  9. Estimation of quarterly value using Pro Rata distribution

  10. Merits and demerits of Pro Rata Distribution Easy to compute and interpret No special software is needed, it can be done in spread-sheet Quarterly estimates can be derived each year independently Estimates are well aligned to benchmark value and it is fairly reliable when BI ratios are stable This method smoothens quarterly estimates only within a year Concentrates bias in one quarter and cause the abrupt change As a result, it creates so called “step problem”, therefore it is not recommended for longer time series

  11. Proportional Denton method • This method allows to find a re-aligned VA estimates by minimizing its difference with indicator values VA t –Value added for quarter t AVA – annual value added T – last quarter for which data are available • Unlike Pro Rata Distribution BI ratio in this method • changes gradually so there is no jump from one year to another • However, computation for estimation of quarterly VA is more • complicated, thus requires specialized software.

  12. Example of proportional Denton method No big jump in year change Taken from IMF manual on quarterly GDP

  13. Some other aspects of benchmarking • Benchmarking is done retrospectively when annual • survey data are available • Benchmarking is a part of temporal disaggregation that • also comprises Interpolation and Extrapolation • Interpolation is done when no measures currently exists • for target variable; extrapolation aims to provide an • estimate of monthly/quarterly indicator when annual • estimate is not available; normally by applying last • available BI ratio or by forecasting new BI ratio • Benchmarking can also be done to align monthly indices • to quarterly estimates

  14. Thank you for your attention!

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