<\/strong>In the arithmetic linking method the relationship between the indices in the old series (y) and those in the new series (x) us assumed to be linear =cx , where is the conversion factor given by y.<\/p>\n\n\n\n c=x, y and x being average values of the indices in the two series for the year chosen as the base period (12 months) of the new series (X=100)<\/p>\n\n\n\n
Ratio method: The Month-wise ratio of new indices and old indices are worked out first, and then the average of ratios is taken as linking factors.
Regression method: The relationship is based on y=a+bx, <\/p>\n\n\n\n
Where a and b would be so\nestimated that the sum of squares of deviations of the actual values of from the estimated values is minimum. <\/p>\n\n\n\n
The linking factor derived from these three\ndifferent formulae may vary and provide different estimates. In view of the conceptual and methodological\ndifference between the 2004-05 and 2011-12 series, the estimate of a linking factor will vary depending on the type\nof method used. Therefore, the ministry\nof economic adviser recommends users free to choose any method as may\nbe considered appropriate by them.<\/p>\n\n\n\n
In the past, in order to maintain continuity in the time series data on the wholesale price index, the linking factor currently used by the government is geometric mean<\/em> rather than arithmetic mean user earlier (the base year 2004-05 series). This is as per international best practice and similar to the practice adopted for the CPI. Data available for the overlapping period with the old base and new base indicate that the WPI inflation as per the new base is consistently lower than the same as per the old base. Several reasons are responsible for this outcome. The geometric mean itself has significantly moderated WPI inflation, besides other factors such as a change in the composition of the basket, weighting diagram, and so on. Moderation of WPI as per revised base has pushed up real GDP\/GVA considerably during recent years.<\/p>\n\n\n\nLessons Learned<\/h3>\n\n\n\n
Undertaking this work with Voltas allowed us to translate what we know about wholesale price index and regression into the context of pricing healthcare engineering projects. Here are a few suggestions for companies interested in using regression analyses to calculate the price index. <\/p>\n\n\n\n
- Regression Analysis cannot be used\nto reflect the true level of price indices, and as such, they have to\nnecessarily use the linking factor given by the ministry or the arithmetic\nconversion or weighted average. <\/li>
- Bias or errors in the price index\nmainly due to use of imperfect indexing formula, change in the wholesale\nlandscape, inappropriate quality adjustment, over recognition of quality\nchange, failure to recognize quality change or new goods, or variation in\nexisting goods, failure to include environmental improvements and the value of\nbusiness services, poor representation of commodities,<\/li>
- Secondly, the assumption of changes\nin the dependent variable based on a unit change in independent variables\ncannot be the common criterion and especially in this case since there are a variety\nof factors which go to decide the price indices released by the ministry.<\/li>
- Although both regressors seek to\nexplain the same dependent variable, they are neither measured nor can they be converted to, meaningfully\ncommon units of measurement both conceptually, methodologically and statistically.\nEspecially, when there is a changing in the basket of commodities, increase no.\nof price quotations, 17 vegetables rather than 11, collect prices from more\ncentres, to get better estimates. assigning new weights to the commodities does\nnot include taxes in order to remove the impact of fiscal policy and many more\nchanges between new and old series. This is precisely the point: Only when\nexplanatory variables are on meaningfully common units of measurement is there\na chance of comparison. If there is no common unit of measurement, there is no\nchance of meaningful comparison. <\/li><\/ul>\n\n\n\n
- Check what\u2019s been proposed already. In this case, the\nMinistry has recommended using linking factor proposed by them. Especially item\nin the revised WPI is based on the net traded value of the item in the base\nyear 2011-12. Secondly, item-level indices are being compiled based on\nstatistically robust geometric mean as compared to Arithmetic mean used in the\nWPI 2004-05 series. However, it is extremely difficult to obtain the data in a timely fashion.\nA possible alternative is to apply the geometric mean method to the index\ncalculation. As explained above, if it is assumed that expenditure shares do\nnot change with relative price changes (i.e., the elasticity of substitution\nequals one), then the geometric mean index is equal to the theoretical price\nindex. If the substitution effect is relatively small (i.e., the elasticity of\nsubstitution is smaller than one), the index will be biased downward. Taking\nthese factors into account, the WPI using the geometric mean formula (WPI-UGM)\napplies the geometric mean method only at the lower level of aggregation–from\n\u201csample item\u201d to \u201citem class\u201d–the level at which substitution between\ncommodities seems more likely to occur. At the higher level of\naggregation–from \u201citem class\u201d to \u201call items\u201d–the arithmetic mean of the\nLaspeyres method is retained. This approach is based on the presumption that\nquantity changes are influenced not by relative prices of goods with little\nrelevance to each other, but by relative prices of goods whose attributes are\nsimilar. For example, demand for instant coffee is not affected by changes in\nthe relative price of passenger cars but is likely to be affected to some\nextent by changes in the relative price of regular coffee. Of course, the above\napproach is not applicable to all cases, since technological advances and other\nfactors can trigger substitution even between commodity groups. Judging from\nthe historical data, however, indexes partially adopting the geometric mean\nshow less bias while indexes applying the geometric mean at all aggregation\nlevels display a downward bias. Therefore, the newly published WPI presumably contains less downward bias than\nwould have been the case if the geometric mean method had been applied at all\naggregation levels.<\/li><\/ul>\n\n\n\n
The report is prepared by a Statswork R&D\nsenior researcher who\u2019s suitably qualified and approved by a director of\nresearch who has knowledge about economic indicators, especially wholesale\npricing. <\/p>\n","protected":false},"excerpt":{"rendered":"
Statswork, a Unit of Guires Ltd., has worked with Voltas India\u2019s premier air-conditioning company and a leading provider of engineering and back-up services. The Domestic Projects Group of Voltas contributes to nation-building through the execution of engineering projects for industry and infrastructure. The Challenges Voltas has approached for revalidation\/validation of the data given by their […]<\/p>\n","protected":false},"author":1,"featured_media":804,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-146","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-case-studies"],"_links":{"self":[{"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/posts\/146","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/comments?post=146"}],"version-history":[{"count":13,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/posts\/146\/revisions"}],"predecessor-version":[{"id":723,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/posts\/146\/revisions\/723"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/media\/804"}],"wp:attachment":[{"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/media?parent=146"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/categories?post=146"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/tags?post=146"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}