Abstract

14.1 The consumer price index (CPI) represents a key indicator of economic performance in most countries, as described in Chapter 2. Where statistics are categorized according to their potential impact, the CPI is always prioritized. It follows therefore that it must be published, and otherwise disseminated, according to the policies, codes of practice, and standards set for such data. In addition to having information on price movement at the total level, users often require information on the weights, methodology, and price movements at a more disaggregated level.

Introduction

14.1 The consumer price index (CPI) represents a key indicator of economic performance in most countries, as described in Chapter 2. Where statistics are categorized according to their potential impact, the CPI is always prioritized. It follows therefore that it must be published, and otherwise disseminated, according to the policies, codes of practice, and standards set for such data. In addition to having information on price movement at the total level, users often require information on the weights, methodology, and price movements at a more disaggregated level.

14.2 The CPI should therefore be:

  • Released as soon as possible

  • Made available to all users at the same time (exceptions must be communicated in a transparent way; for example, if the central bank receives the results a few days before they are published on account of its monetary policy tasks, this should be mentioned in the press release)

  • Released according to preannounced release calendars

  • Released separately from ministerial comment

  • Made available in convenient form to users

  • Accompanied by methodological explanation

  • Backed up by CPI compilers and economists who can answer questions and provide further information

14.3 Above all, the CPI should comply with the Fundamental Principles of Official Statistics1 (United Nations [UN], A/RES/68/261 from 29 January 2014). These principles are published in several languages on the websites of the United Nations and the United Nations Economic Commission for Europe. They refer to dissemination and to all aspects of statistical work. These and other standards are discussed in paragraphs 14.40–14.42.

Time-Series Presentation of Level and Change

14.4 The presentation of the CPI data to users (for example, on press releases disseminated by the national statistical office [NSO]) commonly focuses on the percentage change over 12 months (price movement between the current month and the same month one year earlier). The 12-month comparison provides an indication of price changes over a reasonably long time frame, by reference to periods that may be expected to be similar year to year. Thus, seasonal factors are unlikely to be influential. It is also usual to compare this annual change with the annual change shown one month previously. The illustrative presentation in Figure 14.1 provides an example of this.

Figure 14.1
Figure 14.1

Example of an Illustrative CPI Press Release

14.5 The data release should also focus on the month-on-previous-month change or highlight quarter-on-quarter changes. These provide an indication of price change over a short time frame, which would highlight those products with volatile prices, such as fuels. To avoid any confusion in interpreting the results, it is very important to precisely specify to which period the published inflation relates.

14.6 The index reference period (a month or a longer period, as described in Chapter 8) refers to a period in the past where the index equals 100. All subsequent months/periods then have index numbers that reflect the relative change over time since the index reference period. Indeed, it is that index that is used as the basic figure from which the other changes are calculated. The index reference period generally coincides with a routine update, but some countries choose to retain the old index reference period when updating weights. When implementing changes to compilation methods, re-referencing (setting the index back to 100) signals to users that a new index has been published. The reference period should be defined in all publications and in the methodological explanation.

14.7 Indices and rates of change are usually shown only to one decimal place in the press release, and in this case, figures would be rounded. Rounding may lead to inconsistencies. For example, if the unrounded index of t - 1 is 101.1459, and the unrounded index of the following month t is 102.7591, the rate of change compiled with unrounded indices will be 1.6 percent while the rate of change compiled with rounded indices will be 1.7 percent. As long as this inconsistency can be explained (use full precision to calculate the rate of change), it is not a problem. An option could be to make available to users both rounded and unrounded figures: in the press release, the figures are rounded to one decimal, but national statistical offices (NSOs) often publish data on the website at or near full precision for analytical and research purposes. To avoid this inconsistency, it is also possible to compile the published rate of change with rounded indices to one or two decimals. This is the case for the European Union (EU) Harmonised Indices of Consumer Prices (HICPs) where the rate of change is compiled with two decimals rounded indices.

14.8 Care must be taken to differentiate between percentages and index points. If in one month the index is, for example, 200 and in the following month 201, then the change can be described as one index point (above the period when the index was set at 100) or as a half percentage point (where the previous month is taken as 100 percent). Both are valid, even if it is more common to refer to change as a percentage.

14.9 The CPI is, by definition, an index and therefore not a level or a series of absolute changes in prices. Nevertheless, in the process of compiling the CPI, average prices can be calculated for categories of products. It is thus possible to publish some average prices for groups of goods or services, and to show the upper and lower bands of the prices from which the averages have been calculated. These averages may be useful for some users, such as researchers. Average prices should only be published for tightly defined, homogeneous item groups that are relatively similar (in quality) and for which the variation coefficient is acceptable. It is also important to make it clear to users that average prices are a by-product of CPI compilation and are not used to calculate price changes.

14.10 The previous discussion refers not only to the all-items CPI, but also to a more disaggregated level such as regions of a country, population subgroups (for example, pensioners), or to related or alternative measures of price change. Related or alternative measures, and subaggregate indices, are discussed in paragraphs 14.23–14.34.

Seasonal Adjustment and Smoothing of the Index

14.11 The treatment of seasonal products and the estimation of seasonal effects are discussed in Chapter 11 of this Manual and Chapter 4 of the publication Consumer Price Index Theory. This chapter discusses the dissemination of adjusted or smoothed series.

14.12 Many series of economic statistics are disseminated seasonally adjusted, as well as unadjusted. CPIs, however, are not normally seasonally adjusted, although some countries do produce a seasonally adjusted CPI. Seasonally adjusted CPIs are purely analytical and do not replace the headline unadjusted index. Seasonal factors, for any series, are frequently recalculated using the most recent data, so seasonally adjusted series can be changed retrospectively, but unadjusted CPIs are not normally revised.

14.13 In comparing one month with the same month a year earlier, it is assumed that seasonal patterns are much the same from one year to the next. There may be, however, exceptional months when the usual seasonal pattern is advanced or delayed. Such exceptional circumstances should be noted in the monthly release as one of the likely causes of a change in the CPI or in one of its components. Even if seasonal patterns are much the same from one year to the next, there may be months when relevant calendar effects differ from one year to the next due to moving holidays such as Ramadan or Easter. Seasonal expenditure patterns can be observed around these holidays which could be considered seasonal in nature.

14.14 Changes over periods of less than a year are subject to seasonality. To differentiate seasonal patterns from other factors, it is necessary to make estimates of seasonal effects and to note them as factors that have contributed to changes in the index. For this purpose, it is necessary to clearly identify seasonal products. The NSO also may possibly calculate complementary indices, for example, a CPI that only contains seasonal products and a CPI without seasonal products.

14.15 Although the CPI is not usually seasonally adjusted, some variants of the CPI (for analytical purposes) may be seasonally adjusted. These variants should be explained to the users and can be revised in retrospect if necessary. Seasonal adjustment usually leads to smoother series than the original unadjusted series. There are other ways of smoothing monthly series, for example using three-month moving averages.

14.16 NSOs do not usually smooth the CPI series in their published presentations. In general, consumer price changes are not so erratic from month to month as to disguise price trends. If there is an erratic change, the producers of the index can usually explain the reasons for it. In any case, if seasonally adjusted or smoothed series are published, it is important to also publish the unadjusted series, so that the effect of the adjustment process is clear to users who may wish to know what has happened to prices and whether or not the changes can be ascribed to seasonal factors. Similarly, a full explanation should be given for the reasons why a particular seasonal adjustment procedure has been followed.

Analysis of Contributions to Change

14.17 Contributions to change help explain what groups of goods and services contributes most to inflation. These data are useful to better understand the sources of inflation and can increase transparency.

14.18 The CPI is an aggregate of many different goods and services with prices changing at different rates, some of which may be going up while others are going down. The weights of these products or groups of products are different, resulting in a varying impact on the all-items index. If the weight of a group of products is high and its price trend strong, the impact on the overall inflation rate will therefore also be high. Many users of the index want to know which goods or services have contributed most to changes in the total, and which prices may be out of step with general price trends.

14.19 The CPI compilers are well placed to provide analyses of the contributions to the overall price change, at the same time as the index is published. Sufficient detail should be made available so that users can understand what has happened to various groups of prices. In addition, to assist journalists and others working under time constraints, the CPI compiler should indicate the goods and services or group of products whose changes in price are the main contributors to the all-items CPI, and also goods and services whose changes in price are the most different from the aggregate. The statistics can be presented in the form of tables and charts so that the trends may be compared. Similarly, compilers should indicate any reasons for price changes that may not be immediately obvious but are nevertheless discernible from the published figures. For example, if there has been a sharp price rise or fall one year earlier, then it will affect the current year-on-year change, regardless of what is currently happening to prices.

14.20 The formulas used to calculate the contributions to change are described in Chapter 9.

Economic Commentary and Interpretation of the Index

14.21 In undertaking an analysis of the CPI results, CPI compilers must be objective so that users of the data may differentiate clearly between the figures and their interpretation. It is therefore essential that care is taken to avoid expressing any judgment of the impact of current policy on price changes or the possible implications of price changes for future policies. Whether the figures should be seen as good or bad news is for the users to decide for themselves. The NSO’s role is to provide objective information so that users can form their own judgment from the perspective of their own economic or political views.

14.22 There are several ways of avoiding any apparent or real lapses in the analysis. The first, and perhaps the most important, is to publish the figures independently of any ministerial or other political comment. Another is to be consistent in the way in which the figures are presented; that is, the data should be presented in much the same format every month (see paragraphs 14.35–14.39). For example, tables and charts should cover the same periods every month and use the same baselines.

Presentation of Related or Alternative Measures

Core Inflation

14.23 For the purpose of economic analysis, it is desirable to construct measures of “core” or “underlying” inflation that exclude movements in the inflation rate that are attributable to transient factors. In other words, measures of core or underlying inflation seek to measure the persistent or generalized trend of inflation. For example, central banks use measures of the general trend of inflation when setting monetary policy, and for this reason, economists and statisticians are increasingly interested in developing measures of underlying inflation.

14.24 Several methods can be used to derive a measure of core or underlying inflation. Most measures of underlying inflation focus on reducing or eliminating the influence of exceptionally volatile prices, or of exceptionally large individual price changes. The most traditional approach is to exclude particular components of the CPI on a discretionary basis. The items to be excluded would be based on the compiler’s knowledge of the volatility of particular items, depending on the country’s economic conditions. Items commonly excluded under this approach are fresh meat, fruit, and vegetables, and fuels and other energy products. Many countries also exclude imported goods, government charges, and government-controlled prices. In some countries, a calculation is made to exclude the effect of indirect taxes such as the value-added tax. Of course, care must be taken not to exclude so many items that the remainder becomes only a small and unrepresentative component of the total. The chosen method for producing underlying inflation should be described in the metadata and publication.

14.25 Other methods of deriving an underlying measure of inflation include smoothing techniques, for example, annualizing three-month average inflation. A more complex method is to exclude outliers (that is, those items with the highest or lowest increases).

Alternative and Subaggregate Indices

14.26 While publishing alternative aggregations of the CPI meets data user needs, this can also create confusion for other data users. Users can be confused over what is the headline, or official, rate of inflation and how these alternative measures compare to the headline index. NSOs must clearly explain the methods used and define the purpose of compiling these alternative indices. It must be clear to users how these indices can be used and why the NSO has published this alternative aggregation.

14.27 Countries commonly calculate price indices for hundreds of products (for example, bread or footwear), based on thousands of individual price records. The number of possible subaggregates is therefore very large. The choice of disseminated subaggregates is left to NSOs, according to the users’ needs.

14.28 One kind of subaggregation is the grouping of items or products that, when taken together, comprise the all-items index. An important consideration here is the relationship of the products within the subgroups. For example, an index may be presented for food and, under the heading of food, indices may be presented for subgroups such as cereals or vegetables.

14.29 Subaggregates from different divisions of the Classification of Individual Consumption According to Purpose (COICOP) can be combined to compile special aggregates. For example, a special index for education can be compiled using weights and indices from different groups. Tuition and fees are part of Education Services (Division 10 of COICOP 2018), while school uniforms are part of Clothing and Footwear (Division 03); textbooks and school supplies are part of Recreation, Sport, and Culture (Division 09); and school transport is under Transport (Division 07). An alternative index for education gives users a more complete picture of price change for education. Other examples would be to compile the CPI with and without production for own consumption in the weights. This analytical series meets the needs of poverty economists and analysts. Other examples include CPIs compiled by income group or for the elderly.

14.30 Other forms of subaggregate indices include the dissemination of regional indices. For those countries that compile a national index based on regional indices, the detailed regional indices should be disseminated. As with the national index, the monthly release should include data at a more aggregate level with detailed indices published on the NSO website. Data should be disseminated to the lowest level possible, ideally down to the elementary aggregate level.

14.31 One of the first considerations in presenting such subaggregate data for related products is consistency. There should be a set of subaggregates for which indices are calculated and presented each period. Users commonly attach great importance to being able to continue their analysis for the most recent period.

14.32 Another consideration is international standardization of the division of the index into groups of goods and services, which enables comparison between countries. Some countries also have their own subaggregate groupings which may precede the current international standard. The generally accepted international standard for the presentation of subaggregates is COICOP, as discussed in Chapter 2. It is used, for example, in the EU HICPs. Because COICOP defines groups of items by the general purpose for which they are used (for example, “transport” or “housing and household services”), it combines goods and services within the same subgroups. Where the national CPI is subaggregated by divisions other than the international standard, it is advisable either to present a breakdown also by COICOP or at least to show how the national classification compares to the international standard.

14.33 Another common type of subaggregate index is an index that excludes certain items. The core or underlying inflation index discussed in paragraphs 14.23–14.25 is an example. Some countries publish, in addition to the all-items CPI, an index or indices that exclude certain expenditures (for example, a CPI without fuels) or merge the products differently (for example, a CPI for durable goods or a CPI for public services).

14.34 In the presentation of all related or alternative measures, their definitions (for example, methodology or differences with the all-items CPI) should be made clear. It is also advisable to give the reasons for their publication. Most importantly, it should not be suggested that the subag-gregate index is more meaningful than the CPI.

Press Release, Bulletin, and Methodological Statement

14.35 The model presentation of a CPI in Figure 14.1 is an example of a press release for a fctitious country. Other formats are possible. For example, the presentation might include a seasonally adjusted index for analytical purposes. As indicated in the model, the presentation should contain the following information:

  • Details of issuing office

  • Date and time of release

  • Percentage change in current month over the same month one year earlier

  • Comparison with change in the previous month

  • Information on the product groups which contributed to the change and on any significant component price

  • Reference to where more information (for example, detailed results or metadata) can be found

  • Date for next release and link to advance release calendar

Note that no judgments are offered on policy or economic reasons for the price change, and no judgment is given on whether the change is good or bad.

14.36 The format of the press release should be the same from month to month. Using a consistent format is important to avoid appearing to choose a different format to indicate a preferred trend. Using the same format also allows for rationalization.

14.37 Additional content (in the paper or digital version available on the NSO website) of the press release should provide information on the indices from which the percentage changes are calculated. Similar indices should also be published for major groups of goods and services. Charts may also be used to illustrate, for example, which prices have contributed most or least to the all-items CPI.

14.38 If any other consumer price variant is also being published, then the differences between the indices should be briefly explained, including any methodological differences. Variants that require explanation include, for example, a national index based on the EU HICPs methodology, any regional indices, or versions of the CPI that exclude particular components of consumer expenditure. The press release should include a short note on methodology, similar to that given in Figure 14.2 or a link to the official website where the methodology is described. A more detailed explanation could be given in a handbook.

Figure 14.2
Figure 14.2

Model Note on Methodology—To Be Included in Press Releases on CPI or on the Official Website

14.39 Whether released in paper or digitally, the format of the press release remains the same. The only difference is the mode of dissemination. Countries continue to move to a digital format and disseminate the monthly press release in a digital format on the NSO website and via email to those requesting or subscribing to the monthly release. Some countries release the data electronically on the website, but also continue to officially release the data via a press conference using the traditional paper format.

International Standards Concerning the Dissemination of Consumer Price Indices

14.40 There are many international standards that apply, in general terms or specifically, to the CPI. The introduction to this chapter lists some of the broad principles that are reflected in many of the international standards in some form. One very general standard, but by its nature a fundamental one, is the UN Fundamental Principles of Official Statistics. It refers not just to dissemination but to all aspects of statistical work.

14.41 The International Monetary Fund (IMF) standards are particularly pertinent in regard to dissemination. As discussed in Chapter 13, two standards refer to statistics including the CPI, the Enhanced General Data Dissemination System (e-GDDS) and the Special Data Dissemination Standard (SDDS and SDDS Plus). The e-GDDS provides a general framework, with some specific indicators defined as “core” and others defined as “encouraged.” The Special Data Dissemination Standard is based on the GDDS framework but is more demanding and applies to countries that choose to subscribe to it in writing to the IMF Board. Detailed information on both standards is available on the IMF website.2

14.42 Under the heading of quality, the e-GDDS refers to the need to provide information on sources and methods, as well as on component details and checking procedures. Under the heading of integrity, it refers to declared standards of confidentiality, internal government access before data release, identification of ministerial commentary, and information on revision and advance notice of changes in methodology. Under the heading access by the public, it refers to the need for preannounced release dates and simultaneous access for all users. In the tables of data categories, it refers to the CPI as a core indicator that should be issued monthly, within one to two months of the reference period. All these standards are reflected in the present Manual. The International Labour Organization has also published Guidelines concerning dissemination practices for labor statistics (ILO 1998), which are available on the International Labour Organization website.3

Timing of Dissemination of the Consumer Price Index

14.43 The CPI should be released as soon as possible following the reference month, but it is equally important to release the index following a strict timetable. This timetable of release dates should be published as far in advance as possible. Having a fixed release date, published well in advance, is important for three main reasons. First, it reduces the scope for manipulation of the release date for political expediency. Second, it instills confidence in users that the release date is as soon as possible and has not been delayed (or brought forward) for purely political reasons. A third advantage is that users know when to expect the data and can be prepared to use them.

Timeliness of Release versus Data Accuracy

14.44 The IMF’s e-GDDS, discussed in paragraphs 14.41 and 14.42, recommends that the CPI be released each month within one to two months of the reference period. It is usual, in practice, for most countries to release the CPI in the middle of the month after the month to which the index refers. This is possible because, in many cases, the data are collected mainly over a limited period in the middle of the month to which the latest data refer. Thus, the CPI compilers have some time to check and analyze the data and to prepare the tables and charts in which the data will be disseminated.

14.45 The accuracy of the index is particularly relevant because of the importance of the CPI, as discussed in more detail in Chapter 2. Also, partly because data are collected according to a strict schedule by price collectors it is rare for data to be reported after the CPI is published, and partly because of the way in which the index is used in contracts, it is very rarely revised. This represents a major difference between the CPI and other economic or socioeconomic statistics.

14.46 It follows that, although timeliness is important, the dissemination timetable must allow time for the data to be properly prepared and thoroughly checked. After the release date, in most cases, a revision to the CPI would not be permissible, except in the case of a seasonally adjusted CPI. The HICPs of the EU are an exception as they are revised from time to time. If any series is revised, then of course the changes must be fully described and explained when the new data are released. If there is any methodological change, this is usually known in advance. Users should be informed before any such change occurs.

14.47 Best practice suggests that NSOs develop a revision policy for the CPI. Should an error be discovered that exceeds a defined threshold, the CPI would be revised. This revision policy allows for the correction of errors and enhances transparency. Users should be made aware of the revision policy in the metadata.

14.48 A possible compromise between accuracy and timeliness can be the publication of flash estimates. A flash estimate is an early estimate of inflation computed from preliminary data at a given time, released at the end of the current month or at the very beginning of the following month, giving users a provisional figure very quickly. A flash estimate is always followed by the official publication of the results, once the data are complete and all controls and analyses have been carried out. For example, Eurostat publishes flash estimates for the euro area.

Access to Data

14.49 For a number of countries, the internet has become the main dissemination medium, usually via the NSO website. For the data producer, distribution costs are relatively small. No printing or mailing costs are involved. As soon as the data are disseminated online, they are available to all users simultaneously. Disseminating a large amount of data

on the NSO website costs little more than disseminating a smaller amount.

14.50 Ideally, the CPI, accompanied by any essential metadata, should be released simultaneously to the press and other users. One way in which some NSOs are doing this is by making the press release available confidentially to the journalists shortly before the official release time (maybe half an hour), providing them with the printed press release. Then, when the data are released, the journalists are permitted to release their reports or stories to the public. Care must be taken to ensure that the CPI is available to all users at the same time, regardless of the dissemination medium used.

14.51 With the CPI as with other statistics, users should be allowed access to as much data as possible for two main reasons. First, some users find the detailed data very useful in their analysis. Second, access to the data inspires confidence in the data.

14.52 Data should be disseminated to the lowest level possible, ideally the elementary aggregate level. Whether to publish a particular elementary aggregate may depend upon confidentiality issues, as addressed in paragraph 14.55.

14.53 In general, the CPI and its major components are deemed to be of such wide importance that they are made available for free through press releases and on the NSO website. While the goal should be to meet data user needs, special analyses made at the request of particular users may incur costs outside of the normal monthly production and processing budget. Some countries will charge for special analysis requests to defray the additional cost of preparing the data.

Confidentiality

14.54 Although, in general, as much data as possible should be made available to users, there are reasons why confidentiality is important in some instances. First, some data are supplied by retailers and others on the understanding that the data will be used only for the purpose of aggregation with other data and will not be released in any other form. This can be especially important where the data are given voluntarily, as they often are. For example, in the case when a single respondent provides data for a given elementary aggregate, publishing data at the elementary aggregate level would identify the data provider. To avoid any issues, some countries will obtain written permission from the respondent authorizing the dissemination of the elementary aggregate even though such publication would reveal the identity of the data provider. Second, some elementary aggregates may be compiled based on a small number of prices and could be deemed as not being sufficiently representative for publication purposes. Samples should be selected in such a way as to support the dissemination of all elementary aggregates.

Presentation of Methodology

14.55 When the CPI is published each month, users want to see the main figures and to use them. Users do not generally want to be burdened with explanations concerning the methodology underlying the data. Nevertheless, methodological explanations must be accessible to those who may want them, and in forms which are comprehensible to users with different levels of expertise and interest. Any significant changes in methodology must be fully explained and notified as far in advance as possible of the change being made.

14.56 In addition to a brief statement in press releases (see paragraphs 14.35–14.39), methodological explanations should be available on at least two levels. Nonexperts should be able to refer to a booklet that explains the history, principles, and practice underlying the CPI and any alternative measures which may also be available. A more thorough explanation of sources and methods should also be readily available for those users who are sufficiently interested and, for example, for compilers who may be working on the production of the CPI for the first time. The information must also be kept up to date despite the pressures to devote time to the output at the expense of documentation. The ready availability of a full explanation of sources and methods is essential to confidence and trust in the CPI.

14.57 Developing frequently asked questions (FAQs) provides another level of information on the methodology for users. FAQs provide concise responses to explain key methodological questions and define important concepts. Overly technical language and explanations should be avoided. FAQs should be written in plain language understandable by all users. FAQs should be posted on the NSO website and can be used to summarize index compilation methods at the end of a press release.

Explaining Index Quality

14.58 Some users may regard the CPI results with suspicion, as noted in Chapter 2. Metadata usually refers to the “average consumer” or “average household,” but each consumer and household has different expenditure patterns from the expenditure patterns of others and may notice changes in one set of prices but not in others. More importantly, perhaps, there is criticism of the index because of suspicion that it does not keep track of newer types of goods and services, changes in the quality of products, or newer types of retail outlets. See Chapter 2, paragraphs 2.47–2.49, for more details on inflation perceptions.

14.59 It is important for CPI compilers to be willing to discuss these issues and to explain how the compilation methods used address these issues. The producers of the index must be open about their methods and the extent to which they can overcome the potential or real problems that have been identified. It follows that the NSO should publish explanations concerning the quality aspects, whether or not the quality of the index is currently being questioned.

14.60 Some countries develop personal inflation calculators that make data more relevant to individual users. A personal inflation calculator allows a user to define their own individual basket. Users are asked either to input expenditure details for a group of items (monthly, annually, etc.) or to define specific items and the proportion of expenditure made on each item. Based on these inputs, a personal inflation rate is compiled and presented.4

User Consultation

Different Uses of Consumer Price Indices

14.61 It is important to explain the different uses of the CPI to potential data users (CPI uses are discussed in Chapter 2). To this end, it is important to explain how the CPI is constructed and to provide details of its sources and methods. It is also important to make readily available explanations of alternative indices or subindices, indicating how their uses differ from the uses of the CPI.

14.62 If there are different uses for CPIs, there are also different users. It is useful to identify the different users to provide them the relevant information. The basic user would be interested in knowing general results on an occasional basis, while the central bank or an academic would be interested in detailed results over a longer period. The identification and classification of CPI users are useful to better respond to their expectations.

Role of Advisory Committees

14.63 For a statistical series as important as the CPI, it is essential to organize an advisory committee, or a set of committees, representing users and producers. There are many contentious issues in the construction of the CPI. In many countries, there have been fierce arguments about, for example, which components should be included and excluded. The role of an advisory committee is to consider and advise on best practice and methodologies. An equally important role of an advisory committee is to enhance the credibility of the CPI.

14.64 In those countries where advisory committees have not been the norm, there may be a fear on the part of the CPI compiler that including nongovernmental participants may raise expectations beyond what the NSO can deliver, thereby increasing dissatisfaction among the general public. In fact, the inclusion of nongovernmental users can lead to a greater understanding of the realities and the practical constraints to meeting theoretical needs. This is the usual experience of NSOs that already have advisory bodies that include representatives of all the major constituencies, both inside and outside government. It is therefore important that the advisory committee should include academics, employers, trade union representatives, and others who have an interest in the index from differing points of view. It is also important that the reports of the advisory committee are made available to the public in full and without undue delay.

Key Recommendations

  • NSOs should disseminate two key measures—the 12-month (current month to the same month in the previous year) and the month-on-month change (current to the previous month).

  • Disseminate contributions to change so users better understand what items have contributed most to inflation in a given period.

  • Written analysis or commentary should be neutral and focus only on describing important or unusual movements in a given period.

  • Provide alternative aggregations of CPI data to better meet data user needs.

  • Develop and disseminate detailed metadata describing CPI compilation methods. The release should include a short methodology note, and a more detailed description should be available on the NSO website.

  • A calendar of release dates should be disseminated at least one year in advance. Once the release date has been established, data should be released without delay.

  • CPI data should be released simultaneously to all users.

  • Detailed data should be disseminated on the NSO website. These include detailed weights, indices, and changes (monthly and annual).

  • Data should be published down to the elementary aggregate level. Every effort should be made to design samples that support the detailed dissemination of data.

  • To enhance transparency and increase user confidence, NSOs should explain data quality issues and provide details on the methods used to minimize these biases.

  • User groups should be consulted and informed when making updates or revisions to the CPI. Advisory groups can be a useful means of maintaining contact with key data users.

Author: Brian Graf
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    Example of an Illustrative CPI Press Release

  • View in gallery

    Model Note on Methodology—To Be Included in Press Releases on CPI or on the Official Website

  • Abe, N., and K. Shinozaki. 2018. “Compilation of Experimental Price Indexes Using Big Data and Machine Learning: A Comparative Analysis and Validity Verification.” Paper presented at the UNECE/ILO Meeting of the Group of Experts on Consumer Price Indices, Geneva, May 7–9. https://www.unece.org.

    • Search Google Scholar
    • Export Citation
  • Abraham, K. G., J. S. Greenlees, and B. R. Moulton. 1998. “Working to Improve the Consumer Price Index.” Journal of Economic Perspectives 12 (1): 2736.

    • Search Google Scholar
    • Export Citation
  • Aczél, J. 1987. A Short Course on Functional Equations. Dordrecht: Reidel Publishing Co.

  • Advisory Commission to Study the Consumer Price Index. 1995. Toward a More Accurate Measure of the Cost of Living. Interim Report to the Senate Finance Committee, September 15. Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Aizcorbe, A. M., and P. C. Jackman. 1993. “The Commodity Substitution Effect in CPI Data, 1982–91.” Monthly Labor Review 116 (12): 2533.

    • Search Google Scholar
    • Export Citation
  • Aizcorbe, A., C. Corrado, and M. Doms. 2001. Constructing Price and Quantity Indexes for High Technology Goods. Industrial Output Section, Division of Research and Statistics. Washington, DC: Board of Governors of the Federal Reserve System.

    • Search Google Scholar
    • Export Citation
  • Aizcorbe, A., C. Corrado, and M. Doms. 2003. “When Do Matched-Model and Hedonic Techniques Yield Similar Price Measures?Working Paper 2003–14, Federal Reserve Bank of San Francisco, San Francisco, CA.

    • Search Google Scholar
    • Export Citation
  • Alterman, W. F., W. E. Diewert, and R. C. Feenstra. 1999. International Trade Price Indexes and Seasonal Commodities. Washington, DC: Bureau of Labor Statistics.

    • Search Google Scholar
    • Export Citation
  • Anderson, R. G., B. E. Jones, and T. Nesmith. 1997. “Building New Monetary Services Indexes: Concepts, Data and Methods.” Federal Reserve Bank of St. Louis Review 79 (1): 5383.

    • Search Google Scholar
    • Export Citation
  • Arguea, N. M., C. Haseo, and G. A. Taylor. 1994. “Estimating Consumer Preferences Using Market Data: An Application to U.S. Automobile Demand.” Journal of Applied Econometrics 9: 118.

    • Search Google Scholar
    • Export Citation
  • Armknecht, P. A. 1996. “Improving the Efficiency of the U.S. CPI.” Working Paper No. 96/103, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Armknecht, P. A. 2016. “Fixed Basket Methods for Compiling Consumer Price Indexes.” Meeting of the Group of Experts on Consumer Price Indices, Geneva, Switzerland, May 2–4, 2016. http://www.unece.org/index.php.

    • Search Google Scholar
    • Export Citation
  • Armknecht, P. A., and D. Weyback. 1989. “Adjustments for Quality Change in the U.S. Consumer Price Index.” Journal of Official Statistics 5 (2): 10723.

    • Search Google Scholar
    • Export Citation
  • Armknecht, P. A., and F. Maitland-Smith. 1999. “Price Imputation and Other Techniques for Dealing with Missing Observations, Seasonality and Quality Change in Price Indices.” Working Paper No. 99/78, International Monetary Fund, Washington, DC. http://www.imf.org/external/pubs/ft/wp/1999/wp9978.pdf

    • Search Google Scholar
    • Export Citation
  • Armknecht, P. A., and M. Silver. 2014. “Post-Laspeyres: The Case for a New Formula for Compiling Consumer Price Indexes.” The Review of Income and Wealth 60 (2): 22544.

    • Search Google Scholar
    • Export Citation
  • Armknecht, P. A., W. F. Lane, and K. J. Stewart. (1997) 2003. “New Products and the U.S. Consumer Price Index.” In Scanner Data and Price Indexes, NBER Studies in Income and Wealth, edited by R. C. Feenstra and M. D. Shapiro. Chicago, IL: University of Chicago Press, pp. 37591.

    • Search Google Scholar
    • Export Citation
  • Arrow, K. J., H. B. Chenery, B. S. Minhas, and R. M. Solow. 1961. “Capital–Labor Substitution and Economic Efficiency.” Review of Economics and Statistics 63: 22550.

    • Search Google Scholar
    • Export Citation
  • Astin, J. 1999. “The European Union Harmonized Indices of Consumer Prices (HICP).” in Proceedings of the Ottawa Group Fifth Meeting, Reykjavik, Iceland, August 25–27, edited by R. Gudnason and T. Gylfadottir. Reykjavík: Statistics Iceland; also published in Statistical Journal of the United Nations ECE16: 12335. http://www.statcan.ca/secure/english/ottawagroup/.

    • Search Google Scholar
    • Export Citation
  • Astin, J. 2001. “New Uses of PPPs within the European Union.” Paper presented at the Joint World Bank–OECD Seminar on Purchasing Power Parities: Recent Advances in Methods and Applications, Washington, DC, January 30–February 2.

    • Search Google Scholar
    • Export Citation
  • Aten, B., R. Summers, and A. Heston. 2001. “An Explanation of Stability in Country Price Structures: Implications for Spatial– Temporal Comparisons.” Paper presented at the Joint World Bank–OECD Seminar on Purchasing Power Parities: Recent Advances in Methods and Applications, Washington, DC, January 30–February 2.

    • Search Google Scholar
    • Export Citation
  • Australian Bureau of Statistics. 1997. “An Analytical Framework for Price Indexes in Australia.” Information Paper, Catalogue No. 6421.0. http://www.abs.gov.au.

    • Search Google Scholar
    • Export Citation
  • Australian Bureau of Statistics. 2003. “Australian Consumer Price Index: Concepts, Sources and Methods.” Catalogue No. 6461.0. http://www.abs.gov.au/.

    • Search Google Scholar
    • Export Citation
  • Australian Bureau of Statistics. 2009. “Consumer Price Index: Concepts, Sources and Methods.” Catalogue No. 6461.0, ABS, Canberra. http://www.abs.gov.au/ausstats/abs@.nsf/mf/6461.0.

    • Search Google Scholar
    • Export Citation
  • Australian Bureau of Statistics. 2013. “Feature Article: The Use of Transactions Data to Compile the Australian Consumer Price Index.” Catalogue No. 6401.0, ABS, Canberra. http://www.abs.gov.au/.

    • Search Google Scholar
    • Export Citation
  • Australian Bureau of Statistics. 2015. “Enhancing the Australian CPI: A Roadmap.” Catalogue No. 6401.0.60.001, ABS, Canberra. http://www.abs.gov.au/.

    • Search Google Scholar
    • Export Citation
  • Australian Bureau of Statistics. 2016a. “Making Greater Use of Transactions Data to Compile the Consumer Price Index.” Information Paper 6401.0.60.003, ABS, Canberra. http://www.abs.gov.au/.

    • Search Google Scholar
    • Export Citation
  • Australian Bureau of Statistics. 2016b. “Measuring Price Change of Attached Dwellings in the CPI.” Feature Article, ABS, Canberra. http://www.abs.gov.au/.

    • Search Google Scholar
    • Export Citation
  • Australian Bureau of Statistics. 2017a. “An Implementation Plan to Maximise the Use of Transactions Data in the CPI.” Information Paper 6401.0.60.004, ABS, Canberra. http://www.abs.gov.au/.

    • Search Google Scholar
    • Export Citation
  • Australian Bureau of Statistics. 2017b. “Introduction of the 17th Series Australian Consumer Price Index.” Information Paper 6470.0.55.001, ABS, Canberra. http://www.abs.gov.au/AUS-STATS/abs@.nsf/DetailsPage/6470.0.55.0012017.

    • Search Google Scholar
    • Export Citation
  • Bäckström, Peter, and Muhanad Sammar. 2012. “The Use of Superlative Index Links in the Swedish CPI.” Paper presented at the meeting of the Group of Experts on CPI, Geneva. https://www.unece.org/index.php.

    • Search Google Scholar
    • Export Citation
  • Bailey, M. J., R. F. Muth, and H. O. Nourse. 1963. “A Regression Method for Real Estate Price Construction.” Journal of the American Statistical Association 58: 93342.

    • Search Google Scholar
    • Export Citation
  • Baker, D. 1998. “Does the CPI Overstate Inflation? An Analysis of the Boskin Commission Report.” In Getting Prices Right, edited by D. Baker. Washington, DC: Economic Policy Institute, pp. 79155.

    • Search Google Scholar
    • Export Citation
  • Baldwin, A. 1990. “Seasonal Baskets in Consumer Price Indexes.” Journal of Official Statistics 6 (3): 25173.

  • Baldwin, A., A. Nakamura, and M. Prudhomme. 2010. “An Empirical Analysis of the Different Concepts for Owned Accommodation in the Canadian CPI: The Case of Ottawa, 1996–2005.” Paper presented at the UNECE/ILO Meeting of the Group of Experts on Consumer Price Indices, Geneva, May 10–12. https://www.unece.org.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 1980a. “Seasonal Products in Agriculture and Horticulture and Methods for Computing Price Indices.” Statistical Studies No. 24. The Hague: Netherlands Central Bureau of Statistics.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 1980b. “Seasonal Commodities and the Construction of Annual and Monthly Price Indexes.” Statistische Hefte 21: 1106.

  • Balk, B. M. 1980c. “A Method for Constructing Price Indices for Seasonal Commodities.” The Journal of the Royal Statistical Society Series A 143: 6875.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 1981. “A Simple Method for Constructing Price Indices for Seasonal Commodities.” Statistische Hefte 22: 728.

  • Balk, B. M. 1983. “Does There Exist a Relation between Inflation and Relative Price Change Variability? The Effect of the Aggregation Level.” Economic Letters 13: 173180.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 1985. “A Simple Characterization of Fisher’s Price Index.” Statistische Hefte 26: 5963.

  • Balk, B. M. 1989a. “Changing Consumer Preferences and the Cost of Living Index: Theory and Nonparametric Expressions.” Zeitschrift für Nationalökonomie 50 (2): 15769.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 1989b. “On Calculating the Precision of Consumer Price Indices.” Contributed Papers 47th Session of the ISI. Paris: International Statistical Institute.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 1990. “On Calculating Cost-of-Living Index Numbers for Arbitrary Income Levels.” Econometrica 58 (1): 7592.

  • Balk, B. M. 1994. “On the First Step in the Calculation of a Consumer Price Index.” Paper presented at First Meeting of the International Working Group on Price Indices, Ottawa, October 31–November 4. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 1995. “Axiomatic Price Index Theory: A Survey.” International Statistical Review 63: 6993.

  • Balk, B. M. 1996a. “A Comparison of Then Methods for Multilateral International Price and Volume Comparisons.” Journal of Official Statistics 12: 199222.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 1996b. “Consistency in Aggregation and Stuvel Indices.” The Review of Income and Wealth 42: 35363.

  • Balk, B. M. 1998a. Industrial Price, Quantity and Productivity Indices. Boston, MA: Kluwer Academic Publishers.

  • Balk, B. M. 1998b. “On the Use of Unit Value Indices as Consumer Price Subindices.” Paper presented at the Fourth Meeting of the International Working Group on Price Indices, Washington, DC, April 22–24. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 2000a. “Divisia Price and Quantity Indexes 75 Years After.” Draft Paper, Department of Statistical Methods, Statistics Netherlands, Voorburg.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 2000b. “On Curing the CPI’s Substitution and New Goods Bias.” Research Paper 0005, Department of Statistical Methods, Statistics Netherlands, Voorburg.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 2001. Aggregation Methods in International Comparisons: What Have We Learned? Report Series Research in Management ERS-2001–41-MKT, Erasmus Research Institute of Management. Rotterdam: Erasmus University.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 2005. “Price Indexes for Elementary Aggregates: The Sampling Approach.” Journal of Official Statistics 21: 67599.

  • Balk, B. M. 2008. Price and Quantity Index Numbers. Cambridge: University Press.

  • Balk, B. M. 2008. Price and Quantity Index Numbers: Models for Measuring Aggregate Change and Difference. New York: Cambridge University Press.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M. 2010. “Lowe and Cobb-Douglas Consumer Price Indexes and Their Substitution Bias.” Jahrbücher für Nation-alökonomie und Statistik (The Journal of Economics and Statistics) 230 (6): 72640.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M., and H. M. P. Kersten. 1986. “On the Precision of Consumer Price Indices Caused by the Sampling Variability of Budget Surveys.” Journal of Economic and Social Measurement 14: 1935.

    • Search Google Scholar
    • Export Citation
  • Balk, B. M., and W. E. Diewert. 2001. “A Characterization of the Törnqvist Price Index.” Economics Letters 73: 27981.

  • Ball, A., and D. Fenwick. 2004. “Costing Mobile Telephone Calls: The Use of Constrained User Profiles.” Paper presented at the Eighth Meeting of the International Working Group on Price Indices, Helsinki, August 23–25. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Bartik, T. J. 1988. “Measuring the Benefits of Land Improvements in Hedonic Models.” Land Economics 64 (2): 17283.

  • Bas, C. 2018. “Different Approaches and Methods for Strong Seasonal Items in the CPI.” Paper presented at the UNECE/ILO Meeting of the Group of Experts on Consumer Price Indices, Geneva, May 7–9. https://www.unece.org.

    • Search Google Scholar
    • Export Citation
  • Bascher, J., and T. Lacroix. 1999. “Dishwashers and PCs in the French CPI: Hedonic Modeling, from Design to Practice.” Paper presented at the Fifth Meeting of the International Working Group on Price Indices, Reykjavik, August 25–27. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Baxter, M. (ed.). 1998. The Retail Prices Index. Technical Manual. London: Office for National Statistics, UK.

  • Bean, L. H., and O. C. Stine. 1924. “Four Types of Index Numbers of Farm Prices.” Journal of the American Statistical Association 19: 305.

    • Search Google Scholar
    • Export Citation
  • Becker, G. S. 1965. “A Theory of the Allocation of Time.” Economic Journal 75: 493517.

  • Beck-MacNeil, R. 2018. “Pricing the Internet—The Internet Access Services Component of the Canadian CPI.” Statistics Canada. Paper presented at the UNECE/ILO Meeting of the Group of Experts on Consumer Price Indices, Geneva, May 7–9. https://www.unece.org.

    • Search Google Scholar
    • Export Citation
  • Beidelman, C. 1973. Valuation of Used Capital Assets. Sarasota, FL: American Accounting Association.

  • Beidelman, C. 1976. “Economic Depreciation in a Capital Goods Industry.” National Tax Journal 29: 37990.

  • Beisteiner, A. 2008. “Optimal Allocation of Prices in Practice— Using the Neumann Formula as Tool.” Paper presented at the UNECE/ILO Meeting of the Group of Experts on Consumer Price Indices, Geneva, May 8–9. https://www.unece.org.

    • Search Google Scholar
    • Export Citation
  • Bentley, A., and F. Krsinich. 2017. “Towards a Big Data CPI for New Zealand.” Paper presented at the Fifteen Meeting of the International Working Group on Price Indices, Eltville am Rhein, May 10–12. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Bernardini, A., C. De Vitiis, A. Guandalini, F. Inglese, and M. D. Terribili. 2016. “Measuring Inflation Through Different Sampling Designs Implemented on Scanner Data.” Paper presented at Meeting of the Group of Experts on Consumer Price Indices, Geneva, Switzerland, May 2–4.

    • Search Google Scholar
    • Export Citation
  • Berndt, E. R. 1991. The Practice of Econometrics: Classic and Contemporary. Reading, MA: Addison-Wesley.

  • Berndt, E. R., D. Ling, and M. K. Kyle. 2003. “The Long Shadow of Patent Expiration: Generic Entry and Rx to OTC Switches.” In Scanner Data and Price Indexes, NBER Studies in Income and Wealth, edited by M. Shapiro and R. C. Feenstra. Chicago, IL: University of Chicago Press, pp. 22973.

    • Search Google Scholar
    • Export Citation
  • Berndt, E. R., L. T. Bui, D. H. Lucking-Reiley, and G. L. Urban. 1997. “The Roles of Marketing, Product Quality and Price Competition in the Growth and Composition of the U.S. Anti-Ulcer Drug Industry.” In The Economics of New Goods, NBER Studies in Income and Wealth, edited by T. Bresnahan and R. J. Gordon. Chicago and London: University of Chicago Press, pp. 23277.

    • Search Google Scholar
    • Export Citation
  • Berndt, E. R., Z. Griliches, and N. J. Rappaport. 1995. “Econometric Estimates of Price Indexes for Personal Computers in the 1990s.” Journal of Econometrics 68: 24368.

    • Search Google Scholar
    • Export Citation
  • Berry, S., J. Levinsohn, and A. Pakes. 1995. “Automobile Prices in Market Equilibrium.” Econometrica 63 (4): 841–90; also published as NBER Working Paper No. W4264, July 1996. http://www.nber.org.

    • Search Google Scholar
    • Export Citation
  • Beuerlein, I. 2001. “The German Consumer Price Index for Telecommunication Services: A User Profile Approach for Mobile Technology and Internet Access.” Paper presented at the Sixth Meeting of the International Working Group on Price Indices, Canberra, April 2–6. http://www.ottawagroup.org/.

    • Search Google Scholar
    • Export Citation
  • Bhardwaj, H., T. Flower, P. Lee, and M. Mayhew. 2017. Research Indices Using Web Scraped Price Data. August 2017 Update, UK Office for National Statistics, London.

    • Search Google Scholar
    • Export Citation
  • Bird, D., R. Breton, C. Payne, and A. Restieaux. 2014. Initial Report on Experiences with Scanner Data in ONS. Report, Office for National Statistics, Newport, UK.

    • Search Google Scholar
    • Export Citation
  • Bode, B., and van J. Dalén. 2001. “Quality-Corrected Price Indexes of New Passenger Cars in the Netherlands, 1990–1999.” Paper presented at the Sixth Meeting of the International Working Group on Price Indices, Canberra, April 2–6. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Boettcher, I. 2015. “Automatic Data Collection on the Internet (Web Scraping).” Statistic Austria. Paper presented at the Fourteen Meeting of the International Working Group on Price Indices, Tokyo, May 20–22. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Böhm-Bawerk, E. V. 1891. The Positive Theory of Capital. Translated from the original German edition of 1888 by W. Smart. New York: G. E. Stechert.

    • Search Google Scholar
    • Export Citation
  • Bortkiewicz, L. V. 1923. “Zweck und Struktur einer Preisin-dexzahl.” Nordisk Statistisk Tidsskrift 2: 369408.

  • Boskin, M. J. (Chair), E. R. Dullberger, R. J. Gordon, Z. Griliches, and D. W. Jorgenson. 1996. Final Report of the Commission to Study the Consumer Price Index, U.S. Senate, Committee on Finance. Washington, DC: U.S. Government Printing Office.

    • Search Google Scholar
    • Export Citation
  • Boskin, M. J. (Chair), E. R. Dullberger, R. J. Gordon, Z. Griliches, and D. W. Jorgenson. 1998. “Consumer Prices in the Consumer Price Index and the Cost of Living.” Journal of Economic Perspectives 12 (1): 326.

    • Search Google Scholar
    • Export Citation
  • Böttcher, I., and S. Sergeev. 2014. “Austrian Scanner Data Project—Report Multipurpose Consumer Price Statistics: The Use of Scanner Data.” Paper presented at the UNECE/ILO Meeting of the Group of Experts on Consumer Price Indices, Geneva, May 26–8. https://www.unece.org.

    • Search Google Scholar
    • Export Citation
  • Bowley, A. L. 1899. “Wages, Nominal and Real.” In Dictionary of Political Economy, Volume 3, edited by R. H. I. Palgrave. London: Macmillan, pp. 64051.

    • Search Google Scholar
    • Export Citation
  • Bowley, A. L. 1901. Elements of Statistics. Westminster: Orchard House.

  • Bowley, A. L. 1919. “The Measurement of Changes in the Cost of Living.” Journal of the Royal Statistical Society 82: 34361.

  • Bradley, R. 1995. “The Use of Scanner Data as a Means to Reduce the Mean Squared Error in the CPI.” Working paper, U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Bradley, R. 2007. “Analytical Bias Reduction for Small Samples in the U.S. Consumer Price Index.” Journal of Business & Economic Statistics 25: 33746.

    • Search Google Scholar
    • Export Citation
  • Bradley, R., B. Cook, S. E. Leaver, and B. R. Moulton. 1997. “An Overview of Research on Potential Uses of Scanner Data in the U.S. CPI.” Paper presented at the Third Meeting of the International Working Group on Price Indices, Voorburg, April 16–18. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Braithwait, S. D. 1980. “The Substitution Bias of the Laspeyres Price Index: An Analysis Using Estimated Cost-of-Living Indexes.” American Economic Review 70 (1): 6477.

    • Search Google Scholar
    • Export Citation
  • Brand, M., C. Vermeulen, D. Fischbach, and Y. Carpy. 2017. “Swiss Residential Property Price Index: The Use of Geolocal-ised Information for Quality Adjustment in Location.” Paper presented at the Fifteenth Meeting of the International Working Group on Price Indices, Eltville, May 10–12. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Bresnahan, T. F. 1997. “Comment.” In The Economics of New Goods, NBER Studies in Income and Wealth, edited by T. F. Bresnahan and R. J. Gordon. Chicago, IL: University of Chicago Press, pp. 23747.

    • Search Google Scholar
    • Export Citation
  • Breton, R., G. Clews, L. Metcalfe, N. Milliken, C. Payne, J. Winton, and A. Woods. 2016. “Research Indices Using Web Scraped Data.” Paper presented at the UNECE/ILO Meeting of the Group of Experts on Consumer Price Indices, Geneva, May 2–4. https://www.unece.org.

    • Search Google Scholar
    • Export Citation
  • Brown, C., and A. Stockburger. 2006. “Item Replacement and Quality Change in Apparel Price Indexes.” Monthly Labor Review (December): 3545. https://www.bls.gov/opub/mlr/2006/12/art3full.pdf.

    • Search Google Scholar
    • Export Citation
  • Burnett-Isaacs, K. 2015. “Using Hedonics to Create Land and Structure Indexes for the Ottawa Condominium Market.” Paper presented at the Fourteen Meeting of the International Working Group on Price Indices, Tokyo, May 20–22. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Cage, R., J. S. Greenlees, and P. Jackman. 2003. “Introducing the Chained Consumer Price Index.” In International Working Group on Price Indices (Ottawa Group): Proceedings of the Seventh Meeting, edited by Thierry Lacroix. Paris: INSEE, pp. 21346.

    • Search Google Scholar
    • Export Citation
  • Canning, J. B. 1929. The Economics of Accountancy. New York: The Ronald Press Co.

  • Carli, G.-R. 1804. “Del valore e della proporzione dei metallic monetati.” In Scrittori classici italiani di economia politica, Vol. 13. Milano: G. G. Destefanis, pp. 297366; originally published in 1764.

    • Search Google Scholar
    • Export Citation
  • Carruthers, A. G., D. J. Sellwood, and P. W. Ward. 1980. “Recent Developments in the Retail Prices Index.” The Statistician 29: 132.

    • Search Google Scholar
    • Export Citation
  • Cassel, E., and R. Mendelsohn. 1985. “On the Choice of Functional Forms for Hedonic Price Equations: Comment.” Journal of Urban Economics 18 (September): 13542.

    • Search Google Scholar
    • Export Citation
  • Castles, I. 1997. The OECD–EUROSTAT PPP Program: Review of Practice and Procedures. Paris: OECD.

  • Cavallo, A. 2016. “Are Online and Offine Prices Similar? Evidence from Large Multi-Channel Retailers.” Massachusetts Institute of Technology. Paper presented at the UNECE/ILO Meeting of the Group of Experts on Consumer Price Indices, Geneva, May 2–4. https://www.unece.org.

    • Search Google Scholar
    • Export Citation
  • Cavallo, A., and R. Rigobon. 2016. “The Billion Dollar Project. Using Online Data for Measurement and Research.” MIT & NBER. Paper presented at the UNECE/ILO Meeting of the Group of Experts on Consumer Price Indices, Geneva, May 2–4. https://www.unece.org.

    • Search Google Scholar
    • Export Citation
  • Cavallo, A., and R. Rigobon. 2016. “The Billion Prices Project: Using Online Prices for Measurement and Research.” Journal of Economic Perspectives 30 (2): 15178.

    • Search Google Scholar
    • Export Citation
  • Caves, D. W., L. R. Christensen, and W. E. Diewert. 1982a. “The Economic Theory of Index Numbers and the Measurement of Input, Output and Productivity.” Econometrica 50: 1393414.

    • Search Google Scholar
    • Export Citation
  • Caves, D. W., L. R. Christensen, and W. E. Diewert. 1982b. “Multilateral Comparisons of Output, Input and Productivity Using Superlative Index Numbers.” Economic Journal 92: 7386.

    • Search Google Scholar
    • Export Citation
  • Cecchetti, S. G. 1997. “Measuring Inflation for Central Bankers.” Federal Reserve Bank of St. Louis Review 79: 14355.

  • Chessa, A. G. 2017. “Comparisons of QU-GK Indices for Different Lengths of the Time Window and Updating Methods.” Paper prepared for the Second Meeting on Multilateral Methods Organised by Eurostat, Luxembourg, March 14–15, Statistics Netherlands.

    • Search Google Scholar
    • Export Citation
  • Chessa, A. G. 2020. “A Comparison of Multilateral Index Extension Methods on Transaction Data of Seasonal Items.” Paper presented at the meeting of the Ottawa Group, Rio de Janeiro, May 8–10, 2019.

    • Search Google Scholar
    • Export Citation
  • Chessa, A. G., and R. Griffoen. 2016. “A New Methodology for Processing Scanner Data in the Dutch CPI.” Eurona 1: 4969.

  • Chessa, A. G., J. Verburg, and L. Willenborg. 2017. “A Comparison of Price Index Methods for Scanner Data.” Paper presented at the Fifteen Meeting of the International Working Group on Price Indices, Eltville am Rhein, May 10–12. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Christensen, L. R., and D. W. Jorgenson. 1969. “The Measurement of U.S. Real Capital Input, 1929–1967.” Review of Income and Wealth 15 (4): 293320.

    • Search Google Scholar
    • Export Citation
  • Christensen, L. R., D. W. Jorgenson, and L. J. Lau. 1971. “Conjugate Duality and the Transcendental Logarithmic Production Function.” Econometrica 39: 25556.

    • Search Google Scholar
    • Export Citation
  • Church, A. H. 1901. “The Proper Distribution of Establishment Charges, Part III.” The Engineering Magazine 21: 90412.

  • Clements, K. W., and H. Y. Izan. 1981. “A Note on Estimating Divisia Index Numbers.” International Economic Review 22: 74547.

  • Clements, K. W., and H. Y. Izan. 1987. “The Measurement of Inflation: A Stochastic Approach.” Journal of Business and Economic Statistics 5: 33950.

    • Search Google Scholar
    • Export Citation
  • Cobb, C., and P. H. Douglas. 1928. “A Theory of Production.” American Economic Review 18: 39165.

  • Cochran, W. G. 1977. Sampling Techniques, 3rd edition. New York: Wiley.

  • Cockburn, I. M., and A. H. Anis. 1998. “Hedonic Analysis and Arthritic Drugs.” Working Paper 6574, National Bureau of Economic Research, Cambridge, MA.

    • Search Google Scholar
    • Export Citation
  • Coggeshall, F. 1887. “The Arithmetic, Geometric and Harmonic Means.” Quarterly Journal of Economics 1: 836.

  • Combris, P., S. Lecocqs, and M. Visser. 1997. “Estimation of a Hedonic Price Equation for Bordeaux Wine: Does Quality Matter?Economic Journal 107 (441): 390402.

    • Search Google Scholar
    • Export Citation
  • Commission of the European Communities (Eurostat), International Monetary Fund, Organisation for Economic Co-operation and Development, United Nations, and World Bank. 1993. System of National Accounts 1993. Brussels/Luxembourg, New York, Paris, Washington, DC: United Nations.

    • Search Google Scholar
    • Export Citation
  • Congressional Budget Office (CBO). 1994. “Is the Growth of the CPI a Biased Measure of Changes in the Cost of Living?CBO Paper, October, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Crawford, A. 1998. “Measurement Biases in the Canadian CPI: An Update.” Bank of Canada Review (Spring): 3956.

  • Cropper, M. L., L. L. Deck, and K. E. McConnell. 1988. “On the Choice of Functional Form for Hedonic Price Functions.” Review of Economics and Statistics 70 (4): 66875.

    • Search Google Scholar
    • Export Citation
  • Crump, N. 1924. “The Interrelation and Distribution of Prices and Their Incidence upon Price Stabilization.” Journal of the Royal Statistical Society 87: 167206.

    • Search Google Scholar
    • Export Citation
  • Cunningham, A. W. F. 1996. “Measurement Bias in Price Indices: An Application to the UK’s RPI, Bank of England.” Working Paper 47, Bank of England, London.

    • Search Google Scholar
    • Export Citation
  • Curry, B., P. Morgan, and M. Silver. 2001. “Hedonic Regressions: Mis-specification and Neural Networks.” Applied Economics 33: 65971.

    • Search Google Scholar
    • Export Citation
  • Czinkota, M. R., and I. Ronkainen. 1997. “International Business and Trade in the Next Decade: Report from a Delphi Study.” Journal of International Business Studies 28 (4): 82744.

    • Search Google Scholar
    • Export Citation
  • Dalén, J. 1992. “Computing Elementary Aggregates in the Swedish Consumer Price Index.” Journal of Official Statistics 8: 12947.

  • Dalén, J.. 1994. “Sensitivity Analyses for Harmonizing European Consumer Price Indices.” Paper presented at the First Meeting of the International Working Group on Price Indices, Ottawa, October 31–November 4. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Dalén, J.. 1995. “Quantifying Errors in the Swedish Consumer Price Index.” Journal of Official Statistics 13 (3): 34756.

  • Dalén, J.. 1997. “Experiments with Swedish Scanner Data.” Paper presented at the Third Meeting of the International Working Group on Price Indices, Voorburg, April 16–18. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Dalén, J.. 1998a. “On the Statistical Objective of a Laspeyres Price Index.” Paper presented at the Fourth Meeting of the International Working Group on Price Indices, Washington, DC, April 22–24. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Dalén, J.. 1998b. “Studies on the Comparability of Consumer Price Indices.” International Statistical Review 66 (1): 83113.

  • Dalén, J.. 1999a. “On Reliability, Uncertainty and Bias in Consumer Price Indexes.” In Proceedings of the Measurement of Inflation Conference, edited by M. Silver and D. Fenwick. Cardiff: Cardiff University, pp. 18490.

    • Search Google Scholar
    • Export Citation
  • Dalén, J.. 2017. “Unit Values and Aggregation in Scanner Data— Towards a Best Practice.” Paper presented at the Fifteen Meeting of the International Working Group on Price Indices, Eltville am Rhein, May 10–12. http://www.ottawagroup.org.

    • Search Google Scholar
    • Export Citation
  • Dalén, J., and E. Ohlsson. 1995. “Variance Estimation in the Swedish Consumer Price Index.” Journal of Business and Economic Statistics 13 (3): 34756.

    • Search Google Scholar
    • Export Citation
  • Dalton, K. V., J. S. Greenlees, and K. J. Stewart. 1998. “Incorporating a Geometric Mean Formula into the CPI.” Monthly Labor Review 121 (10): 37.