17 Statistical Principles for Inflation-Targeting Regimes and the Role of IMF Data Initiatives
- Carol Carson, Claudia Dziobek, and Charles Enoch
- Published Date:
- September 2002
Inflation targeting emerged in the 1990s as a popular framework within which monetary policy is conducted. Its main feature is that the central bank is given a clear mandate to concentrate on achieving an explicit inflation target as the overriding objective of monetary policy. A number of industrial and developing countries have chosen this regime, and some others are moving in this direction. While many of the conceptual aspects of inflation targeting have been studied extensively in recent years, less attention has been given to the statistical issues related to inflation targeting: which statistics are needed, how they should be compiled, and whether there is a role for international bodies (including the IMF) in establishing best practices in statistics.1
This chapter addresses the statistical principles underlying inflation-targeting regimes.2 It looks at whether and how inflation targeting has changed the data requirements of countries that have adopted such a regime. It also explores how the IMF’s statistical initiatives, such as the Special Data Dissemination Standard (SDDS), Data Quality Assessment Framework (DQAF), data module of the Report on the Observance of Standards and Codes (ROSC), and work on methodological manuals might bolster the statistical foundations and facilitate the compilation and dissemination of pertinent information.
Statistical Aspects of an Inflation-Targeting Regime
Inflation Forecasting and an Arsenal of Indicators
In an inflation-targeting system, countries focus directly on inflation rather than intermediate targets such as monetary aggregates or exchange rates.3 The authorities announce a target or, more typically, a target range for inflation as the overriding policy objective; policy actions are taken if projected inflation (usually over a one- to two-year time horizon) falls outside the announced target range. Because monetary policy actions typically affect inflation with a lag, the inflation-targeting process is forward looking. Policy thus emphasizes the systematic assessment of future, rather than current or past, inflation. Most inflation-targeting countries rely on market-based instruments (usually open market operations) to adjust short-term interest rates (referred to as operating targets) in response to deviations of expected inflation from the target.4
The selection of a price index as the measure of inflation to target is a prerequisite for inflation targeting. The choice of price measure is influenced by, among other factors, the timeliness and availability of the data, public understanding and acceptance of the measure, its freedom from manipulation by the authorities, and controllability (Bloem, Armknecht, and Zieschang, this volume). Certain volatile components are sometimes excluded from the target price index to increase central bank control over it.
Depending on data availability and country-specific circumstances, central banks use a combination of quantitative models and traditional and forward-looking indicators of economic activity to arrive at inflation forecasts. The choice and specification of the quantitative model has implications for the data requirements for inflation targeting. Clarity about the model being used is therefore important in assessing the adequacy of the statistical framework.5
Forward-looking indicators include measures of inflation expectations that are typically derived from developments in the asset markets, such as forward exchange rates, differences in yields between inflation-indexed and nominal government securities, term structure of interest rates, and differences in nominal and real forward interest rates. Forward-looking indicators also include measures of inflation expectations derived from business and household surveys. These have an important role in inflation-targeting regimes. Forward-looking indicators supplement traditional economic and financial data such as measures of overall economic activity, monetary and credit aggregates, current account and fiscal balances, and consumer and producer price indices, which are derived from the major macroeconomic statistical systems (national accounts, government finance, monetary, and balance of payments statistics) and price statistics.
The role of some of the variables employed in an inflation-targeting framework differs from their role in other regimes. Inflation targeting places a premium on price statistics and forward-looking indicators. The monetary aggregates are used not as intermediate targets in inflation targeting, but as explanatory variables in quantitative economic models or general economic variables that, together with other variables, are used to make judgmental adjustments to the inflation forecasts derived from the models. Implementing monetary policy in an inflation-targeting regime thus calls for a broad set of economic and financial indicators as well as reliable forecasting.
Reliable forecasting depends on the information content of the data used. To some extent, central banks may be in a position to exert direct influence over the quality of the data they collect, such as the money stock and its components.6 This influence most often results from the central bank’s role in supervising the banking system and its authority to collect balance sheet data and other reports from banks. In contrast, many of the other economic indicators used in an inflation-targeting regime are likely to be compiled by the national statistical office or other agencies. In particular, price data used in measuring inflation are often compiled by the national statistical office. The authorities may also tap other government agencies, research institutes, or the private sector as sources of forward-looking and other indicators (such as expectation surveys, interest rate statistics, and real estate prices). Given the wide range of data sources for indicators employed in inflation forecasting, the development of statistics with high information content requires close cooperation between compiling agencies to ensure consistent application of statistical methodologies and concepts, and to minimize duplication of effort.
Credibility, Accountability, and Transparency
Convergence of public expectations about inflation and the central bank’s inflation target is an important objective in inflation targeting. The public’s inflation expectations are influenced, in part, by the central bank’s reputation, or credibility, in achieving the inflation target. To this end, central banks in inflation-targeting countries have incentives to operate in a transparent manner and communicate clearly to the general public the objectives, targets, and time horizons of the framework. Credibility in achieving the inflation target is further bolstered when government authorities hold the central bank accountable for deviations from the inflation target and insulate monetary policy decisions from political pressures.7
The importance of transparency in inflation-targeting regimes has meant that some central banks take significant steps to disclose regular forecasts of inflation and other variables, as well as their views on inflation performance, motivations behind monetary actions, and the inflationary outlook. To satisfy the need for accountability to government authorities, they also prepare and publish reports. These reports are sometimes supplemented by public appearances by senior central bank officials before parliamentary committees, and by publication of the minutes of monetary policy meetings.
Transparency of policy is supplemented by transparency in the statistical processes and methodologies used in measuring economic activity. Transparency in statistics is achieved when the central bank and statistical agencies disseminate enough information to enable the public and market participants to understand how the data are compiled and used, and to judge whether the inflation targets can be met. From a global perspective, the IMF has assumed an active role in promoting data transparency, as discussed in more detail below. Methodological soundness is a core element in these efforts.
Key Statistical Principles
The foregoing discussion can be cast in terms of four key statistical principles that should underlie an inflation-targeting regime:
- development of a wide range of economic and financial indicators to support reliable inflation forecasting and informed policy action;
- close cooperation among statistical agencies involved in the collection and compilation of traditional and forward-looking indicators to promote consistent application of methodologies and to minimize duplication of effort;
- transparency in statistical methodologies and compilation procedures, as well as transparency that results from the timely dissemination of a wide range of data, to promote public understanding of how the data are compiled and used; and
- methodological soundness in statistics to promote consistency of definitions and concepts among indicators, as well as public understanding of and confidence in the indicators.
In recent years, the IMF and other international organizations have drawn on country practices to develop a number of standards and areas of best practice in statistics. The remainder of the paper explores how these statistical initiatives can support the implementation of key statistical principles for an inflation-targeting regime.
An Overview of IMF Statistical Initiatives
The IMF’s Statistics Department has played a central role in developing internationally accepted statistical methodologies and standards for compiling macroeconomic data, as well as best practices for disseminating them. These methodological standards form a basis for IMF programs and for discussions with member countries during surveillance missions. Also, IMF missions provide training and technical assistance in statistical methodologies, data compilation, and the development of metadata underlying the dissemination activities. This section briefly touches on major statistical initiatives that appear to have direct relevance to inflation-targeting regimes.
Data Dissemination: SDDS and GDDS
The IMF established the Special Data Dissemination Standard in 1996 to enhance the availability of timely and comprehensive statistics and thereby contribute to the pursuit of sound macroeconomic policies and the efficient functioning of financial markets for those countries participating in, or seeking to participate in, international financial markets.8 The SDDS is designed to be applicable across a broad range of countries and policy regimes.
The SDDS prescribes best practices in four dimensions of the dissemination of statistics: (1) data coverage, periodicity, and timeliness; (2) public access to data; (3) data integrity; and (4) data quality. For the first dimension, the SDDS prescribes the dissemination of data for the four major macroeconomic sectors (real, fiscal, financial, and external) and for the population. It specifies data components and the respective periodicity and timeliness for each data category. For the access dimension, the SDDS specifies dissemination practices that facilitate ready and equal access; under the integrity and quality dimensions, it specifies additional descriptive information that should accompany the data. Disseminating descriptions of the underlying methodologies and statistical techniques used in compiling the data helps users make their own assessments of the data’s quality.
Each SDDS subscriber is expected to submit information about its data and its dissemination practices—so-called metadata—to the IMF for presentation on an electronic bulletin board called the Dissemination Standards Bulletin Board (DSBB).9 In addition, a statement summarizing the methodology for each data category is posted on the DSBB. The presentation of the metadata on the DSBB helps the IMF, the financial markets, and other data users monitor observance of the SDDS. The DSBB provides hyperlinks to the National Summary Data Page (NSDP) websites that the subscribers have established to disseminate the actual data meeting the coverage, periodicity, and timeliness requirements prescribed by the SDDS. The DSBB also provides an Advance Release Calendar showing the dates on which the data in each category will be disseminated in the coming quarter.
Since its inception, the SDDS has evolved into an internationally recognized tool for disseminating economic and financial statistics. It had 50 subscribers as of the end of 2001, consisting of a mix of industrial countries, emerging market economies, and transition economies. Market participants make frequent use of the SDDS website.
The IMF established the General Data Dissemination System (GDDS) in 1997 to provide member countries with a framework for evaluating needs for improving data and setting priorities, and for guiding the dissemination to the public of comprehensive, timely, accessible, and reliable economic, financial, and sociodemographic statistics. The GDDS provides a tool for building statistical capacity, and for many countries it serves as an entry to the more demanding SDDS. Thirty-nine countries are now participating in the GDDS.10
The Data Quality Assessment Framework
In statistics, quality has traditionally been synonymous with accuracy. In recent years, however, a consensus has emerged in the international statistical community that quality is a broad, multidimensional concept that encompasses the collection, processing, and dissemination of statistical information (Carson, 2001). Building on the growing literature on data quality, practical experience, and feedback from several rounds of consultations, the IMF Statistics Department developed the DQAF, consisting of generic as well as dataset-specific frameworks for assessing data quality.11 The generic framework brings together the core elements of internationally accepted standards and practices for official statistics and serves as the underpinning for frameworks specific to the dataset. The generic framework (see Appendix 17.2) covers the entire statistical infrastructure for data collection, compilation, and dissemination. Specific frameworks that have been developed so far cover the major macroeconomic statistical datasets: national accounts, price statistics (namely, the consumer price index, or CPI, and the producer price index, or PPI), government finance, monetary, and balance of payments statistics.12
The DQAF specifies five dimensions of quality—integrity, methodological soundness, accuracy and reliability, serviceability, and accessibility—together with the prerequisites of quality (see Box 17.1). The DQAF takes a comprehensive approach to data quality and includes transparency and cooperation among agencies that compile data as integral parts of data quality.
Data Module of the Report on the Observance of Standards and Codes
In response to the financial crises of the mid-1990s, the IMF has taken a number of steps to strengthen the architecture of the international financial system. One important motivation for this is the perceived need for increased transparency and for standards and codes in areas relevant to the effective functioning of members’ economic and financial systems. Assessments of members practices with respect to such standards and codes form the basis for the ROSC.
These assessments are conducted as separate modules covering several areas, one of which is data.13 The data module assesses both disclosure (provision of information to the public) and statistical practices (quality of the information). The SDDS and the GDDS together serve as elements against which to assess data dissemination practices. The DQAF is employed to assess the quality of the data provided to the public. Thus, the data module of the ROSC applies the SDDS/GDDS and the DQAF in a diagnostic exercise to help countries strengthen their statistical frameworks and identify areas for improvement.
Box 17.1.Data Quality Assessment Framework (DQAF): Dimensions of Quality
Work toward a comprehensive framework for assessing the quality of data has been under way in the IMF’s Statistics Department for some time. This work responds to a number of needs, in particular the needs to complement the quality dimension of the IMF’s Special Data Dissemination Standard (SDDS), to focus more closely on the quality of the data countries provide the IMF that underpin the IMF’s surveillance of their economic policies, and to assess evenhandedly the quality of a country’s statistics as part of the IMF’s Reports on the Observance of Standards and Codes (ROSCs).
The five dimensions of quality included in the DQAF are as follows:
- Integrity. Statistical systems should be based on firm adherence to the principle of objectivity in the collection, compilation, and dissemination of statistics. Integrity encompasses the institutional foundations that are in place to ensure professionalism in statistical policies and practices, transparency, and ethical standards.
- Methodological soundness. The methodological basis for the production of statistics should follow international standards, guidelines, and agreed practices. In application, this dimension will necessarily be dataset-specific, reflecting differing methodologies for different datasets—for example, the System of National Accounts 1993 for national accounts (see Commission of the European Communities and others, 1993) and the fifth edition of the IMF’s Balance of Payments Manual for balance of payments (see IMF, 1993).
- Accuracy and reliability. Accuracy and reliability are among the most sought-after attributes of data. Users are concerned that data sufficiently portray reality at all stages of dissemination—from “flash” to “final” estimates. Thus, source data and compilation techniques must be sound if data are to meet users’ needs.
- Serviceability. Another area of concern is whether the data that are produced and disseminated are actually useful. Data should be produced and disseminated in a timely fashion and with an appropriate periodicity, provide information relevant to the subject field, be consistent internally and with other related datasets, and follow a predictable revisions policy.
- Accessibility. Users want understandable, clearly presented data. They need to know how data are put together and they need to be able to count on data producers for prompt and knowledgeable answers to their questions. Thus, it is necessary to ensure that clear data and metadata are easily available, and that assistance to users of data is adequate.
The framework also includes a few elements that, although not dimensions of quality in themselves, have an overarching role as prerequisites, or institutional conditions, for quality. They appear as a zero category in the first row of the generic DQAF in Appendix 17.2 and relate to such issues as whether a supportive legal and administrative framework is in place, whether resources are commensurate with the needs of statistical programs, and whether quality is recognized as a cornerstone of statistical work by producers of official statistics.
As an important complement to developing data dissemination standards and DQAFs, the IMF helps countries improve the quality of their data through the development of internationally accepted guidelines on statistical methodology. In recent years, international and supranational organizations, in close cooperation with national statistical agencies, have made major advances in developing consistent methodologies for macroeconomic statistical areas (see Box 17.2). Through its extensive technical assistance program, the IMF’s Statistics Department is actively involved in putting these methodologies into practice.
IMF Statistical Initiatives and Inflation-Targeting Regimes
This section examines how the IMF statistical initiatives discussed above can be used to support the implementation of key statistical principles for an inflation-targeting regime. Moreover, lessons learned from the experiences of inflation-targeting countries will enhance the IMF’s ability to assist member countries that adopt inflation targeting.
Box 17.2.Methodological Manuals
A wide range of methodological manuals1 has become available in the last decade. A representative sample is described below under four headings: comprehensive manuals, sector-based manuals, construct-based manuals, and theme-based manuals. (Manuals prepared by the IMF are shown in boldface; manuals in which the IMF participated are underlined.)
The manuals that provide for a comprehensive view of the economy are those that deal with the national accounts at large.
By far the broadest macroeconomic set in scope is the System of National Accounts1993 (1993 SNA) (Commission of the European Communities and others, 1993).
The recently published Quarterly National Accounts Manual (IMF, 2001j) deals extensively with practical and theoretical issues specific to the quarterly accounts. The Handbook of National Accounting: Use of the System of National Accounts in Economies in Transition (United Nations, 1996) is largely a compilation guide to provide help on the application of 1993 SNA in transition economies and other countries introducing market mechanisms.
The Handbook of Input-Output Table Compilation and Analysis (United Nations, 1999) covers the conceptual and statistical integration of the supply and use table. It provides conceptual guidelines and is also a compilation guide. It is fully harmonized with the 1993 SNA.
The sector-based manuals present the economy from a sector perspective (unlike the comprehensive framework, in which sectors are viewed as parts of a whole and, as such, conform to one another).
The Balance of Payments Manual (BPM5) (IMF, 1993) provides an integrated framework to record economic activities of all residents of an economy with nonresidents. The Manual is complemented by a Compilation Guide and Textbook.
International Merchandise Trade: Concepts and Definitions (United Nations, 1998) provides guidelines for recording cross-border merchandise trade. It is largely consistent with BPM5; a compilation guide is under way.
The OECD Benchmark Definition of Foreign Direct Investment (OECD, 1996) provides guidance on how to compile direct investment statistics. It is a compilation guide, with concepts and definitions consistent with BPM5.
The guidelines provided in the Coordinated Portfolio Investment Survey Guide (IMF, 2002a) are in full agreement with BPM5.
External Debt Statistics: Guide for Compilers and Users (BIS and others, 2002, forthcoming) provides a framework for recording the liabilities to nonresidents that require payments of interest and/or principal by the debtor at some point in the future. The manual includes reconciliation with the relevant elements of 1993 SNA and BPM5.
Government Finance Statistics Manual 2001 (IMF, 2001f) presents an integrated framework for recording government economic activities. It is harmonized with the 1993 SNA and provides bridging links to it.
Measuring the Non-Observed Economy: A Handbook (OECD, 2002) covers production activities that are not included in the basic statistics because they are informal, illegal, underground, or otherwise missed by the statistical system. It identifies activities by sector according to the framework of 1993 SNA.
Measuring Capital: A Manual on the Measurement of Capital Stocks, Consumption of Fixed Capital and Capital Services (OECD, 2001) is fully consistent with the 1993 SNA.
The Monetary and Financial Statistics Manual (IMF, 2000a) provides guidelines on concepts, definitions, and classification of monetary and financial statistics; a compilation guide is under way.
For corporations, except for the manual Links Between Business Accounting and National Accounting (United Nations, 2000), which deals largely with country practices with references to methodological issues, no guidelines exist that pertain exclusively to concepts and definitions.
The household sector is covered in the Handbook on Household Accounting: Experience in the Use of Concepts and Their Compilation (United Nations, International Labor Organization, and Johns Hopkins University, 1998). Also, some of the activities of the household are covered in a manual that is under way on nonprofit institutions (see below under theme-based manuals).
The construct-based manuals focus on the “what” of the question, “Who does what with whom?” The “what” refers to constructs that arise from production as well as from other types of activities.
The construct manuals on goods and services expand on the classification of goods and services by function, commodity, and industry. They also include manuals that focus on the prices and volumes of goods and services.
The construct manuals dealing exclusively with nonproduced assets are on financial derivatives, one for national accounts (see IMF, 2000b) and the other for balance of payments (see IMF, 2000c). Nonproduced assets are also extensively dealt with in the framework of environmental accounts (see theme-based manuals below).
Theme-based manuals respond to specialized analytical needs, largely using existing guidelines as building blocks and expanding on them.
The Manual on Statistics of International Trade in Services(TIS) places special emphasis on the statistical needs of the General Agreement on Trade in Services. It is a manual on concepts and definitions, and a compilation guide will follow. The accounts presented in TIS have been bridged to accounts of BPM5 and 1993 SNA.
International Reserves and Foreign Currency Liquidity: Guidelines for a Data Template (Kester, 2001) provides guidelines for compiling information on the external liquidity of a country’s authorities. It is a compilation guide and includes descriptions on how to bridge to the reserve assets of BPM5.
The Handbook on Non-Profit Institutions in the System of National Accounts (United Nations, forthcoming) develops and implements a system on nonprofit entities. It is both a manual on guidelines and a compilation guide.
The System of Integrated Environmental and Economic Accounting (United Nations and others, forthcoming) is a manual of guidelines on concepts, definitions, and classifications, with links to 1993 SNA. It provides a complete and extended set of economic accounts with full consideration of environmental assets, along with techniques for policy analysis purposes. A compilation guide is also available.
For more information on the manuals cited here, see Carson and Laliberté (2001) at http://www.imf.org/external/pubs/ft/wp/2001/wp01183.pdf, Annex I, pp. 16–20.
Transparency in Statistics
Transparency in statistics—reflected in the promotion of public access to, and understanding of, the economic indicators used for forecasting inflation and other purposes in inflation-targeting countries—is important in building public confidence in the central bank’s monetary policy. Countries that agree to prepare the ROSC data module and publish the report send clear signals about their openness to publicizing their statistical policies and practices and their commitment to addressing areas in need of improvement. The publication of the data module thus fosters better public understanding of the data used for inflation forecasting and policymaking.
To date, seven inflation-targeting countries have published the results of an ROSC data module (Australia, Chile, the Czech Republic, Hungary, South Africa, Sweden, and the United Kingdom).14 All of these countries were found to have generally well-developed statistical systems that are adequate for conducting effective surveillance. Nevertheless, shortcomings in some statistical practices have the potential to detract from the accurate and timely analysis of economic and financial developments and the formulation of appropriate policies. For example, price statistics in some inflation-targeting countries were found to have limited sector coverage, infrequent updating of weights, inadequate treatment of new products and quality changes, and partial validation procedures.
Subscription to the SDDS is particularly relevant for inflation-targeting countries because one of the fundamental objectives of SDDS is the transparency of macroeconomic statistics. Taking a comprehensive view of the dissemination of economic and financial data, SDDS subscribers provide the public, including market participants, with simultaneous access to data, advance notice of release dates, indication of internal government access to data before release, information on national statistical methodologies, and, by way of links to the NSDP, the actual data meeting the coverage, periodicity, and timeliness prescribed by the SDDS. The SDDS thus provides a tool for promoting international access to data and understanding of how the data are compiled and used in formulating economic and monetary policy.
All inflation-targeting countries but one (New Zealand) subscribe to the SDDS. Statistics in all SDDS categories are used as economic indicators in an inflation-targeting regime, but price statistics and forward-looking indicators play a particularly relevant role (Table 17.1 shows the data categories specified by the SDDS). Some possible ways in which the SDDS can be used to promote transparency in price statistics, forward-looking indicators, interest rate statistics, and other statistics employed in inflation targeting are explored below.
|National accounts: nominal, real, and associated prices||GDP by major expenditure category and/or by productive sector||Saving, gross national income|
|Production index/indices||Industrial, primary commodity, or sector, as relevant||Forward-looking indicator(s)—for example, qualitative business surveys, orders, composite leading indicators index1|
|Labor market||Employment, unemployment, and wages/earnings, as relevant|
|Price indices1||Consumer prices and producer or wholesale prices|
|General government or as relevant||Revenue, expenditure, balance, and domestic public sector operations (bank and nonbank) and foreign financing||Interest payments|
|Central government operations||Budgetary accounts: revenue, expenditure, balance, and domestic (bank and nonbank) and foreign financing||Interest payments|
|Central government debt||Domestic and foreign, as relevant, with a breakdown by currency (including indexed), as relevant, and a breakdown by maturity; debt guaranteed by central government, as relevant||Debt service projections: interest and amortization on medium- and long-term debt (quarterly for next 4 quarters and then annually) and amortization on short-term debt (quarterly)|
|Analytical accounts of the banking sector||Money aggregates, domestic credit by public and private sector, external position|
|Analytical accounts of the central bank||Reserve money, domestic claims on public and private sector, external position|
|Interest rates1||Short-term and long-term government security rates, policy variable rate||Range of representative deposit and lending rates|
|Stock market||Share price index, as relevant|
|Balance of payments||Goods and services, net income flows, net current transfers, selected capital (or capital and financial) account items (including reserves)||Foreign direct investment and portfolio investment|
|International reserves and foreign currency liquidity||Total official reserve assets, (gold, foreign exchange, SDRs, and IMF position); other foreign currency assets; predetermined short-term drains on foreign currency assets; contingent short-term drains on foreign currency assets; and related items|
|Merchandise trade||Exports and imports||Major commodity breakdowns with longer time lapse|
|International investment position||Direct investment; portfolio investment; other investment; and reserve assets|
|Exchange rates||Spot rates and 3- and 6-month forward market rates, as relevant|
|External debt (transition period through end of March 2003)||Debt of the general government, the monetary authorities, the banking sector, and other sectors. Data should also be broken down by maturity—short-term and long-term—on an original maturity basis and by instrument, as set out in the BPM5||Debt service payment schedule, domestic/foreign currency breakdown|
|Addendum: Population||Key distributions—for example, by age and sex|
Indicate Special Data Dissemination Standard (SDDS) data categories for price indices, interest rates, and forward-looking indicators, which play an important role in inflation-targeting regimes.
Indicate Special Data Dissemination Standard (SDDS) data categories for price indices, interest rates, and forward-looking indicators, which play an important role in inflation-targeting regimes.
The SDDS prescribes the dissemination of data on consumer and producer (or wholesale) prices on a monthly basis within one month following the reference month. Because countries may prepare various price indices that differ with respect to geographic and item coverage, the SDDS specifies that the indices most widely used in the country should be included. The SDDS does not prescribe any component or subindex detail; dissemination of a single index for consumer and producer price data meets the standard.
All inflation-targeting countries that subscribe to the SDDS disseminate a broad price index on the NSDP and associated metadata on the DSBB (see Table 17.2).
|Price Index for Inflation Targeting|
|Metadata in the DSBB|
index posted on
|Brazil||IPCA||M||Broad national CPI (IPCA)||Yes||Yes|
|Korea||CPI||M||CPI, excluding price changes of selected petroleum products and non-cereal agriculture products||No||Yes|
|Norway||CPI||M||CPI, excluding changes in interest rates, taxes, excise duties, and extraordinary temporary||No||No|
|South Africa||CPI||M||CPI, excluding mortgage interest payments and fuel||No||Yes|
|Thailand||CPI||M||CPI, excluding prices on raw food and energy||No||Yes|
|United Kingdom||RPI||M||Retail prices index (RPI), excluding mortgage interest payments||No||Yes|
|New Zealand1||—||—||CPI, excluding interest charges and section prices||—||Yes|
Not an SDDS subscriber.
Not an SDDS subscriber.
However, five SDDS countries target a core measure of inflation that is narrower in item coverage than the price index described on the DSBB and posted on the NSDP.15 Therefore, providing metadata on the DSBB to describe the rationale for not including some items in the core inflation measure, as well as information on the relative weights of these items in the overall price index,16 would be useful for bolstering public knowledge and understanding of the core measure of inflation.17
The SDDS provides for the dissemination of forward-looking indicators as an encouraged category—a data category that is not prescribed but is recommended for enhancing the transparency of economic performance and policy. Because of the variety of possible forward-looking indicators, the SDDS does not define them, but provides examples of the indicators that might be included in this category. Currently, seven inflation-targeting countries (Australia, Chile, Korea, Mexico, Norway, South Africa, and Sweden) post forward-looking indicators on their NSDPs and associated metadata on the DSBB (see Box 17.3).18
Given the role and diversity of forward-looking indicators used in forecasting inflation, there may be a need to explore further whether the SDDS provides enough room and flexibility for the inclusion of metadata to promote public understanding of the concepts and definitions used in compiling them.
Box 17.3.Forward-Looking Indicators and Metadata Posted on the National Summary Data Page (NSDP) and the Dissemination Standards Bulletin Board (DSBB)
Forward-looking indicators suggest the direction of possible future developments in certain variables. In order to enhance the transparency of economic performance and policy, the SDDS encourages disseminating one or more forward-looking indicators on the DSBB. The SDDS considers forward-looking indicators that “include surveys of expectations such as qualitative surveys of business management and of consumers; surveys of presaging events such as orders, contracts, and construction permits; and indices that combine several indicators into a single index. The last item is often referred to as ‘leading indicators,’ which may be part of a system of indicators of business cycles” (IMF, 1996, pp. 22–23).
Seven inflation-targeting countries—Australia, Chile, Korea, Mexico, Norway, South Africa, and Sweden—currently disseminate metadata on forward-looking indicators on the DSBB. Following are the indicators those countries post on their NSDPs and the supporting metadata they disseminate on the DSBB.
Indicators on NSDP: Expected aggregate change in trading performance profit (short term).
Metadata on DSBB
Quarterly Business Expectations Survey, which measures changes in expectations from the current quarter to the future reference quarter for the following variables: operating income, selling prices, profit, wages, non-wage labor expenses, other operating expenses, total operating expenses, inventories, employment, and capital expenditure. A weighted net balance for these indicators is also available.
Quarterly Experimental Composite Leading Indicator (CLI), which is the aggregation of eight partial leading indicators used to predict turning points in the business cycle. Partial indicators used in the CLI are real interest rates, trade factor (defined as the ratio of the commodity prices to the producer price index for imported materials), gross domestic product for the United States, job vacancies for all industries, secured housing finance commitments to individuals, all-industrial index, production expectations, and business expectations.
Other quarterly forward-looking indicators such as anticipated capital expenditure, expected sales, and expected mineral and petroleum exploration.
Indicator on NSDP: None.
Metadata on DSBB
Monthly data on construction approved and started that show trends in construction activity for the metropolitan region and the country as a whole.
Indicators on NSDP: Leading Composite Index (1995 = 100) and Business Survey Index.
Metadata on DSBB
Quarterly Business Survey, which assesses general business conditions and future expectations as viewed by managers of large nonfinancial corporations. The Business Survey Index covers business conditions, equipment investment, sales, production, and profitability, and so forth.
Monthly Leading Composite Index, which provides advance indications of cyclical turning points in economic activity. It has 10 components, which come from administrative records and statistical surveys. Variables from administrative records are the floor area of permitted building construction for residential and industrial use, M3, outstanding export letters of credit, and imports. Variables from statistical surveys are the ratio of placed to displaced workers, producers’ shipment index (intermediate and durable consumer goods), value of machinery orders received (private and public), and inventory circulation indicators.
Indicators on NSDP: Percentage of surveyed enterprises expecting their production to increase over the previous month (same as the metadata shown on DSBB).
Metadata on DSBB
Monthly qualitative survey of expectations of high-ranking officials from a sample of firms in the manufacturing sector about the direction (for example, whether production increases, decreases, or remains the same) of certain variables such as production, sales, employment, inventories of finished products, and raw materials stocks.
Indicator on NSDP: None.
Metadata on DSBB
Quarterly Survey of Business Management judged by business managers, containing qualitative data on both actual and expected developments, which covers trends in output, orders, capacity utilization, prices, and employment in manufacturing and in mining and quarrying.
Indicator on NSDP: Leading Indicator Index.
Metadata on DSBB
Monthly Leading Indicator Index, which is a composite index comprising 21 components that have a record of reaching cyclical turning points before those in aggregate economic activity.
Indicators on NSDP: Confidence indicator (construction), confidence indicator (manufacturing), and indicator of sales volume in durable goods.
Metadata on DSBB
Monthly surveys of business trends covering manufacturing, construction, and retail trade sectors, which provide qualitative data on recent and expected development of production (turnover), orders, prices, stocks, and employment. The surveys are carried out in accordance with the European Union’s harmonized statistical program. Quarterly surveys of business trends use enlarged questionnaires and enhanced samples.
Interest rate statistics
The SDDS prescribes the dissemination of interest rates on short-term and long-term government securities, together with a policy variable interest rate such as a central bank lending or discount rate. Room is provided on the DSBB for metadata to describe the role of policy variable interest rates. The SDDS also provides for the dissemination of deposit and lending rates as an encouraged category.
Table 17.3 presents a list of short-term interest rates used as operating targets by inflation-targeting countries.19 While all countries disseminate the operating targets on their national websites, five countries do not post them on the NSDP or describe their role in monetary policy on the DSBB. In addition, two countries describe their operating targets on the DSBB but do not post the associated data on the NSDP. Conversely, one country posts the operating target on the NSDP but does not provide the associated metadata on the DSBB. The inclusion of interest rates used as operating targets on the NSDP together with metadata on the DSBB could be useful in bolstering public knowledge and understanding of their role in monetary policy.
|Australia||Cash rate target||No||No||Yes|
|Brazil||Overnight interbank interest rate||Yes||Yes||Yes|
|Canada||Target for the overnight rate||No||No||Yes|
|Chile||Monetary policy rate target||Yes||Yes||Yes|
|Colombia||Central bank intervention interest rate (repo)||No||No||Yes|
|Czech Republic||Two-week repo rate||Yes||Yes||Yes|
|Hungary||Two-week central bank deposit rate||Yes||Yes||Yes|
|Iceland||14-day repo rate||Yes||Yes||Yes|
|Israel||Interest rate on monetary deposits at the Bank of Israel||No||No||Yes|
|Korea||Overnight call rate||Yes||No||Yes|
|Mexico||Settlement balances using a mechanism known as the corto||No||No||Yes|
|Norway||Overnight sight deposit rate||Yes||Yes||Yes|
|Poland||28-day National Bank of Poland bill rate||No||Yes||Yes|
|South Africa||Repo rate||Yes||Yes||Yes|
|Sweden||Weekly repo rate||Yes||Yes||Yes|
|Thailand||14-day repo rate||Yes||No||Yes|
|United Kingdom||Two-week repo rate||Yes||Yes||Yes|
|New Zealand1||Official cash rate||—||—||Yes|
Not an SDDS subscriber.
Statistics in other SDDS categories
Most inflation-targeting countries have developed subindices, or components, of data categories to facilitate forecasting core measures of inflation. For example, they sometimes use subindices of stock market prices and interest rates, and components of monetary aggregates. Although in inflation-targeting regimes monetary statistics are not used as intermediate-target variables, they are still important as explanatory variables in inflation-forecasting models, and as timely general economic variables.20 When such data are used in an inflation-targeting regime, public knowledge and understanding of subindices and components of aggregated data could be promoted not only by publishing the data, but also by including additional metadata on the DSBB to describe the concepts and definitions used to compile these indicators.
A Range of Methodologically Sound Indicators
The adoption of internationally accepted statistical standards promotes methodological soundness in statistics through consistency of definitions and concepts. It also promotes public understanding of and confidence in the economic and financial indicators used to formulate monetary policy. Participating in and publishing the data module of the ROSC enables authorities and the public to assess, among other factors, adherence to internationally agreed-on methodological statistical standards.
One feature of an inflation-targeting regime is that a wide range of economic indicators is explicitly considered in the formulation of monetary policy. While the set of indicators may not necessarily be larger than in other regimes, the way these variables are employed is usually different. Monetary and exchange rate targeting tend to rely on intermediate-target variables compiled in the standard macroeconomic statistical systems for national accounts, government finance, monetary, and balance of payments statistics. As noted earlier, methodological manuals and dataset-specific DQAFs have been developed to address conceptual, compilation, and data quality issues for each of these datasets. DQAFs for both the CPI and the PPI have also been developed, and methodological manuals are being drafted.21
While inflation-targeting regimes make use of indicators compiled in the standard statistical systems, such regimes increasingly rely on economic indicators that are outside the scope of these statistical systems (for example, inflation expectation surveys). This may suggest a need to identify best practices in methodology22 that go beyond the scope of current methodological manuals.23
Cooperation Among Agencies
The IMF’s DQAF highlights two important conditions for the efficient production of relevant and high-quality statistics: (1) an institutional environment that is supportive of statistics and that clearly delineates the responsibilities for collecting, processing, and disseminating statistics, and (2) procedures for data sharing and coordination among data-producing agencies—in particular, arrangements to ensure the timely flow of data between agencies and to avoid duplication of effort. Given limited resources, the outcome of the ROSC data module process enables countries to set priorities for statistical activities in the areas the authorities deem to be of greatest immediate importance.
Limited information is available on whether countries have made substantial changes in the way they designate institutional responsibilities for comparing statistics, before or after they adopt their inflation-targeting regimes. Such information might be useful, both for countries that already have an inflation-targeting regime and for countries considering shifting to one, in understanding some of the institutional consequences for statistics.
Conclusions and Issues for Discussion
Implementing monetary policy in an inflation-targeting regime calls for a broad set of economic and financial indicators and reliable forecasting based on high-quality data. Price statistics and forward-looking indicators of inflation play an important role in an inflation-targeting regime.
As part of the effort to establish and maintain the credibility of the policy framework, central banks in inflation-targeting countries need to operate in a transparent manner and communicate clearly to the general public, both at home and abroad, the objectives, targets, and time horizons of the framework. The credibility of the policy framework is enhanced by transparency in the statistical processes and methodologies used in measuring economic activity.
Key statistical principles underlying an inflation-targeting regime are (1) the development of a wide range of economic and financial indicators, (2) close cooperation among statistical agencies, (3) transparency in statistical methodologies and compilation procedures, as well as the transparency that results from dissemination of a wide range of data, and (4) methodological soundness in statistics. IMF statistical initiatives support these statistical principles.
Subscription to the SDDS and preparation of the data module of the ROSC are particularly useful tools for promoting transparency in statistics. The outcome of the data module of the ROSC enables countries to also assess, among other things, adherence to internationally agreed-on statistical standards for indicators compiled in the traditional statistical systems (national accounts, government finance, monetary, balance of payments, and price statistics), and the degree of cooperation among statistical agencies.
To further the development of the main statistical principles for inflation targeting, several issues may warrant discussion:
- Disseminating additional metadata on the DSBB to promote public knowledge and understanding of (1) the rationale for not including some items in the core inflation measure and information on the relative weights of these items in the overall price index; (2) the concepts and definitions for compiling forward-looking indicators used in inflation targeting, and their timeliness and periodicity; (3) interest rates used as operating targets; and (4) the concepts and definitions used to compile subindices and components of aggregated data when such data are used in formulating monetary policy.
- While recognizing that the SDDS is a general framework applicable to countries regardless of policy regime, considering whether there are any other changes that might be made to the SDDS to make it more supportive for inflation-targeting countries.
- Developing methodology for forward-looking indicators and those economic indicators that are outside the scope of the traditional statistical systems—for example, inflation expectations surveys, stock market indices, and interest rate statistics.
- Sharing experiences on the institutional consequences for cooperation between agencies to optimize the availability and use of relevant statistics that a shift to inflation targeting may bring. What has been the role of the private sector in providing data for inflation targeting?
|Central bank legal framework||Instrument independence and currency or price stability is a central bank objective in all cases. Central bank financing of government deficit is limited or prohibited in all emerging market countries.|
|Design of the Inflation Target|
|Announcement of target||Announced by government or jointly by government and central bank, unless the central bank has an explicit mandate for price stability as the primary objective.|
|Target horizon||Indefinite for countries at longer-run inflation rate and annual for countries in disinflation.|
|Price index||Consumer price index for most emerging market countries and core inflation for most industrial countries.|
|Formal escape clauses||Only used by several industrial countries.|
|Point target or target range||Target range preferred by most countries given uncertainties associated with hitting targets. Point targets have been adopted in some cases to focus inflation expectations.|
|Accountability and transparency||Press releases of policy changes, regular inflation outlook reports, active dialogue with the private sector, and, in some cases, publication of inflation-forecasting models.|
|Operational Issues: Conduct of Monetary Policy|
|Inflation forecasting||Based on indicator variables, quantitative economic models, discussions with market participants, and, especially for emerging market countries, qualitative judgment.|
|Policy transmission channels||Emerging market countries with higher rates of inflation have channels characterized by downward price stickiness and rapid exchange rate pass-through.|
|Policy implementation||All countries use market-based instruments to target a short-term interest rate. Changes in official interest rates reflect deviations of inflation from the target and the output gap.|
|Changing economic relationships||As inflation-targeting framework gains credibility, linkages between inflation and the level of economic activity seem to weaken.|
|Responding to economic and financial shocks||Responses to external shocks range from doing nothing to a mixture of foreign exchange intervention and tighter monetary policy, depending on whether shocks are expected to affect inflation expectations or the stability of the financial system.|
|Breaches of the inflation target||Asymmetric responses to breaches of floor and ceiling during disinflation, and symmetric responses when inflation is at the long-run level; breaches do not seem to have damaged credibility.|
|Organizational Implications for Central Banks|
|Internal decision making||Many central banks have incorporated a broader range of perspectives and decentralized their organizational structure to enhance judgment-based decision making.|
|Monetary policy committees||Most central banks have formal committees. Consensus decisions are typically published, whereas voting records are not.|
|Central bank reorganization||Emerging market central banks have reorganized to improve data collection, inflation forecasting, and policy analysis.|
|Disinflation||Emerging market countries that started with higher inflation and crawling exchange rate bands disinflated over a long period to minimize output disruptions.|
|Long-run inflation objective||Consensus of around 1 to 3 percent for industrial countries and somewhat higher for emerging market countries.|
|Shifting from a fixed exchange rate regime||Slow and fast-track transitions from an exchange rate regime to full-fledged inflation-targeting framework for emerging market countries.|
|0. Prerequisites of quality||0.1 Legal and institutional environment—The environment is supportive of statistics.||0.1.1 The responsibility for collecting, processing, and disseminating statistics is clearly specified.|
0.1.2 Data sharing and coordination among data-producing agencies are adequate.
0.1.3 Respondents’ data are to be kept confidential and used for statistical purposes only.
0.1.4 Statistical reporting is ensured through legal mandate and /or measures to encourage response.
|0.2 Resources—Resources are commensurate with needs of statistical programs.||0.2.1 Staff, financial, and computing resources are commensurate with statistical programs of the agency.|
0.2.2 Measures to ensure efficient use of resources are implemented.
|0.3 Quality awareness—Quality is a cornerstone of statistical work.||0.3.1 Processes are in place to focus on quality.|
0.3.2 Processes are in place to monitor the quality of the collection processing and the dissemination of statistics.
0.3.3 Processes are in place to deal with quality considerations, including trade-offs within quality, and to guide planning for existing and emerging needs.
|1. IntegrityThe principle of objectivity in the collection, processing, and dissemination of statistics is firmly adhered to.||1.1 Professionalism—Statistical policies and practices are guided by professional principles.||1.1.1 Statistics are compiled on an impartial basis.|
1.1.2 Choices of sources and statistical techniques are informed solely by statistical considerations.
1.1.3 The appropriate statistical entity is entitled to comment on erroneous interpretation and misuse of statistics.
|1.2 Transparency—Statistical policies and practices are transparent.||1.2.1 The terms and conditions under which statistics are collected, processed, and disseminated are available to the public.|
1.2.2 Internal governmental access to statistics before their release is identified to the public.
1.2.3 Products of statistical agencies/units are clearly identified as such.
1.2.4 Advance notice is given of major changes in methodology source data and statistical techniques.
|1.3 Ethical standards—Policies and practices are guided by ethical standards.||1.3.1 Guidelines for staff behavior are in place and are well known to the staff.|
|2. Methodological soundnessThe methodological basis for the statistics follows internationally accepted standards, guidelines, or good practices.||2.1 Concepts and definitions—Concepts and definitions used are in accord with internationally accepted statistical frameworks.||2.1.1 The overall structure in terms of concepts and definitions follows internationally accepted standards, guidelines, or good practices.|
|2.2 Scope—The scope is in accord with internationally accepted standards, guidelines, or good practices.||2.2.1 The scope is broadly consistent with internationally accepted standards, guidelines, or good practices.|
|2.3 Classification/sectorization—Classification and sectorization systems are in accord with internationally accepted standards, guidelines, or good practices.||2.3.1 Classification/sectorization systems used are broadly consistent with internationally accepted standards, guidelines, or good practices.|
|2.4 Basis for recording—Flows and stocks are valued and recorded according to internationally accepted standards, guidelines, or good practices.||2.4.1 Market prices are used to value flows and stocks.|
2.4.2 Recording is done on an accrual basis.
2.4.3 Grossing/netting procedures are broadly consistent with internationally accepted standards, guidelines, or good practices.
|3. Accuracy and reliabilitySource data and statistical techniques are sound and statistical outputs sufficiently portray reality.||3.1 Source data—Source data available provide an adequate basis to compile statistics.||3.1.1 Source data are collected from comprehensive programs that take into account country-specific conditions.|
3.1.2 Source data reasonably approximate the definitions, scope, classifications, valuation, and time of recording required.
3.1.3 Source data are timely.
|3.2 Statistical techniques—Statistical techniques employed conform with sound statistical procedures.||3.2.1 Data compilation employs sound statistical techniques.|
32.2 Other statistical procedures (for example, data adjustments and transformations, and statistical analysis) employ sound statistical techniques.
|3.3 Assessment and validation of source data—Source data are regularly assessed and validated.||3.3.1 Source data—including censuses, sample surveys, and administrative records—are routinely assessed, for example, for coverage, sample error, and nonsampling error; the results of the assessments are monitored and made available to guide planning.|
|3.4 Assessment and validation of intermediate data and statistical outputs—Intermediate results and statistical outputs are regularly assessed and validated.||3.4.1 Main intermediate data are validated against other information where applicable.|
3.4.2 Statistical discrepancies in intermediate data are assessed and investigated.
3.4.3 Statistical discrepancies and other potential indicators of problems in statistical outputs are investigated.
|3.5 Revision studies—Revisions, as a gauge of reliability, are tracked and mined for the information they may provide.||3.5.1 Studies and analyses of revisions are carried out routinely and used to inform statistical processes.|
|4. ServiceabilityStatistics are relevant, timely, and consistent and follow a predictable revisions policy.||4.1 Relevance—Statistics cover relevant information on the subject field.||4.1.1 The relevance and practical utility of existing statistics in meeting users’ needs are monitored.|
|4.2 Timeliness and periodicity—Timeliness and periodicity follow internationally accepted dissemination standards.||4.2.1 Timeliness follows dissemination standards.|
4.2.2 Periodicity follows dissemination standards.
|4.3 Consistency—Statistics are consistent within the dataset, over time, and with other major datasets.||4.3.1 Statistics are consistent within the dataset (for example, accounting identities observed).|
4.3.2 Statistics are consistent or reconcilable over a reasonable period of time.
4.3.3 Statistics are consistent or reconcilable with those obtained through other data sources and/or statistical frameworks.
|4.4 Revision policy and practice—Data revisions follow a regular and publicized procedure.||4.4.1 Revisions follow a regular, well-established, and transparent schedule.|
4.4.2 Preliminary data are clearly identified.
4.4.3 Studies and analyses of revisions are made public.
|5. AccessibilityData and metadata are easily available and assistance to users is adequate.||5.1 Data accessibility—Statistics are presented in a clear and understandable manner, forms of dissemination are adequate, and statistics are made available on an impartial basis.||5.1.1 Statistics are presented in a way that facilitates proper interpretation and meaningful comparisons (layout and clarity of text, tables, and charts).|
5.1.2 Dissemination media and formats are adequate.
5.1.3 Statistics are released on the pre-announced schedule.
5.1.4 Statistics are made available to all users at the same time.
5.1.5 Nonpublished (but nonconfidential) subaggregates are made available on request.
|5.2 Metadata accessibility—Up-to-date and pertinent metadata are made available.||5.2.1 Documentation on concepts, scope, classifications, basis of recording, data sources, and statistical techniques is available, and differences from internationally accepted standards, guidelines, or good practices are annotated.|
5.2.2 Levels of detail are adapted to the needs of the intended audience.
|5.3 Assistance to users—Prompt and knowledgeable support service is available.||5.3.1 Contact person for each subject field is publicized.|
5.3.2. Catalogues of publications, documents, and other services, including information on any charges, are widely available.
The elements and indicators included here bring together the “pointers to quality” that are applicable across the five identified dimensions of data quality.
The elements and indicators included here bring together the “pointers to quality” that are applicable across the five identified dimensions of data quality.
The authors are grateful to Carol S. Carson, Robert Di Calogero, Claudia Dziobek, Charles Enoch, Randall Merris, and Wipada Soonthomsima for helpful comments and suggestions on an earlier draft.
See, for example, Bernanke and Mishkin (1997), Debelle and others (1998), Green (1996), Leiderman and Svensson (1995), Schaechter, Stone, and Zelmer (2000), and Svensson (1997 and 2000). A summary of the institutional framework, operational issues, and organizational implications for central banks in inflation-targeting countries is shown in Appendix 17.1.
Many of the statistical issues discussed in this chapter are of interest beyond countries with inflation-targeting regimes.
Countries that have adopted such a regime as of end-2001 include Australia, Brazil, Canada, Chile, Colombia, the Czech Republic, Hungary, Iceland, Israel, Korea, Mexico, New Zealand, Norway, Poland, South Africa, Sweden, Thailand, and the United Kingdom. Also, several other European countries—including Finland and Spain—had adopted inflation targeting before the centralization of their monetary policies in Stage 3 of European Economic and Monetary Union.
The operating target can be defined as the money market indicator that best captures the authorities’ intentions; see Schaechter, Stone, and Zelmer (2000), p. 23.
Clarity about the policy regime is also relevant to assessing the adequacy of the statistical framework.
Throughout the rest of this paper, the term “quality” is broadly defined, as discussed in more detail in the next section under the DQAF.
In an inflation-targeting regime, central banks usually have a clear mandate and the independence to carry out the necessary tasks to accomplish the monetary policy objective.
The DQAF is used in conducting country assessments in the context of the data module of the ROSC For a comprehensive list of papers on data quality issues, see IMF’s data quality reference site at http://dsbb.imf.org/dqrsindex.htm.
A specific DQAF for sociodemographic statistics is being developed by the World Bank.
The other modules are fiscal transparency, monetary and financial policy transparency, and financial sector regulation and supervision. Financial sector modules are done together with the World Bank. The World Bank conducts ROSCs on accounting and auditing, insolvency regimes, and corporate governance.
Australia and the United Kingdom conducted a self-assessment before the application of the DQAF. The Czech Republic was assessed by IMF staff, also before the application of the DQAF in the ROSC. Published ROSC data modules are available at http://www.imf.org/extental/np/rosc/rosc.asp.
The 12 remaining SDDS inflation-targeting countries target a headline price index (for example, the CPI) that corresponds with the price index disseminated on their NSDP and described on the DSBB.
The SDDS allows for some flexibility with respect to the periodicity and/or timeliness of the dissemination of two prescribed data categories (including price indices) as chosen by the subscribers. One SDDS subscriber that has adopted inflation targeting uses the flexibility option (with respect to periodicity) for price indices (see Table 17.2).
Almost all inflation-targeting countries disseminate the actual targeted price index via their national websites (see last column in Table 17.2).
Chile and Norway post metadata for forward-looking indicators on the DSBB, but do not post the indicators on their NSDP.
Mexico does not use short-term interest rates as the operating target; it uses settlement balances derived through a mechanism known as the corto.
The principles and guidelines contained in the IMF’s recently published Monetary and Financial Statistics Manual (IMF, 2000a) provide a potentially powerful tool for inflation-targeting countries in their efforts to increase the information content of their monetary and financial statistics, not least because the additional sectoral and instrument detail enhances the leading indicator properties of monetary statistics.
A methodological manual on the CPI is being developed under the auspices of the International Labor Organization, and a methodological manual on the PPI is being developed under the auspices of the IMF.
For example, a summary of best practices for the compilation of forward-looking indicators, stock market indices, and interest rate statistics could be developed. The compilation of interest rate statistics, for instance, is complicated by issues of aggregation, weighting, and sampling.
One important new statistical area for the IMF’s Statistics Department is the development of guidelines for the measurement and compilation of indicators of financial soundness. A set of core and encouraged indicators has been identified, which includes forward-looking indicators that would be useful for inflation targeting. Further information on the analytical and statistical aspects of financial soundness indicators (FSIs) is provided in Sundararajan and others (2002).