A. Forecast Design

Good forecasts have several desirable characteristics. First, good forecasts are based on all available information that is relevant for prediction, including anticipated future developments. Second, they include a point estimate accompanied by a measure of uncertainty at each point in time over the forecasting horizon. A single consolidated forecast of the inflation indicator being targeted presenting the central bank’s view would avoid much of the expectational uncertainty concerning future policy decisions and consequently would enhance central bank credibility. Third, they meet the highest professional standards. Good forecasts are prepared in a systematic way considering the contributions of information provided by up-to-date theoretical and forecasting methodologies. Finally, good forecasts are honed by expert judgment and require an institutional mechanism for incorporating such judgment in a systematic way. Inflation projections represent the central bank’s view summarizing the best collective judgment of the central bank board and are technically supported by the central bank staff’s analysis.

The central bank must produce inflation forecasts that can be both used for policy decisions and shared with the public at large. Forecasts can play two roles in an inflation targeting framework: as an input into policy decisions and as a communication tool. While one can distinguish between forecasts used by the authorities and those communicated to the market for public accountability, the inflation forecast used by central banks in making its decisions on interest rates should be the same as the one communicated to the public at large, once policy decisions have been reached. In other words, once a central bank has decided to implement inflation targeting, and hence has set its monetary policy to anchor inflation expectations, it needs to build credibility in its own inflation projection and base its decisions around it.

The central bank forecasting framework must be designed to answer a basic question concerning future inflation: What will happen with inflation (and the other variables the central bank is concerned about in the short term) under specific assumptions about the future policy stance? Answering this question requires an understanding of the transmission mechanism. The answers are likely to differ among countries and over time because the transmission mechanism (or the central bank’s understanding of the transmission mechanism) can be expected to differ and change. Yet, the basic question is likely to remain the same.

B. Division of Labor

Producing an official inflation forecast may involve many tasks and the work of a large number of people within the institution (Box 1). Significant advantages in terms of efficiency can be obtained by an appropriate division of labor, assigning tasks according to the relative strengths of individual staff members. The larger the economy and the central bank, the more feasible it is to have a more specialized division of labor. In central banks with smaller resources, the same tasks may need to be done by a smaller group of people. Table 1 presents some central banking evidence for a group of selected countries.

Table 1.

Selected Countries: Staff Involved in Forecasting Analysis

Source: National authorities.

Table 1.

Selected Countries: Staff Involved in Forecasting Analysis

	People Involved in Forecasting/ Their Qualifications	Increase (Recently) in the Number of Staff Working on Inflation Forecasting	Number of Departments Involved in Forecasting
Australia	Statement of Monetary Policy preparation involves 40 people, 2 weeks each quarter	Yes	Four
Chile	10 people; 1/3 Ph.D.s, 2/3 local Master’s degrees	Tripled since 1999	Two (Conjunctural analysis and Modeling); another department provides international scenario
Colombia	10 people, most with Master’s degrees	Yes	One (Programming and Inflation Forecasting Department); there is also a representative from the Macroeconomic Modeling Department
Czech Republic	7 (Ph.D.s with an international experience)	No	One (Monetary and Statistics Department, 4 divisions)
Peru	26 people; 14 with Master’s degrees, 4 with Ph.D.s.	Yes	One (Economic Studies Department, 8 divisions)
Turkey	18 people; 5 Ph.D.s, 10 Master’s degrees, 1 M.B.A., 2 B.A.s	Yes	One (General Directorate of Research and Monetary Policy, 2 divisions)

Source: National authorities.

View Table

Table 1.

Selected Countries: Staff Involved in Forecasting Analysis

	People Involved in Forecasting/ Their Qualifications	Increase (Recently) in the Number of Staff Working on Inflation Forecasting	Number of Departments Involved in Forecasting
Australia	Statement of Monetary Policy preparation involves 40 people, 2 weeks each quarter	Yes	Four
Chile	10 people; 1/3 Ph.D.s, 2/3 local Master’s degrees	Tripled since 1999	Two (Conjunctural analysis and Modeling); another department provides international scenario
Colombia	10 people, most with Master’s degrees	Yes	One (Programming and Inflation Forecasting Department); there is also a representative from the Macroeconomic Modeling Department
Czech Republic	7 (Ph.D.s with an international experience)	No	One (Monetary and Statistics Department, 4 divisions)
Peru	26 people; 14 with Master’s degrees, 4 with Ph.D.s.	Yes	One (Economic Studies Department, 8 divisions)
Turkey	18 people; 5 Ph.D.s, 10 Master’s degrees, 1 M.B.A., 2 B.A.s	Yes	One (General Directorate of Research and Monetary Policy, 2 divisions)

Source: National authorities.

Encouraging teamwork requires the careful design of an incentive structure for staff involved in the process. The incentive structure is especially important for the forecasting and data management teams. The authorities might find it useful to devise schemes that encourage the dissemination of information obtained by central bank staff during the normal course of their operations that could be useful for forecasting.

A forecasting team is usually defined across departmental lines with a clear mandate and responsibility. The forecasting team must combine expertise in macroeconomic theory, econometrics, and survey analysis. Along with the central bank authorities, the forecasting team will act as the key body for forming a consensus view of the monetary transmission mechanism, as most of the analytical work will be undertaken by this team. The reference point for discussions on the inflation process is typically a structural macroeconomic model, which is built by the forecasting team. Specialization in monitoring leading indicators and main sectors of the economy, such as the external sector, domestic demand, labor markets, price developments, or financial markets is warranted to develop and maintain a comprehensive and coherent macroeconomic model. The latter would require knowledge of basic time-series analysis and regular contacts with industry participants.

C. Data Monitoring and Management

A central bank should make use of all the available information that is potentially useful to assess the inflation outlook. There is no clear ranking for the most useful sources of information. In addition to the standard variables in a central bank database such as macroeconomic and financial data, important series that have been developed and widely used in recent years by central banks should also be stored in the database. These might include, for example, survey evidence.

Proper documentation of forecasting rounds is warranted. It is essential to create an archive that contains the forecasts and the analyses that undergird the forecasts. A typical database includes numbers and also data, programs, and documents that are prepared for the quarterly forecast, together with the minutes of the discussion in the monetary policy committee. Documentation of each forecasting round would assist ex post analyses to draw lessons from systematic forecasting errors and form an institutional memory for the forecasting process, thereby becoming the lynchpin of the forecasting monitoring system.

The database must be carefully maintained. The database should be regularly updated and be accessible to all members of the forecasting team and the decision committee. It is important to document all sources of data, possible problems with the series if any, and a timetable for the arrival of data. An effort should be made to obtain all the right numbers and confirm their accuracy (Carson, Enoch, and Dziobek, 2002).

It is important to store the original database used for decision making. Policy decisions are made with imperfect data that includes preliminary estimates of several important economic series, such as GDP. It is often the case that preliminary data are subject to errors and are subsequently revised, sometimes more than once. It is essential to understand the nature of such revisions and their impact on modeling and forecasting. In order to do that, some central banks started to form real-time databases that contain successive releases of data for a number of different series. Research has shown that revisions matter for forecasting. For example, Orphanides (2001) shows that historical estimates of the output gap in the United States, an important measure of economic slack, changed significantly after data revisions.

A group within the institution should be in charge of collecting data with a consistent, easily accessible and integrated view. The group should report directly to decision makers and provide the database to both the forecasting team and the members of the decision-making body. The forecasting team uses its time to make forecasts, monitor procedures, and improve its professional ability to make forecasts. It should be spared from gathering information and entering numbers into a database, but should have access to the latest data at all times.

A. Data Issues

Forecasting systems and current statistical analysis involve data issues that include decisions on appropriate target indices and supported statistics. They also entail the proper design of surveys, the use of leading indicators, and the impact of asset price movements. Some evidence for selected countries on data availability, reliability, and indices is advanced in Appendix I.

B. Short-Term Forecasting

The first step in forecasting for policy is assessing the current and near-term state of the economy. As important data including national income accounts are produced with some lag, other sources of information, such as leading indicators, surveys, or industry contacts, are used to assess the current state of the economy. Time series and other econometric techniques are primarily used for short-term forecasting. While macroeconomic theory and structural models are essential to form a view about the medium-term evolution of the economy, short-term forecasting is more of a measurement issue. Sectoral expertise and judgment is especially important for short-term forecasting.

Short-term inflation forecasts usually involve a considerable number of variables such as economic activity, aggregate demand, interest rates, and exchange rates.⁹ When forecasting inflation, many other variables, for instance, productivity, may become of interest too. It is often the case that forecasts of these intermediate variables are explicitly considered by policymakers when making decisions. It is also the case that boards at central banks and policymakers often ask for forecasts coming from a diverse group of forecasters involving a wide range of approaches.

In all cases, expert advice plays a critical role in adjusting raw econometric forecasting by combining knowledge of factors affecting relevant variables and insights on lagged structures, causal relationships, dynamics, and correlations. Better forecasts can be made by combining insights from statistical models with judgments and sectoral experts’ opinions concerning specific events. As stated in Table 2, the degree of judgment applied, either at an earlier or later stage in forecast formulation, is generally considerable.

Table 2.

On the Use of Judgment

Source: Central banks’ self-assessments as part of the underlying survey of the World Economic Outlook, September 2005.

Table 2.

On the Use of Judgment

	Degree of Judgment Applied in Forecast Formulation	Stage at which the Judgment Enters
Brazil	Considerable amount	Beginning stage
Chile	Considerable amount	Beginning stage
Colombia	Considerable amount	Beginning stage
Mexico	Small amount	Beginning stage
Peru	Considerable amount	Beginning stage
Israel	Enormous amount	Beginning stage
South Africa	Considerable amount	Final stage
Hungary	Considerable amount	Modeling stage
Poland	Considerable amount	Modeling stage
Czech Republic	Considerable amount	Beginning stage
Korea	Considerable amount	Beginning stage
Philippines	Enormous amount	Beginning stage
Thailand	Considerable amount	Modeling stage
Australia	Enormous amount	Modeling stage
Canada	Considerable amount	Modeling stage
Iceland	Considerable amount	Final stage
New Zealand	Considerable amount	Beginning stage
Norway	Considerable amount	Beginning stage
Sweden	Considerable amount	Beginning stage
Switzerland	Considerable amount	Beginning stage
United Kingdom	Considerable amount	Beginning stage

Source: Central banks’ self-assessments as part of the underlying survey of the World Economic Outlook, September 2005.

View Table

Table 2.

On the Use of Judgment

	Degree of Judgment Applied in Forecast Formulation	Stage at which the Judgment Enters
Brazil	Considerable amount	Beginning stage
Chile	Considerable amount	Beginning stage
Colombia	Considerable amount	Beginning stage
Mexico	Small amount	Beginning stage
Peru	Considerable amount	Beginning stage
Israel	Enormous amount	Beginning stage
South Africa	Considerable amount	Final stage
Hungary	Considerable amount	Modeling stage
Poland	Considerable amount	Modeling stage
Czech Republic	Considerable amount	Beginning stage
Korea	Considerable amount	Beginning stage
Philippines	Enormous amount	Beginning stage
Thailand	Considerable amount	Modeling stage
Australia	Enormous amount	Modeling stage
Canada	Considerable amount	Modeling stage
Iceland	Considerable amount	Final stage
New Zealand	Considerable amount	Beginning stage
Norway	Considerable amount	Beginning stage
Sweden	Considerable amount	Beginning stage
Switzerland	Considerable amount	Beginning stage
United Kingdom	Considerable amount	Beginning stage

Source: Central banks’ self-assessments as part of the underlying survey of the World Economic Outlook, September 2005.

Sectoral expertise and judgment play an important role in some central banks for short-term forecasting. A macroeconomic forecast based on judgment is built up in a fully coordinated way from sectoral forecasts provided by sectoral experts. This is a full macroeconomic forecast as an alternative or a complement to a model-based one. To start the process, a forecast coordinator sets up assumptions about variables that are common but exogenous to each sector. Each sectoral expert then comes up with his or her own forecast, using a variety of techniques, including econometric models of their sector, surveys, anecdotal evidence, and his or her own judgment of the impact of developments that cannot be captured by other tools. These forecasts are then aggregated, and additional iterations are done to ensure consistency of the macroeconomic framework.

The advantage of this approach is the incorporation of specific information about the current quarter and the near future. A key disadvantage is the difficulty of analyzing alternative scenarios under different assumptions. Often, it is too difficult to coordinate the aggregation process for anything other than the central projection. There are alternative uses of judgmental forecasts. They can either be used as the main forecast for the next few quarters, and feed into a structural model for beyond, or be used as an alternative to model-based forecasts with a view to reconcile the two.

Storing experts’ opinions and advice in a systematic way may complement the statistical analysis of forecasts, especially in the wake of structural changes or unexpected events. Experts’ opinions and judgments may be stored in databases for future use to improve the quality of forecasts. Short-term forecasting is usually based on time series analysis, whether univariate or multivariate, and might be enhanced by incorporating key indicators of construction, price changes, information on exchange rate pass-through effects, and financial sector developments.

Econometric models are used to inform and complement expert knowledge and judgment. The simplest econometric tools used in time series forecasting are univariate linear models. Time series models impose less economic structure. However, they provide accurate short-term forecasts and a good reference for consistency checks for larger structural models.¹⁰ Within this set, ARIMA models became the customary device for time series analysis. In the ARIMA specification, a variable of interest (for instance, CPI) is modeled, exclusively, as a function of its own past and hence is governed by the autocorrelations of the process. Sometimes, simple random walk is the best possible univariate representation. However, this modeling approach does not incorporate useful information about the structure of the economy and thus becomes useless as a vehicle to discuss, explain, and elucidate policy decisions.

Multivariate modeling strategies involve models in which the variable of interest depends on its own past values and on those of other accompanying variables. In this venue, it is important to adequately distinguish between statistical specifications based on endogenous forecasts of the dependent variable and those that require the use of exogenous forecasts of the explanatory variables. The first approach has been popularized by the VAR (vector autoregression) methodology, which seems particularly useful for that purpose. The second approach involves specifying a model in which the forecast of the dependent variable relies on other explanatory variables rather than its own past and uses exogenous forecasts— computed outside the model—as explanatory variables. Some VAR models combine the two approaches by including exogenous variables in their specifications.

In addition to macroeconometric (core) models, central banks might be willing to use Output Gap Models (OGMs) or Phillips Curve types of models. These models relate nominal price or wage inflation to some measure of excess demand or real disequilibrium (unemployment or output gap). In many instances, given their simplicity and transparency, OGMs are used to cross-check forecasts on wage/prices derived from core models. They can also be used to calibrate parameters of structural models. The focus on the relationship between inflation and summary measures of real disequilibrium make them useful for policy simulation exercises. Nevertheless, simplicity comes at the expense of omitting other important variables.

While the previous techniques provide an understanding of the statistical behavior of key variables, they are routinely complemented with medium-term forecasting procedures that usually involve assumptions about the structure of the economy and with more general econometric models. In this regard, macroeconomic models provide consistency by linking short-term economic and financial developments with long-term assessments. A structural approach to forecasting depicting a comprehensive view of economic performance would typically include an open economy IS curve, a Phillips curve, an international asset market equilibrium condition, and a monetary policy reaction function.¹¹ All these techniques have been used in empirical work in central bank behavior, in modeling monetary policy rules, and in analyzing sacrifice ratios. At the same time, these elements constitute the core of some large macroeconometric models such as the Bank of Canada’s ToTEM introduced in December 2005, and the U.S. Federal Reserve Board’s FRB/U.S. model, among others.

Different models used at central banks are considered as complements, not competitors. Table 3 shows a range of models and equations used to build models for forecasting and economic analysis at selected central banks. Bank of England (1999) provides an extensive discussion on the use of different models for forecasting and policy analysis, including detailed explanations of pros and cons of different types of models that the Bank of England employs.¹²

Table 3.

Expectations, Equations, and Models

Source: Central banks’ self-assessments as part of the underlying survey of the WEO, September 2005.

Table 3.

Expectations, Equations, and Models

	Expectations Adaptive/Forward-Looking	Number of Behavioral Equations in a Core Model	Number of Models (if a suite of models used)
Brazil	A mix of the two	2	3
Chile	A mix of the two	10	15
Colombia	A mix of the two	6	30
Mexico	A mix of the two	9	3
Peru	A mix of the two	4	8
Israel	A mix of the two	10	2
South Africa	Adaptive	28	6
Hungary	Adaptive	17	3
Poland	No core model used	..	2
Czech Republic	A mix of the two	11	..
Korea	Adaptive	50	3
Philippines	Adaptive	45	2
Thailand	A mix of the two	24	1
Canada	Forward-looking	45	2
Iceland	Adaptive	15	3 to 5
New Zealand	A mix of the two	25	3
Norway	A mix of the two	4	15
Sweden	Forward-looking	25	3 to 5
Switzerland	Adaptive	30	5

Source: Central banks’ self-assessments as part of the underlying survey of the WEO, September 2005.

View Table

Table 3.

Expectations, Equations, and Models

	Expectations Adaptive/Forward-Looking	Number of Behavioral Equations in a Core Model	Number of Models (if a suite of models used)
Brazil	A mix of the two	2	3
Chile	A mix of the two	10	15
Colombia	A mix of the two	6	30
Mexico	A mix of the two	9	3
Peru	A mix of the two	4	8
Israel	A mix of the two	10	2
South Africa	Adaptive	28	6
Hungary	Adaptive	17	3
Poland	No core model used	..	2
Czech Republic	A mix of the two	11	..
Korea	Adaptive	50	3
Philippines	Adaptive	45	2
Thailand	A mix of the two	24	1
Canada	Forward-looking	45	2
Iceland	Adaptive	15	3 to 5
New Zealand	A mix of the two	25	3
Norway	A mix of the two	4	15
Sweden	Forward-looking	25	3 to 5
Switzerland	Adaptive	30	5

Source: Central banks’ self-assessments as part of the underlying survey of the WEO, September 2005.

C. Medium-Term Forecasting—The Core Model

A core (semi)structural model with strong theoretical foundations is a reference point for discussions about inflation dynamics. Industrial economies have relied on large-scale macroeconomic models for many years as a guide for their decision-making processes in monetary policy. It is now the case that several emerging market economies have successfully developed quantitative models that also guide their decision-making process. Table 4 presents some evidence for a selected group of countries on core macro and other models. The core model represents the central bank’s view of the monetary transmission mechanism. Such a model usually consists of several behavioral equations in which important variables converge to a well-behaved steady state. By construction, the model is internally consistent, which helps the bank to provide a coherent story about how it sees the economy evolving. In that capacity, the model is also a communication device.

Table 4.

Forecasting Skills and Resources

Source: Central banks’ self-assessments as part of the underlying survey of the WEO, September 2005.

Table 4.

Forecasting Skills and Resources

	Inflation Forecast
	Single Core Macro Model	Suite of Different Models	Other
New Zealand	Single core model for medium-term projections		Satellite models for short- term forecasting and other models used to produce competing forecasts (e.g., VARs, Factor Models)
Norway	Single core macro model		Core model, satellite models and empirical models
Sweden	Core Dynamic Stochastic General Equilibrium Model	A suite of different models	Bayesian VARs, a Component CPI model
Switzerland		i) Structural econometric models; ii) Nonstructural and structural VARs; and iii) An M3 Vector Error Correction Model
United Kingdom		A suite of models, with a large weight placed on the Bank of England Quarterly Model (BEQM), a core model

Source: Central banks’ self-assessments as part of the underlying survey of the WEO, September 2005.

View Table

Table 4.

Forecasting Skills and Resources

	Inflation Forecast
	Single Core Macro Model	Suite of Different Models	Other
New Zealand	Single core model for medium-term projections		Satellite models for short- term forecasting and other models used to produce competing forecasts (e.g., VARs, Factor Models)
Norway	Single core macro model		Core model, satellite models and empirical models
Sweden	Core Dynamic Stochastic General Equilibrium Model	A suite of different models	Bayesian VARs, a Component CPI model
Switzerland		i) Structural econometric models; ii) Nonstructural and structural VARs; and iii) An M3 Vector Error Correction Model
United Kingdom		A suite of models, with a large weight placed on the Bank of England Quarterly Model (BEQM), a core model

Source: Central banks’ self-assessments as part of the underlying survey of the WEO, September 2005.

Macroeconomic modeling encompasses a minimum set of requirements such as an exchange rate arrangement consistent with the inflation objective and the characterization of monetary policy consistent with the inflation target. Small, semi-structural, New-Keynesian representations of economic fluctuations consist of multiple equations representing a set of long-run structural relationships, which is usually supplemented by identities, transformations, and linking equations. The strengths of these models rest in their much richer account of the economic fundamentals than smaller-scale models. In addition, and given knowledge of the kind of shocks to which an economy is exposed, macroeconometric models can be run and calibrated on a set of different alternative assumptions. However, as in any large set of equations involving so many different behavior relationships, macroeconometric models tend to be very complex.

The forecasting framework should produce a forecast based on a structural picture of the transmission mechanism. In this regard, methodologies are usually chosen according to the specificity of the transmission mechanism and they need to provide a consistent medium-term horizon for assessing policy options. The parameters of structural forecast models can be calibrated, most importantly in countries where data sets are small and structural breaks prevail. A forward-looking transmission mechanism incorporates the following elements:

• Assessing the impact of real interest rates on domestic absorption (at higher frequency level to be incorporated in models of inflation).

• Determining whether a credit channel exists either as a bank credit channel which relies on banks as holders of deposits and originators of loans or as a broader approach focusing on the supply of funds from all financial intermediaries (including all forms of external finance).

• Evaluating the pass-through effect of the exchange rate to inflation (is there a sufficiently ample time series for the exchange rate?).

• Analyzing different types of monetary disturbances: permanent monetary policy shocks to be distinguished from temporary shocks; and velocity, financial intermediation shock, and money demand shock, for instance.

A methodology for combining core models with expert judgment has recently been advanced at the IMF.¹³ The starting point is a small core macroeconomic model that establishes aggregate relationships between the main macroeconomic variables (in terms of deviations from equilibrium levels) in the medium term. The core model, which includes a monetary policy reaction function, allows the formulation of model-consistent forecasts of the impact of monetary policy decisions on inflation and identifies the risks to the inflation forecasts in terms of shocks affecting the economy. The expert judgment involves the calibration of model parameters, the assumed equilibrium path of the main macro variables, and exogenous inputs into the model, partly reflecting the evolution of variables outside the model (most notably fiscal variables in the simplified version of their model).

The core model should be transformed into a functional tool for the decision-making process. As such, it should be used as a vehicle for policy analysis in board meetings and as an underlying device in inflation reports. Given that the core model should be adequately understood by the forecasting team throughout a central bank, it also becomes the familiar locus around which information is exchanged, the debate takes place, and consistency is safeguarded.

D. Involving the Private Sector and Other External Expertise

Involving the private sector in the forecasting exercise can enhance trust in the inflation targeting framework. The inflation targeting approach to monetary policy relies on private sector trust. The private sector must be convinced that the central bank will adjust policies when inflation forecasts deviate from the targeted point or range. To boost confidence, the private sector may be involved in the production of inflation forecasts and participate in central bank-sponsored fora for discussing the best inflation forecasting techniques available and the judgmental factors that need to be considered. By directly participating in the process, the private sector can be reassured that predictable inflationary pressures will not go undetected. The private sector may also increase its awareness of the uncertainty in making forecasts and the need for judgment even after using the most sophisticated techniques.

Private sector entities may assist the central bank in producing its inflation forecast. Many private sector individuals and institutions in the academic and research communities are prospective partners in the production of inflation forecasts. They include economic think tanks, professional forecasting associations, and university students and professors, among others. The central bank may periodically open its forecasting methodology for review by reputable experts. In particular, the central bank may commission an independent study from the private sector or reputable international experts assessing the forecasting systems and methodologies employed by the central bank in forecasting inflation. These assessments regularly include an opinion about the procedures for judgmental adjustments to the forecasts from a benchmark forecasting model.

The private sector may also provide alternative forecasts for comparison. The central bank could create an electronic billboard where alternative inflation forecasts from the private sector are centralized, including forecasts produced with alternative methodologies and a survey of inflation expectations. For encouraging responsible forecasts, the central bank billboard should keep track of the forecasting accuracy of the contributors, including its own. The central bank could then compare the accuracy of its predictions not only with those of other local forecasters, but also with other inflation targeting central banks.¹⁴ The private sector would benefit from the advertisement received from this platform. For-profit organizations with a successful track record in forecasting may benefit by attracting clients. University students would advertise their professional services or academic expertise. Moreover, the central bank may offer privileged access to readily usable databases to its partners in forecasting. The detailed databases could allow central bank partners to provide detailed forecasts to their clients on microeconomic aspects in which the central bank might not be interested.¹⁵ The billboard may alert the central bank of new developments in forecasting techniques and benefit from a variety of points of view. In addition, as proposed in the discussion about databases, the central bank may conduct surveys of inflation expectations among the main actors in the financial and corporate sectors and publish the results on its Web site (Bank of England, Central Bank of Brazil, Banco de México, Reserve Bank of New Zealand, Kyrgyz Republic, among others, do this).

The central bank may establish a forum for discussing inflation forecasting techniques. Among the possible activities, it could organize a yearly conference on inflation targeting open to the public at large, but focused on attracting participants from economic think tanks and universities. To increase participation, reputable presenters may be invited and the central bank may choose to publish the best papers. The central bank could also organize periodic meetings for discussing with selected representatives of the private sector (by invitation only) the implications of new data releases on inflation prospects. The frequency of these meetings may coincide with that of the forecasting cycle. A selection of participants may include the main partners of the central bank in forecasting inflation. The central bank may establish a research agenda that supports its policy and operational decisions, providing the data to its partners in forecasting.

Involving the private sector in forecasting may involve some costs and risks. Investors may use any inside information revealed through interaction with the private sector for their own profit. Forecasts reveal what policymakers believe. In studying central bank forecasts, investors may detect weaknesses in the authorities’ forecasting framework and use this information for their own benefit. Some central banks may feel that they do not significantly benefit from private sector involvement as the central bank often has privileged information and attracts high-caliber personnel. Some country authorities may also be concerned about revealing their weaknesses in forecasting and exposing themselves to personal embarrassment, which may reduce (rather than increase) the credibility of the regime. The authorities may fear that the public at large may attribute to incompetence the natural difficulties in forecasting.

The net benefit of private sector involvement is likely to vary over time and among countries. The benefits of private sector involvement are difficult to measure because they are associated with the psychological effects of higher credibility. Yet, these effects can be strong and can help anchor inflation expectations. The private sector abuse of privileged information may reflect deficiencies in the transparency framework rather than in the monetary regime. Transparency is a lynchpin of the inflation targeting framework and the authorities might find it useful to reveal information about their policy actions simultaneously to all market participants. Thus, central bank partners in inflation forecasting would not get access to information about policy decisions not already available in the market. Moreover, the administrative costs of coordinating the effort are likely to be small compared to the benefits of increased private sector trust. These costs, however, are likely to vary by country and over time.

Involving the private sector requires the central bank to choose, carefully, the timing of different strategies. For example, it should have already prepared a solid forecasting methodology, perhaps initially with the support of external expert advice, before inviting the private sector to prepare alternative forecasts for comparison with those of the central bank in the central bank’s Web site.

E. How Does It All Fit Together?—The Decision-Making Process

Synthesizing all the information and reaching a policy decision is an important element of the art of central banking. There is no single best or common approach to reach the policy decision; each central bank has its own way. The important point is to institutionalize the decision making process. Fortunately, in recent years some central bankers published the details of their decision-making process to shed light on the internal workings of their respective institutions.¹⁶

A reliable official forecast is based on all available forecasting tools. It should not be based only on the predictions of a core model. It should translate a mixture of statistics, model predictions, institutional knowledge, economic theory, and the judgment of the forecasting team into a wide-ranging, reliable, and understandable chronicle of the inflation outlook that would serve as a nexus between the central bank’s policy decisions and goals and external communication. The essential aspect is to have a structured framework that allows the central bank to think systematically about the monetary transmission mechanism and produce inflation forecasts.

Many central banks have a regular schedule for policy meetings and announcements. There are several benefits to doing so. First, a schedule provides a regular opportunity for the central bank to explain its views and actions to the public, and to draw attention away from daily news toward longer-term fundamental developments in the economy. Second, with regular meetings, the central bank communicates with the public even when it does not change the policy variable. A decision not to change the policy variable rate is often as important as a decision to do so, and warrants an explanation. For example, when several minor shocks hit the economy that on balance do not require a change in the policy stance, the public may still benefit from the bank’s assessment of the balance of forces that influence the inflation process in the country. Third, fixed announcements reduce the uncertainty about when the bank will inform the public about any change in the policy rate. Elimination of this risk would encourage financial market participants to shift their focus away from the central bank, and toward economic developments, and to plan ahead of meetings.

A regular schedule is useful for operational purposes as well. A detailed timetable is essential for effective coordination of the forecasting process, as it specifies the timing of the required actions, inputs, and outputs and hence puts a structure on the process. A quarterly forecasting cycle in line with the natural accretion of data is common in many central banks. Each quarter, staff responsible for the inflation forecast could present their forecasts to a technical committee or monetary policy committee, discuss scenarios under different assumptions about exogenous variables, and assess risks and uncertainties in the forecast. The timing of quarterly projections is generally synchronized with the publishing of an inflation report. An interim assessment of the inflation outlook can be done four or six weeks after a quarterly forecasting cycle, but it is important that frequent analysis does not absorb time from the more thorough quarterly reviews and detailed analyses.

Interim meetings

The meetings in between the quarterly forecasting cycle involve reviewing progress and analyzing the new information that has accumulated. Accordingly, the monetary policy committee takes into account all the relevant information for assessing its policy stance, but it is more likely that major changes in the policy stance overlap with the quarterly cycle. Because a change in the monetary policy stance is executed in small steps (the so-called principle of gradualism), the policy rate is likely to be changed consistently in one direction in a series of meetings. There is less new information at the interim meetings owing primarily to the lack of new national income accounts data, and so short-term forecasts have a greater weight in interim assessment compared to the quarterly cycle. The interim meetings could also be used to discuss the approach to monetary policy in general.

Gomez, Uribe, and Vargas (2002) explain the process behind the forecasting procedure at the Banco de la República de Colombia. The inflation forecasting system (Figure 1) consists of a forecasting model (MMT) used for policy analysis, a combination of Structural (single equation) Models (CSM), a model of food inflation by type of product, a neoclassical growth model used for forecasting potential output and for analysis, a supply-side output forecasting system (OFS), and a set of auto-regressive models. The monthly forecasting procedure starts with the release of statistics on inflation used to run the neoclassical growth model, the auto-regressive models, and the food inflation model. The results are subsequently evaluated and used as input to the CSM and the OFS. Then, in the second forecasting round, the resulting output is used to feed the adjustments and the asymmetry of the inflation and output fan charts. The forecasting process takes a week until the inflation report is presented to the board of the Banco de la República. The projections for output, inflation, and interest rates from the MMT are also used as inputs for estimating of the demand of monetary base.

Colombia: The Inflation Forecasting System

Citation: IMF Working Papers 2006, 122; 10.5089/9781451863826.001.A001

Source: Gomez, Uribe, and Vargas (2002).

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Colombia: The Inflation Forecasting System

Citation: IMF Working Papers 2006, 122; 10.5089/9781451863826.001.A001

Source: Gomez, Uribe, and Vargas (2002).

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• Download figure as PowerPoint slide

Colombia: The Inflation Forecasting System

Citation: IMF Working Papers 2006, 122; 10.5089/9781451863826.001.A001

Source: Gomez, Uribe, and Vargas (2002).

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• Download figure as PowerPoint slide

A. Communicating the Forecast

Inflation forecasts are the centerpiece of any monetary policy framework, especially in inflation targeting countries. Deviations from the inflation target can become a signal for adjustment of inflation expectations. Consequently, the credibility of any central bank and the success of the monetary policy strategy hinges on the quality and reliability of its inflation forecast.

The need for legitimacy induces an inflation targeting central bank to continually demonstrate that it owns the expertise, resources, and instruments to achieve the goal of price stability. It is in this setting that a central bank must communicate clearly how its goals are to be achieved. Given that the target audience is the entire society, communication devices and their characteristics must be tailored accordingly. In this regard, the monetary authority must spell out not only its goals, but the strategy, its justification and rationale, and the way by which resources—both human and technical—will be used to achieve those goals. Effective and timely communication may strengthen the institutional independence of central banks and enhance the effectiveness of monetary policy.

Accountability induces the monetary authority to communicate clearly what the objectives are, thereby suggesting that the publication of inflation forecasts is crucial to an efficient monitoring process. One of the main features of such communication is the analytical framework and the statistical support infrastructure used to generate inflation forecasts. The predictability of policy actions can be enhanced by adequate communication of the analytical tools—models, statistics, information, and framework—used. The objective is to communicate appropriately so that the private sector can replicate the exercise and obtain similar inflation forecasts as the central bank.

Timely communication of an inflation forecast enhances the clarity of the decision-making process and the capacity of the public to comprehend and appraise the performance of a central bank. Inflation reports are the proper vehicles for sharing the value judgments used by the central bank and for understanding the reactions by the monetary policy committee members whenever deviations from the inflation forecasts take place. The production of reliable forecasts is a learning-by-doing procedure by which a central bank aims at building up a consistent track record. In doing so, central banks usually opt to compare their own inflation forecasts against others produced by the private sector and the market, explaining and interpreting past forecast errors.

Communication becomes crucial to increasing transparency. However, the release of more information does not automatically imply more transparency. It is vital to gauge the information to be released. In this regard, there are key elements of the forecast process that central banks may wish to share with the market. The central bank must provide enough information to let market participants understand and monitor its decisions in an effort to convince them that it behaves in a way consistent with its commitment to medium-term inflation target. Likewise, effective communication and transparency enhance the central bank’s ability to build up and reinforce credibility. A central bank may gain credibility by sharing information with the market. Timely provision of information concerning both forecasts and the model behind them may foster credibility by allowing the public to assess policy consistency. Table 5 shows how inflation targeting central banks have been gradually adding information into their inflation reports.

Table 5.

Forecasts and Fan Charts

Source: Roger and Stone (2005).

Table 5.

Forecasts and Fan Charts

	Quantitative Forecast				Fan Chart
	1998	2000	2002	2004	1998	2000	2002	2004
New Zealand	Yes	Yes	Yes	Yes	No	No	No	No
Canada	Yes	Yes	Yes	Yes	No	No	No	No
United Kingdom	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Sweden	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Australia	Yes	Yes	Yes	Yes	No	No	No	No
Israel	No	No	Yes	Yes	No	No	Yes	Yes
Czech Republic	Yes	Yes	Yes	Yes	No	No	No	No
Poland		No	Yes	Yes		No	No	Yes
Brazil		Yes	Yes	Yes		Yes	Yes	Yes
Chile		Yes	Yes	Yes		Yes	Yes	Yes
Colombia		Yes	Yes	Yes		No	Yes	Yes
South Africa			No	No		…	Yes	Yes
Thailand	Yes	Yes	Yes	Yes		Yes	Yes	Yes
Korea			Yes	Yes			Yes	Yes
Mexico			Yes	Yes			No	No
Iceland			Yes	Yes			Yes	No
Norway			Yes	Yes			Yes	Yes
Hungary		No	Yes	Yes		No	Yes	Yes
Peru			Yes	Yes			Yes	Yes
Philippines			Yes	Yes			No	Yes

Source: Roger and Stone (2005).

View Table

Table 5.

Forecasts and Fan Charts

	Quantitative Forecast				Fan Chart
	1998	2000	2002	2004	1998	2000	2002	2004
New Zealand	Yes	Yes	Yes	Yes	No	No	No	No
Canada	Yes	Yes	Yes	Yes	No	No	No	No
United Kingdom	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Sweden	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Australia	Yes	Yes	Yes	Yes	No	No	No	No
Israel	No	No	Yes	Yes	No	No	Yes	Yes
Czech Republic	Yes	Yes	Yes	Yes	No	No	No	No
Poland		No	Yes	Yes		No	No	Yes
Brazil		Yes	Yes	Yes		Yes	Yes	Yes
Chile		Yes	Yes	Yes		Yes	Yes	Yes
Colombia		Yes	Yes	Yes		No	Yes	Yes
South Africa			No	No		…	Yes	Yes
Thailand	Yes	Yes	Yes	Yes		Yes	Yes	Yes
Korea			Yes	Yes			Yes	Yes
Mexico			Yes	Yes			No	No
Iceland			Yes	Yes			Yes	No
Norway			Yes	Yes			Yes	Yes
Hungary		No	Yes	Yes		No	Yes	Yes
Peru			Yes	Yes			Yes	Yes
Philippines			Yes	Yes			No	Yes

Source: Roger and Stone (2005).

Fan charts, exhibiting the probability distribution of inflation forecasts over a range of outcomes have become a prevalent tool to present forecasts based on quantitative models.¹⁷

Disclosing the inflation forecast has many benefits. If the central bank promises to adjust interest rates when the inflation forecast is to be out of the range, the market would want to see this forecast. The advantages of publishing forecasts, in general, include: (i) increasing the public understanding of central bank actions, and consequently, its ability to assess the credibility of the monetary authority to control inflation; and (ii) improving the quality of these forecasts because they are under scrutiny by the public. Therefore, besides higher accountability, the public release of inflation forecasts is crucial to influence public expectations and enhance the effectiveness of central bank decisions. In addition, the credibility of the inflation forecast is supported by (i) a transparent and clear transmission of the forecast and its assumptions; (ii) a monetary policy stance consistent with the inflation forecast in the policy horizon; and (iii) an inflation forecast in the policy horizon in close proximity to the inflation target.

There are several means of communicating the inflation forecast and its assumptions. In this regard, inflation reports have emerged as an efficient method to convey the necessary information on inflation forecasting to the public at large. These reports not only facilitate the necessary transparency but also provide relevant information at the exact time to enhance the predictability of central banks. Additionally, most inflation targeting central banks provide to a more specialized audience detailed quantitative aspects and formal characteristics of the analytical process used to derive the inflation forecast. Specific communication is usually transmitted through the publication of research pieces, conferences, and speeches that help shed further light on the statistical procedures, assumptions, and methods.

B. Conditional or Unconditional Forecast?

The assumed path of policy interest rates over the forecasting period is crucial in preparing an inflation forecast. In unconditional forecasting, no explicit assumption is made about a specific interest rate path, while in conditional forecasting the forecast is based on a specific interest rate path. A basic premise of the inflation targeting framework is that the central bank can affect inflation by modifying its short-term policy interest rates. This also implies, however, that the future evolution of policy interest rates affects the inflation forecast and that the conditional forecasts are to be preferred.

Central banks have considered four types of explicit interest rates paths: (i) a constant interest rate path; (ii) market forecasts of the future policy rates; (iii) a central bank discretionary projection of the policy interest rate path; and (iv) the interest rate path that would result from following the central bank’s policy reaction function.

A constant interest rate path is appealing, but may be misleading. As reported in Table 6, the evidence shows the existence of different approaches to this issue. An inflation forecast under constant interest rates answers the question of what inflation would look like over the forecasting period in the absence of any policy action of the central bank for the indefinite future. While it can provide a useful benchmark, a constant interest rate path is inappropriate because it could imply unreasonably high or low real interest rates based on the projected evolution of inflation. Future projected changes in interest rates will be necessary to keep inflation on the target path. Presenting forecasts based on unreasonable assumptions does not contribute to the objectives behind making the forecasts available.

Table 6.

Selected Countries: Assumptions, Publication, and Communication

Source: National authorities.

Table 6.

Selected Countries: Assumptions, Publication, and Communication

	Assumptions on Inflation Forecasts	Publication of Cross-Market Comparison of Inflation Projections/Use of Inflation Expectations Surveys (IES)	Publication of Inflation Forecasting Models/ Internet Access	Revision of Inflation Forecasts/ Is the Inflation Forecast Horizon Constant?	Board Influence on Preparation of Inflation Projections/ Who Participates in the Discussions on Inflation Forecasting?
Australia	Constant interest/exchange rates	No/Monitor IESs	No/Available on request	8 times a year/..	No/Staff
Chile	Interest rates based on forward curve	Yes/Yes	Yes/Yes	3 times a year/Yes	Yes/Board and staff
Colombia	Interest rates based on policy rule	No, but publish a summary of market projections/Monthly and quarterly IES’s	Yes/Available on request	Monthly updates and a major revision every quarter/Yes, 8 quarters	Yes (as an alternative to the base scenario)/ Technical staff and representatives from the ministry of finance
Czech Republic	Interest rates based on policy rule and the yield curve; exchange rates based on uncovered interest rate parity approach and backward- and forward-looking elements of FX market	No, but use for internal purposes/Yes	Yes/Yes (but not the databases)	Quarterly/Yes, 8 quarters (a detailed time schedule is used)	Yes/Board and staff
Peru	Constant interest rate, or a path that corresponds to a future inflation target	No/Use surveys as an indicator Variable	Yes/Yes	3 times a year for published forecasts and monthly for internal/ One or two calendar years	No/Board and staff
Turkey	Interest rates based on policy rule, and constant interest rate.	No, only internal comparisons are done/Yes	No/Yes	Quarterly/Yes. 6 quarters.	Yes/Board and staff.

Source: National authorities.

View Table

Table 6.

Selected Countries: Assumptions, Publication, and Communication

	Assumptions on Inflation Forecasts	Publication of Cross-Market Comparison of Inflation Projections/Use of Inflation Expectations Surveys (IES)	Publication of Inflation Forecasting Models/ Internet Access	Revision of Inflation Forecasts/ Is the Inflation Forecast Horizon Constant?	Board Influence on Preparation of Inflation Projections/ Who Participates in the Discussions on Inflation Forecasting?
Australia	Constant interest/exchange rates	No/Monitor IESs	No/Available on request	8 times a year/..	No/Staff
Chile	Interest rates based on forward curve	Yes/Yes	Yes/Yes	3 times a year/Yes	Yes/Board and staff
Colombia	Interest rates based on policy rule	No, but publish a summary of market projections/Monthly and quarterly IES’s	Yes/Available on request	Monthly updates and a major revision every quarter/Yes, 8 quarters	Yes (as an alternative to the base scenario)/ Technical staff and representatives from the ministry of finance
Czech Republic	Interest rates based on policy rule and the yield curve; exchange rates based on uncovered interest rate parity approach and backward- and forward-looking elements of FX market	No, but use for internal purposes/Yes	Yes/Yes (but not the databases)	Quarterly/Yes, 8 quarters (a detailed time schedule is used)	Yes/Board and staff
Peru	Constant interest rate, or a path that corresponds to a future inflation target	No/Use surveys as an indicator Variable	Yes/Yes	3 times a year for published forecasts and monthly for internal/ One or two calendar years	No/Board and staff
Turkey	Interest rates based on policy rule, and constant interest rate.	No, only internal comparisons are done/Yes	No/Yes	Quarterly/Yes. 6 quarters.	Yes/Board and staff.

Source: National authorities.

Market forecasts of future policy rates could give rise to circularity if the central bank sets its policy rate on the basis of market forecasts.¹⁸ Expectations can become self-fulfilling if the central bank sets its policy rate on the basis of market forecasts. Yet, if the central bank does not plan to always validate those forecasts, it may be misleading the market by basing its inflation forecasts on the market projections.

A central bank discretionary projection of the policy interest rate path implies a forward-looking approach to monetary policy. However, it would complicate the decision-making process of the committee that makes monetary policy decisions. If a policy projection is used for producing the inflation forecast, this would require that the monetary policy committee come to an agreement on this path. Goodhart (2001), and more recently Mishkin (2004), argue that it is better that central banks decide only the present policy rate and not the policy interest rate path.

The use of a policy reaction function for the production of inflation forecasts is a useful-approximation to the forward-looking behavior of the monetary policy committee. A policy reaction function would identify a particular path of interest rates given the expected developments in the economy, based on the likely behavior of the decision maker in response to shocks to the economy. While it is unlikely that country authorities will follow a rule in making their decisions, they are likely to make their judgmental decisions on the basis of applying a heavier weighting to the variables that have been historically relevant.

We argue that the best, most consistent, and technically sound option for a professional inflation forecast is the use of a policy reaction function. Having evaluated the pros and cons of these options, staff from a central bank might find it useful to forecast inflation considering a policy reaction function. This, however, requires that it be discussed and analyzed with the authorities to check whether they can agree with the historical reaction in the policy stance and current objectives and their relative weights. This will avoid significant differences between staff forecasts and the authorities’ view on the interest rate path.

However, the central bank need not disclose the underlying policy interest rate path. On the one hand, there is the view held by Svensson (2002) that central bank inflation forecasts should be complemented with the public release of the projected policy interest rate path. He argues that if inflation forecasts are based on the authorities’ policy interest rate path, it is very likely that the market would demand those forecasts or try to estimate them. On the other hand, releasing the associated future policy interest rate path implicit in the central bank inflation forecast might complicate communication with the public. If the interest rate path is forecasted, future deviations from the projected policy path may be viewed by the public as central bank failure and could harm the central bank’s credibility. A way to combine these two opposite views is to release the expectations of the market regarding interest rates and to send official message about the future policy stance. In this way, central banks do not need to be specific on the policy path (avoiding a loss of credibility because of unexpected adjustments), but can express the general trend of their policy depending on the inflation forecast and the factors that could deviate from the inflation target.

Almost all inflation targeting central banks publish their inflation forecasts, but almost none discloses projections of the future path for the interest rate policy instrument (or projections for output).¹⁹ All inflation targeting central banks have engaged in publication of inflation reports. These reports describe and explain the goals of monetary policy, the numerical values of the inflation targets, projections of inflation and other relevant variables, risks to achieve the targets, etc. Most of the projections are based on the assumption of a constant interest rate.

Notwithstanding that inflation forecasts are usually publicly released, projections of the policy interest rate path remain private to the central bank. Following the practice of several inflation targeting countries, it would be advisable to simply focus on the inflation forecast and its target and not on the policy rate path. The latter might complicate the communication process and the central bank’s accountability.

C. Improving the Framework over Time

Some central banks opened their doors to independent reviews. The Bank of England invited Donald Kohn of the Board of Governors of the Federal Reserve System, who was then the Director of Monetary Affairs, for a review of the MPC procedures. Kohn was asked to assess the material presented to the MPC, the work of the staff team that provides these materials, the efficiency and effectiveness of the forecast process, and the contribution of the inflation report to enhancing the transparency of monetary policy. Kohn’s report and the staff’s response to it were later published (Bank of England, 2001). Later on, the Bank of England invited Prof. Adrian Pagan, a noted econometrician and a former Reserve Bank of Australia Board Member, for a review of its modeling and forecasting techniques (Bank of England, 2003).

The Government of New Zealand initiated a comprehensive review of New Zealand’s monetary policy framework in May 2000. Professor Lars Svensson’s report, its terms of reference, several supporting documents, and reactions were published on the Reserve Bank of New Zealand Web site.²⁰ In Norway, the Centre for Monetary Economics²¹ (CME) each year asks a committee of independent economists to assess Norges Bank’s monetary policy. The annual CME reports not only evaluate the decisions taken by the Norges Bank in the preceding year, but also address some specific issues related to the monetary policy framework. For example, the 2004 and 2005 reports contain comments on the role of exchange rates under inflation targeting, on the new calibrated macroeconomic model of the bank, and on escape clauses.

Flexibility of the forecasting framework is required to allow the use of different techniques over time. The forecasting team’s understanding of the transmission mechanism is likely to change with innovations in theory, data management techniques and instruments, and, of course, structural changes in the economy that affect the transmission mechanism. The forecasting team is also likely to learn from its mistakes. The forecasting methodology must be flexible enough so as to benefit from lessons learned by doing.