Sources and methods of the OECD Economic Outlook

An important starting point in the forecasting process is the re-assessment of the economic climate in individual countries and the world economy as a whole. Here, a combination of model-based analyses and statistical indicator models play an important role in "setting the scene" at the start of each projection round.

A first step is to look at the range of relevant new information since the last projections were produced - such as changes in commodity prices (in particular the oil price), exchange rates and interest rates, fiscal trends, the path of economic activity and other key variables – to see how the recent past has developed differently from what was previously expected. With this new information, and using the previous set of projections as a starting point, the effects of the new elements and revised judgments are typically assessed on the basis of model simulations using the NIGEM global model and short-term indicator models. Thus the likely impact of combined and individual changes in assumptions and new information on key aggregates can be assessed in consistent fashion for each of the major economies and economic groupings. These results are mechanical and therefore intended to be no more than a guide to the informed judgments of country and topic experts on the underlying “forces acting”.

For the euro area and individual G7 economies, the near-term assessment also takes particular account of projections from a suite of statistical models using high frequency indicators to provide estimates of near-term quarterly GDP growth, typically for the current and next quarter or so. This analysis builds on the work of Sédillot and Pain (2003) and Mourougane (2006) in using short term economic indicators to predict quarterly movements in GDP by efficiently exploiting all available monthly and quarterly information. These models typically combine information from both "soft" indicators, such as business sentiment and consumer surveys, and "hard" indicators, such as industrial production, retail sales, house prices etc. and use is made of different frequencies of data and a variety of estimation techniques. The procedures are relatively automated and can be run whenever major monthly data are released, allowing up dating and choice of model according to the information set available.

The most important gains from using the indicator approach are found to be for current-quarter forecasts made at or immediately after the start of the quarter in question, where estimated indicator models appear to outperform autoregressive time series models, both in terms of size of error and directional accuracy. The main gains from using a monthly approach arise once one month of data is available for the quarter being forecast, typically two to three months before the publication of the first official outturn estimate for GDP. For one-quarter-ahead projections, the performance of the estimated indicator models are only noticeably better than simpler time series models once one or two months of information become available for the quarter preceding that being forecast. Modest gains are nonetheless to be made in terms of directional accuracy from using the indicator models.

Statistical indicator models are nonetheless limited in their ability to forecast quarterly GDP growth. Even with a complete set of monthly indicators for the quarter, the 70 per cent confidence bands around any point estimate for GDP growth in that quarter lie in the range from 0.4 to 0.8 percentage points, depending on the country or region and the degree of uncertainty is found to widen as the forecast horizon lengthens. Forecasting errors can also arise for a variety of reasons, including revisions to the initial published data and inaccuracies in the projections of the incoming monthly data. 

Regular indicator model-based estimates of GDP now feed into both routine Economic Outlook assessment exercises and interim analyses and forecast updates released to the press on a routine basis.

While the OECD's world trade forecast is built as the aggregation of individual country import and export forecasts, additional tools are used to assess the short term evolution of world trade and its consistency with the GDP growth projection.  Firstly, indicator models to forecast world trade in the short term have been developed from the techniques used for short term forecasting of GDP growth to allow the incorporation of the most recent information from key monthly trade indicators. This approach includes a bridge equation model based on a limited set of variables (world industrial production, export orders for the G6 economies, 2 technology indicators, oil prices and the Baltic dry index) and a dynamic factor model using an extended dataset (including a larger number of monthly series at world and country levels), see Guichard and Rusticelli (2011). These models are used routinely during forecasting rounds and also for interim analyses. Secondly, a global equation linking world trade growth to world GDP growth is used to assess the consistency of world trade and world GDP forecasts drawing on the work of Cheung and Guichard (2009).  To the extent that possible inconsistencies might be identified, this information is used iteratively in guiding the more detailed forecast components at country and regional levels.

The above use of statistical regression techniques relating GDP or world trade growth over the economic cycle to short-term indicator series contrasts with the longstanding approach used to produce the OECD Composite Leading Indicator series (CLIs). The latter are typically constructed for each country using a set of 5-10 variables that have been observed to be closely related to past turning points in a cyclical reference series such as GDP or, more typically, industrial production. Both techniques have different roles to play in the OECD’s assessment methods.

In making the overall assessment of current and future economic performance in individual countries, a number of key variables and relationships are examined, broadly along the following lines: 

• Domestic expenditures. Projections of private consumption and saving rates typically take into account real disposable income, household wealth, changes in the rate of inflation, monetary and financial conditions, and leading indicators of consumer confidence and retail sales. Business fixed investment is mainly assessed in relation to non-financial (sales, output and capacity utilisation) and financial (cash flow, monetary conditions and interest rates) variables. Business sentiment and survey information is also taken into account. Projections for residential construction typically take account of demographic trends, housing stocks, real income and financial conditions, but also draw on cyclical indicators for the construction sector. Projections of stockbuilding are usually made with reference to relevant stock-output and stock-sales ratios in relation to normal trends.  
• Employment, wages and prices. Employment and other labour market trends are generally assessed on the basis of actual and projected activity. Important additional considerations relate to productivity trends, capacity constraints and costs. Unemployment rate projections are derived from employment and labour supply projections, with the latter assessed on the basis of demographic trends and participation rate assumptions. Wage and earnings assessments take into account a number of key factors, such as the pattern of current wage settlements data as a leading indicator.  Unemployment, labour market conditions, productivity and the terms of trade also influence the overall projection for real wages and real compensation per employee. The assessment of domestic prices and inflation trends depends on unit costs, the strength of demand reflected by output gaps and foreign prices.
• Output gaps. The output gap is measured as the difference between actual and estimated potential GDP, in volume terms and in per cent of potential GDP. Output gaps are difficult to estimate and subject to substantial margins of error given that potential output and structural unemployment rates are generally unobservable variables. OECD work in this area generally follows a production function approach, taking into account the capital stock, changes in labour supply, factor productivities and underlying "non-accelerating inflation rates of unemployment" (NAIRU) (see Giorno et al, (1995) and Beffy et al. (2006) for further discussion of the production function approach and the estimation of output gaps. The OECD’s NAIRU estimates are updated on the basis of the inflation modeling work described in Richardson et al (2000), Turner et al. (2001) and Gianella et al. (2008), Guichard and Rusticelli (forthcoming).
• Foreign trade and balance of payments. In making the forecasts, particular attention is paid to consistency at domestic and world levels, and to ensure that key accounting identities and relationships are observed, notably with respect to international trade and the balance of payments.  For international trade volumes and prices, the process is assisted by use of the OECD’s international trade model (see Pain et al. (2005) and Murata et al. (2000).  Within this framework, projections for aggregated import volumes of goods and services typically take account of domestic activity (weighted expenditures) and relative price competitiveness, giving additional weight however to current and past trend developments in import penetration.  Aggregate goods and services export volume projections are generally linked to developments in export weighted markets, competitiveness positions and trend export performance. Projections for export prices take account of domestic labour costs and import prices, as well as competitors' export prices, while import prices are derived as weighted averages of foreign and domestic prices.

Investment income receipts and payments are set to reflect returns on stocks of external assets and liabilities, while international transfer debits and credit are exogenous, subject to consistency checks across countries. An important feature of the trade and balance of payments exercise is the need to ensure consistency across countries and regions and iterative procedures for maintaining balance at the world level.  A global equation linking world trade growth to world GDP growth is also used in checking the consistency of the world trade trajectory for given world activity, see Cheung and Guichard (2009).

The OECD’s forecasting process is greatly assisted by a purpose-built Forecast Entry system which both centralises the forecast data management process and allows individual country experts to view most recent data outcomes, new information and assumptions and revise their projections in a consistent manner, also taking account of in-built policy rules and equation-based estimates for key variables, such as inflation, trade volumes and prices, etc.  At the same time, the system maintains the consistency and coherence of the data set by incorporating all the relevant National Accounts, trade and other accounting identities linking the various concepts.  Thus as individual forecast components are updated and submitted, all identities are automatically re-evaluated to provide a fully consistent data set.  The underlying data base is maintained and updated continuously through the forecasting round by the centralised Analytical Data Base team, which also prepares associated data sets for publication.

The Forecast Entry system also provides an efficient means of managing and monitoring the overall shape of the forecast, by country and economic region, through a series of purpose-built tabular and graphic outputs.  These are used intensively in the production process and also form the basis of corresponding documents prepared for internal, committee and final publication uses, including the various Economic Outlook country specific and cross-country Annex tables and charts. 

For macro-economic assessment in the context of the Economic Outlook, the OECD uses the NiGEM model of the British National Institute of Economic and Social Research is an estimated model, which uses a ‘New-Keynesian’ framework in that agents are presumed to be forward-looking but nominal rigidities slow the process of adjustment to external events.

A policy-advice model, NIGEM is also designed to be flexible where assumption on behaviour and policy can be changed. Agents can be assumed to look forward in some scenarios, but not in others. Financial markets are normally assumed to look forward and consumers are normally assumed to be myopic but react to changes in their (forward looking) financial wealth. Monetary policy is set according to rules, with defaults designed for speed. However, interest rate feedback rules can be changed, and their parameters adjusted.

The structure of the NIGEM is designed to correspond to macroeconomic policy needs. NiGEM is a structured around the national income identity, can accommodate forward looking consumer behaviour and has many of the characteristics of a Dynamic Stochastic General Equilibrium (DSGE) model. Unlike a pure DSGE model, NiGEM is based on estimation using historical data. It thus strikes a balance between theory and data and enables using the NIGEM both for policy analysis and forecasting.

Most countries in the OECD are modeled separately. The rest of the world is modeled through regional blocks: Latin America, Africa, East Asia, Developing Europe, OPEC and a Miscellaneous group mainly in West Asia. All models contain the determinants of domestic demand, export and import volumes, prices, current accounts and net assets, and the OECD countries are more complex than those of the non-OECD countries.

The core of each of these country models consists of a production function determining output in the long term; a wage-price block; a description of the government sector; consumption, personal income and wealth; international trade; and financial markets. We use a dynamic error-correction structure on the estimated equations, which allows the model to adjust gradually towards equilibrium in response to a shock. In some cases the speed of adjustment will depend on expectations as well as distance from equilibrium.

Linkages in NiGEM take place through trade and competitiveness, interacting financial markets and international stocks of assets. The model is homogeneous in exchange rates, and exports demand equals imports across the world. Competitiveness acts as an important stabilising feedback on the model, as shifts in the domestic price level or the exchange rate feed into relative trade prices, allowing net trade to offset shifts in domestic demand.

In assessing the fiscal situation of Member countries, the OECD uses a wide range of indicators over a period of several years, since looking at one concept for a single year could give a distorted picture, given changes in economic conditions and special one-off factors.

More specifically, the cyclically-adjusted budget balance represents what government revenues and expenditure would be if output were at its potential level. In evaluating the stance of fiscal policy it is also useful to correct the cyclically adjusted balance for interest payments on government debt since these payments do not represent discretionary spending items. Thus, the primary cyclically-adjusted budget balance is derived by adding back net interest payments to the cyclically-adjusted balance. Changes in the primary cyclically-adjusted balance can then be used as a rough indicator for changes in discretionary fiscal policies.

Special attention is also paid to the general government's consolidated gross financial liabilities which measure the total debt held outside the government's accounts and provides an indicator of the likely future debt servicing burden of the economy. It should be noted that measured debt does not give a complete picture of debt servicing burdens, as it generally excludes contingent liabilities and financial assets (i.e. pensions, health care, deferred taxes) and the value of the government's real assets. The "true" value of the government's financial assets is also often difficult to gauge (e.g. government loan programmes and holding of shares in state owned enterprises). Nevertheless, the size of government debt – both gross and with financial liabilities netted out - is a key variable for estimating and evaluating issues related to fiscal sustainability and the room of manoeuvre for fiscal policy.

These concepts are explained in detail in the Notes to the Annex Tables of the Sources and Methods document.

The general class of weighting method used in the Economic Outlook, broadly consistent with current National Accounts practices, is a chain-linked method (ref) where the weights typically applied to rates of change or levels, depending on the concept being aggregated, move over time (year or quarters). The broad justification for such an approach, as discussed by Whelan (2000), (see also the OECD Statistical Glossary, is that moving weights take better account of composition changes over time, especially those related to changes in relative prices or technologies. Depending on the quantity under consideration, one of two specific weighting schemes is adopted:

Method 1 (especially relevant to varaiables expressed as ratios or shares or rates of interest):

Weights are applied to the variable in level terms:

where w is the relevant share of country i for period t in the region under consideration, and X is the variable to be aggregated.

Method 2 (relevant for series in absolute terms, e. g. real GDP, prices and exchange rates):

Aggregation proceeds in three steps. First, a weighted average, pct(.) of individual growth rates of the relevant quantity is computed:

This is converted into an index that takes on the value 1 in the base year.

Secondly, the aggregate level of the relevant quantity is computed for the base year by summing up the countries’ individual base-year levels of the relevant quantity:

For example, aggregate real GDP in the base year is obtained by adding up individual base-year GDP’s expressed in a common currency.
Finally, the resulting index series for aggregated growth is rescaled by the value of the aggregated quantity in the base year.

With either method, the choice of weights used depends on the particular nature of the variables to be aggregated, and represent a country’s share in a certain quantity or concept for the relevant region.
For ratios, the denominator of the variable to be aggregated is used to compute the country’s weight in a region. For example, for the current account balance expressed as a per cent share of GDP (CBGDPR), the weights are based on GDP in the reference years, while for the unemployment rate (UNR, the share of unemployed in the total labour force), the weights are based on the size of the labour force (LF) in each country in the region.

For GDP and expenditures, the weights are computed using the series in nominal i.e. current value terms expressed in a common currency. For example, private consumption (CP) and real private consumption (CPV) are aggregated using weights based on CP (that is a country’s share in the region’s consumption).
For variables related to numbers of persons, the weights are based on the shares of the variable to be aggregated. For example, total employment (ET) is aggregated using weights based the share of the country in the region’s total employment.

For a selection of variables, for example interest rates, where there is no absolute regional concept, regional averages are computed using GDP weights (that is a country’s share in the region’s nominal GDP). Where variables are related to constant or current price values, these variables are first expressed in a common currency (e.g. euro for the euro area) or in Purchasing Power Parities (PPP’s).

In the event of incomplete information, for example due to delays in reporting of recent historical values, the reliability of coverage for estimates for a specific economic aggregate is usually judged via a threshold value. Thus reported history for a specific aggregate is judged to stop as soon as the available country coverage falls below 2/3 for the zone in terms of base-year GDP. Aggregated values thereafter should be treated as “OECD estimates” rather than actual data.  

The Economic Outlook database is a comprehensive and consistent set of macroeconomic data. It contains the biannual macroeconomic forecasts for each OECD country and the OECD area as a whole. Almost all data shown in the statistical annex of the Economic Outlook publication can be found in the EO database in OECD Explorer. Also available to download are the current edition statistical annexes in Excel from the Economic Outlook topic page. 

The Economic Outlook database is released twice a year: end of spring and end of autumn. The spring edition covers the period which goes up to current year +1. The autumn edition goes to current year +2.

It is the edition number. EO90 stands for the "Economic Outlook, 90th edition" i.e. OECD Economic Outlook database which includes data of the 90th edition, autumn 2011, forecast up to 2013. 

The Economic Outlook Database includes data on all OECD countries and for selected non-Member countries (Brazil, China, Colombia, Costa Rica, India, Indonesia, Latvia, Lithuania, Russia, South Africa). It also includes data on country groupings (OECD euro area, Total OECD, G7, Oil producers, World). Variables cover all major economic dimensions. For more information, see the EO database inventory.

All historical time series (i.e. official series reported by countries excluding forecasts) do not end up at the same date. The last historical points are given in a MS Excel file for each EO edition. In OECD Explorer, select a dataset under "economic projections" and then click on to view the link to the MS Excel file with the last historical points.

For most countries, historical data over the two or three last years should be considered as "provisional" and subject to revisions. During this period national statistical offices compile more information and may improve their official estimate

The EO includes quarterly and annual data. All quarterly data are seasonally adjusted. Seasonal adjustment is mostly carried out by the National Statistical Offices and therefore varies by country. An up-to-date guide to current methods in use by country is available on the web. Where seasonally adjusted series are not available, adjustment is done in-house by the OECD using an X12-based method subject to annual constraints.

The EO database uses the reference year used by countries in their national official publications. The reference year is specific to each country. All national reference years are listed in the EO database inventory. 

The base year for country groupings (for example OECD total) was 2010 in the 98 edition. This standard base year is changed approximately every 5 years.

Base years used in previous EO editions are listed in a downloadable Excel file.

All series (levels) are expressed in units, i.e. with power code = 0. Some indices refer to 1, others to 100. See the indicator title, the related metadata or the EO database inventory for more information.

All levels in EODB are expressed in national currency, except trade and country groupings which are shown in US dollars. Trade data are converted to US dollars using exchange rates. Country groupings are calculated using Purchasing Power Parities (PPPs). Read more about .

The EO database inventory contains a section on main changes since the last edition. Database content and structure adapts over time to reflect changes in the source datasets and new subjects studied by the OECD. Information relating to data quality, changes in statistical sources and methodologies, addition or suppression of specific variables is fully documented in the EO database inventory.

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There are two main differences between the SNA08 and Maastricht definitions of debt. The first difference is that the Maastricht debt does not include liabilities related to other accounts payable (comprising trade credits and advances), financial derivatives, and insurance technical reserves. The second difference concerns the valuation methodology.

The Maastricht definition evaluates debt at face value, which is equivalent to the amount that the government has to pay back to creditors at maturity. In contrast, the SNA08 employs market values. Maastricht debt is thus a better measure for assessing government refinancing needs, but the SNA08 captures more adequately the cost of buying back debt.

For non-tradable debt instruments market valuation is not available, requiring imputation of prices by some alternative methods. Also market valuation might be problematic for tradable instruments when markets are volatile or/and illiquid. Consequently, the SNA08 measure can be more volatile than the Maastricht one. Identifying the exact contributions of the various factors to differences in debt level according to the SNA08 and Maastricht definitions is not straightforward.

Government gross financial liabilities refers to the financial liabilities (short and long-term) of all the institutions in the general government sector, as defined in the SNA2008/ESA2010, typically mainly in the form of government bills and bonds.

Valuation of financial liabilities among countries can differ. SNA2008/ESA2010 require valuation at market value or by the amount the debtor must repay to extinguish the claim (for non-marketable liabilities), but some countries as the United States and Canada value government bonds at their face value. (i.e. at issue price).

The OECD data are based on SNA08, while the IMF World Economic Outlook data are based on the IMF Government Finance Statistics Manual (GFSM 2001). The major difference between GFSM2001 and SNA2008/ESA2010, regarding  government liabilities, concerns unfunded pension liabilities, which are included in debt according to the GFSM 2001, but excluded in the SNA2008/ESA2010 data. Other, smaller differences between the two systems add to the discrepancy.

The euro area and the other country groupings are calculated within the database. They are not taken from another source. "Euro area" refers to the group of euro area countries that are at the same time members of the OECD. 

A system of national accounts (SNA) seeks to provide a coherent framework for recording and presenting the main flows relating respectively to production, consumption, accumulation and external transactions of a given economic area, usually a country or a major region within a country. Government revenues are an important part of the transactions recorded in SNA.

There are, however, some differences between the classification of taxes in the OECD Revenue Statistics and SNA concepts, which are listed below. They arise because the aim of the former is to provide the maximum disaggregation of statistical data on what are generally regarded as taxes by tax administrations.

a) OECD includes social security contributions in total tax revenues;

b) there are different points of view on whether or not some levies and fees are classified as taxes;

c) OECD excludes imputed taxes or subsidies resulting from the operation of official multiple exchange rates;

d) there are differences in the treatment of non-wastable tax credits.

Debt is a commonly used concept, defined as a specific subset of liabilities identified according to the types of financial instruments included or excluded. Generally, debt is defined as all liabilities that require payment or payments of interest or principal by the debtor to the creditor at a date or dates in the future.
Consequently, all debt instruments are liabilities, but some liabilities such as shares, equity and financial derivatives are not debt [System of National Accounts, 2008, par. 22.104].