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Forecasting tools and procedures at the Banque de France FORECASTING MODELS AND PROCEDURES OF EU CENTRAL BANKS April 23, 2008, Sofia Macro analysis and forecast division, Banque de France. Main points of the presentation.

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  1. Forecasting tools and procedures at the Banque de France FORECASTING MODELS AND PROCEDURES OF EU CENTRAL BANKSApril 23, 2008, SofiaMacro analysis and forecast division, Banque de France

  2. Main points of the presentation • General overview & a focus on the iteration of the macroeconomic and public finances division. • Revised version of OPTIM • Forecasting inflation tools • French retail sector specificities • Dealing with minimum wage • Temptative labor share equation as a guardrail to help the forecast

  3. Forecasting procedures at the BdF: General overview & a focus on the iteration of the macroeconomic and public finances division.Delphine Irac Banque de France

  4. Public Finances • Spring Exercise: • End of March: annual public finances accounts delivery. Not all components (for instance no investment for local administrations. • Public finances division: almost one week to build a consistent historical database on the previous year. • + make a forecast of the public fi variables • Delay the launching of the macroeconomic model based forecast • Winter Exercise: • From September to December: public finances law project+amendments • Difficult to adjust the annual forecasts of the public finances division with the quarterly public finances data (used in the model).

  5. Monthly forecasting of French GDP: a revised version of the OPTIM model Banque de France Macro-analysis and forecasting division

  6. Monthly forecasting of French GDP:a revised version of the OPTIM model • Description of OPTIM • Modelling strategy and data selection • Results • Conclusion

  7. The spirit of OPTIM (1/2) Brief overview of the difft methods used in short run assessment • Methods equation by equation: • Factor models • Bridge models • Bayesian averaging • Forecasts combination (Stock and Watson 2004) • Model based method • VAR, Bayesian VAR • Neo keynesian model • Accounting relationships

  8. The spirit of OPTIM (1/2) • OPTIM = 1b + 1d • + GETS procedure

  9. 1. Description of OPTIMThe main characteristics • Bridge model created by Irac and Sédillot (2002) • New version by Barhoumi, Brunhes-Lesage, Darné, Ferrara, Pluyaud and Rouvreau (2007) • Forecasts for French GDP and its components for the current quarter (and for the next one, in a forthcoming version) • Based on monthly indicators (survey data and hard data) • Use: SRA BMPE (joint with the macro model Mascotte) + internal conjonctural assessments, monthly

  10. 1. Description of OPTIMA revised version of the model • New equations • Main contribution of the revised model: Monthly forecasts (previously quarterly forecasts) • Systematic data selection using Gets

  11. 2. Modelling strategy and data selectionModelled components (1/3) • French GDP quarterly growth rate+ GDP components quarterly growth rate • Some components are not modelled(production of non market services, immaterial investment, changes in inventories) • Aggregation with equations

  12. 2. Modelling strategy and data selectionModelled components (2/3) • On the demand side: • • Household consumption, computed by aggregation of the forecasts for: • Household consumption in agri-food goods • Household consumption in energy • Household consumption in manufactured goods • Household consumption in services • • Government consumption • • Investment, computed by aggregation of the forecasts for: • Corporate investment in machinery and equipment • Corporate investment in building • Household investment • Government investment • • Exports • • Imports

  13. 2. Modelling strategy and data selectionModelled components (3/3) B. On the supply side: • Total Production, computed by aggregation of the forecasts for: Production of agri-food goods Production of manufactured goods Production of energy Production in construction Production of market services C. Total GDP is forecast using a regression on total production.

  14. 2. Modelling strategy and data selectionMonthly exercises • 3 forecasts for each quarter • After the publication of Insee and EC surveys and before the ECB « monetary » Governing Council • When data are missing for some months of the last quarter, the value for the quarter is computed as the 3-month moving average of the last available observations

  15. 2. Modelling strategy and data selectionThe data set (1/3) • Monthly or higher frequency data • Soft (survey) data and hard data • Recent information (less than 2 months)

  16. 2. Modelling strategy and data selectionThe data set (2/3)

  17. 2. Modelling strategy and data selectionThe data set (3/3) Nov. IPI Dec. IPI Jan. IPI Feb. IPI Mar. IPI Apr. BdF survey Mar. BdF survey Dec. BdF survey Jan. BdF survey Feb. BdF survey Mar. cons. in manuf. goods Feb. cons. in manuf. goods Apr. cons. in manuf. goods Jan. cons. in manuf. goods Dec. cons. in manuf. goods May Inseeand EC surveys Feb. Inseeand EC surveys Apr. Inseeand EC surveys Mar. Inseeand EC surveys Jan. Inseeand EC surveys January February March April May 1st forecast for Q1 2nd forecast for Q1 3rd forecast for Q1 Q4 GDP release Q1 GDP release

  18. 2. Modelling strategy and data selectionGeneral specification of the equations • Autoregressive-distributed-lag (ADL) bridge equations

  19. 2. Modelling strategy and data selectionData selection procedure (1/2) • Systematic data selection using Gets • Preselection of explanative variables strongly correlated with the modelled variable but not with each other • No mix between similar data sources • No use of synthetic survey indicators • Selection of a first set of equations with an emphasis on economic content • Final selection with rolling forecasts, taking into account the data availabilty

  20. Monthly forecasts • Optim: • Same equation for a given quarter • RHS missing explanatory variables are estimated using ad hoc methods (average of the observed months etc.) • Main drawback: very likely to miss turning points • Alternative methods (e.g. INSEE) • Different equations for different months • The equation specification is optimized w.r.t the set of data that are available when the fcsts is implemented • Drawback: more difficult to analyse/justify fcsts revision since change in the equation and change in the model

  21. 3. ResultsRoot Mean Squared Errors

  22. 4. Conclusion • Satisfying results given the comparisons with benchmarks • Next step: future quarter forecasts • Problems concerning the aggregation of forecasts for GDP components

  23. Forecasting inflation : 3 tools according to the horizon Banque de France Macro-analysis and forecasting division

  24. 3 tools according to the horizon of analysis • Very short term (3 months ahead) - NIPE • Very detailed analysis (≈ 50 components) • Unconditional projections (persistence of inflation) • Short term (1 year ahead) - NIPE • Detailed sectored analysis (≈15 components) • Conditional to import prices, wages… • Medium term (2 years ahead) - BMPE • HICP and HICP excluding energy • Value added Prices and Import deflator

  25. Very short term (3 months ahead) • Available information • Non conditional forecast: Stochastic process Zt, observable from t = 1 until t = T Forecast : • SARMA processes (Use of tramoseats)

  26. Very short term (3 months ahead) Food • Each main component is modelled with an equation: • Meat product HICP • Wheat product HICP • Milk product HICP • Oil product HICP • Non-alcoholic HICP

  27. Very short term (3 months ahead) Food • Explanatory variables: • Wholesale prices • Producer prices • Lagged variable

  28. Short term (1 year ahead) - NIPE List of components (components asked for the NIPE in blue)

  29. Short term (1 year ahead)- NIPEThe underlying HICP • The underlying HICP is composed by three main sectors =>Private services HICP (Housing services, Healthcare, Restaurants) =>Processed food HICP =>Industrial goods HICP • For each sectors, an Error Correction Model where year-on-year inflation is supposed to be consistent with an I(1) process. • Exogenous variables come from Mascotte and ECB assumptions • Sectors depend on different factors

  30. Short term (1 year ahead) – NIPE Sectors dependent on different factors • Manufactured goods: • Wages • Prices of raw material • Import prices • Capacity utilization rate

  31. Short term (1 year ahead) – NIPE Sectors dependent on different factors • Private services: • Wages • Unemployment rate

  32. Short term (1 year ahead) – NIPE Sectors dependent on different factors • Processed food prices • A model with two equations: - Domestic Agricultural prices depend on international food prices - Processed food prices depend on domestic agricultural prices, unit labor cost and the capacity utilization rate.

  33. Short term (1 year ahead)- NIPEThe energy HICP • The energy HICP is disaggregated into two components =>oil products =>gas and electricity • Oil products are modelled in two steps =>Oil products HICP without taxes is modelled with an ECM with the price of the brent as exogenous variable =>Taxes are included to take into account their nonlinearity • Gas and electricity prices are modelled via seasonal and non seasonal dummies

  34. Short term (1 year ahead)- NIPEThe unprocessed food HICP • Four sub-indexes =>meat products HICP =>fish products HICP =>fruit HICP =>vegetable HICP • ARMA with fixed seasonal effects

  35. Short term (1 year ahead)- NIPEResearch on a new inflation forecasting model • A need to reassess the equations: • A longer period • Changes in quarterly national accounts • Investigation on the best level of aggregation • New exogenous variables such as producer prices

  36. Short term (1 year ahead) – NIPE Residuals – Industrial goods

  37. Short term (1 year ahead) – NIPE Residuals – Private services

  38. Short term (1 year ahead) – NIPE Industrial goods

  39. Short term (1 year ahead) – NIPE Private services

  40. The French retail sector specificities: Banque de France Macro-analysis and forecasting division 18 March 2008

  41. Introduction • The reform of the retail sector: => From 1996, a sector with low competition => From January 2006 to January 2008, three reforms have changed the competition environment • French sellers/retailers negociation context

  42. The reform of the retail sector • A sector with low competition • The legislation =>The “Raffarin law” (1996) : An authorization is needed to open a retail shop =>The “Galland law” (1996) : It is forbidden to sell beneath the unit cost • As a result, from 1996 to 2004, the inflation in processed food prices is higher in France than in the euro area

  43. The reform of the retail sector • A sector with low competition The inflation in processed food prices is higher in France than in the euro area from 1996 to 2004 Processed food year on year inflation rate 1996 - 2004

  44. Producer Retailer Final consumer Pays commercial services Receives commercial services Margin 2 Receives sell price Pays sell price Receives unit cost Pays unit cost Margin 1 The reform of the retail sector 1. A sector with low competition Even if the sector was competitive, retailers would have positive margins thanks to commercial services

  45. The reform of the retail sector 2. From 2004: a new competition environment • In January 2004, sellers and retailers are urged by the French governmentto negociate their prices down • From January 2006, a new breakeven point is defined: commercial margins are partly deductible from the unit cost. • From January 2006 to January 2008, the amount of commercial margins that is deductible increases up to be totally deductible (“Chatel law”)

  46. The reform of the retail sector 2. A new competition environment • Consequently: the inflation in processed food was below 1% from 2005 to July 2007

  47. The negociation context Prices are negociated at fixed dates • Negociation rounds occur at fixed dates. • Negociations from producers to retailers mostly occurs in January and February. • In the milk market, prices are fixed four times a year by a national syndicate of milk producers. As a result, producer prices are less volatile. • Menu-costs: The impact of the increase in international food prices may be delayed.

  48. Dealing with minimum wage indexation in the forecast

  49. Wage indexation in France • In March N+1, workers of types 2 and 3 will try to catch up with the increase in the wages of workers of type 1 (+1% compared to the previous agreement). • However, there is no reason why there will be full indexation of the other wages on the raise of the SMIC. Therefore, the bargaining process could typically end up in a statu quo agreement such as: • w1 : the SMIC (1010 €) • w2 : 1010 or 1015€ • w3: 1020 or 1025 €

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