Main points of the presentation

Forecasting tools and procedures at the Banque de France

FORECASTING MODELS AND PROCEDURES OF EU CENTRAL BANKSApril 23, 2008, Sofia

Macro analysis and forecast division, Banque de France

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

Forecasting procedures at the BdF:

General overview & a focus on the iteration of the macroeconomic and

public finances division.

Delphine Irac Banque de France

Public Finances

1. 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

forecast2. 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).

Monthly forecasting of French GDP:

a revised version of the OPTIM model

Banque de France Macro-analysis and forecasting division

Monthly forecasting of French GDP:a revised version of the OPTIM model

1. Description of OPTIM

2. Modelling strategy and data selection

3. Results

4. Conclusion

The spirit of OPTIM (1/2)

Brief overview of the difft methods used in short run assessment

Methods equation by equation:a. Factor modelsb. Bridge modelsc. Bayesian averagingd. Forecasts combination (Stock and Watson 2004)2. Model based methoda. VAR, Bayesian VARb. Neo keynesian modelc. Accounting relationships

The spirit of OPTIM (1/2)

• OPTIM = 1b + 1d

• + GETS procedure

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

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

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


A. On the demand side:

• Household consumption, computed by aggregation of the forecasts for:Household consumption in agri-food goodsHousehold consumption in energyHousehold consumption in manufactured goodsHousehold consumption in services

• Government consumption

• Investment, computed by aggregation of the forecasts for:Corporate investment in machinery and equipmentCorporate investment in buildingHousehold investmentGovernment investment

• Exports

• Imports


B. On the supply side:

• Total Production, computed by aggregation of the forecasts for:Production of agri-food goodsProduction of manufactured goodsProduction of energyProduction in constructionProduction of market services

C. Total GDP is forecast using a regression on total production.

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

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)


Name Source Data type Frequency Publication lag

Quarterly National Accounts Insee Hard Quarterly 45

Industrial Production Index Insee Hard Monthly 40

Consumption in manufactured goods Insee Hard Monthly 25

HICP in agri-food Eurostat Hard Monthly 20

New cars registrations CCFA Hard Monthly 2

Electricity consumption RTE Hard Daily 1

Declared housing starts Ministry of Equipment Hard Monthly 30

Business surveys in industry Banque de France Soft Monthly 15

Business surveys in retail trade Banque de France Soft Monthly 15

Business surveys in services Banque de France Soft Monthly 15

Business surveys in industry Insee Soft Monthly 0

Business surveys in retail trade Insee Soft Monthly 0

Business surveys in services Insee Soft Monthly 0

Business surveys in construction Insee Soft Monthly 0

Consumer surveys Insee Soft Monthly 0

Survey on public works FNTP Soft Monthly 35

Business and consumer surveys European Commission Soft Monthly 0


January February March April May

Q1 GDP release

Q4 GDP release

Jan. Inseeand EC surveys

Nov. IPI

Dec. BdF survey

Dec. cons. in manuf. goods

1st

forecast for Q1

2nd forecast for Q1

3rd forecast for Q1

Feb. Inseeand EC surveys

Dec. IPI

Jan. BdF survey

Jan. cons. in manuf. goods

Mar. Inseeand EC surveys

Jan. IPI

Feb. BdF survey

Feb. cons. in manuf. goods

Apr. Inseeand EC surveys

Feb. IPI

Mar. BdF survey

Mar. cons. in manuf. goods

May Inseeand EC surveys

Mar. IPI

Apr. BdF survey

Apr. cons. in manuf. goods

2. Modelling strategy and data selectionGeneral specification of the equations

• Autoregressive-distributed-lag (ADL) bridge equations

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

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

3. ResultsRoot Mean Squared Errors

Component First Second Third AR Naive

GDP with IPI 0.32 0.31 0.23 0.38 0.51 without IPI 0.27 0.25 0.25

Production Agri-food with IPI 0.49 0.47 0.45 0.57 0.68 without IPI 0.54 0.54 0.54Production Manufactured with IPI 1.14 1.07 0.71 1.28 1.73 without IPI 0.82 0.79 0.79Production Energy with IPI 1.56 1.48 1.21 1.44 2.52 without IPI 1.44 1.34 1.34Production Construction with IPI 0.63 0.57 0.55 0.67 0.76 without IPI 0.62 0.60 0.60Production Services with IPI 0.41 0.41 0.34 0.45 0.59 without IPI 0.44 0.39 0.37

Household Consumption 0.26 0.19 0.19 0.33 0.45Government Consumption 0.23 0.23 0.23 0.23 0.28Investment 0.80 0.77 0.71 0.87 1.24Imports 1.23 1.13 1.13 1.31 1.54Exports 1.46 1.32 1.27 1.62 2.07

4. Conclusion

• Satisfying results given the comparisons with benchmarks

• Next step: future quarter forecasts

• Problems concerning the aggregation of forecasts for GDP components

Forecasting inflation : 3 tools according to the horizon

Banque de France

Macro-analysis and forecasting division

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

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)

111 ,... , , ˆˆ ZZZZZ TThT

H

hhT


Food

• Each main component is modelled with an equation:

Meat product HICPWheat product HICPMilk product HICPOil product HICPNon-alcoholic HICP


Food

• Explanatory variables:

Wholesale pricesProducer prices Lagged variable

Short term (1 year ahead) - NIPE

List of components (components asked for the NIPE in blue)

0.207 FOOD Econometric model

0.085 Unprocessed food (meat, fish, vegetables, fruit) ARMA with fixed seasonal effects

0.122 Processed food

0.099 - Processed food excluding tobacco ECM

0.023 - Tobacco Hikes according to announcement

0.298 MANUFACTURED GOODS ECM

0.079 ENERGY

0.044 Oil products ECM

0.035 Other energies (gas, electricity) ARMA

0.416 SERVICES

0.027 CommunicationsLeast square on seasonal dummies

0.362 Private services ECM

0.014 Rail and road transportsLeast square on seasonal dummies

0.008 Air transports Regression on brent prices

0.004 Combined transportsLeast square on seasonal dummies

0.001 Sea transports and others Least square on seasonal dummies

0.999 ENSEMBLE

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

• Manufactured goods:• Wages• Prices of raw material• Import prices• Capacity utilization rate

Short term (1 year ahead) – NIPESectors dependent on different factors

• Private services:• Wages• Unemployment rate


•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.


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

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

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

Short term (1 year ahead) – NIPEResiduals – Industrial goods

-.3

-.2

-.1

.0

.1

.2

.3

00 01 02 03 04 05 06 07 08 09

RESCM

Short term (1 year ahead) – NIPEResiduals – Private services

-.4

-.3

-.2

-.1

.0

.1

.2

00 01 02 03 04 05 06 07 08 09

RESSER

Short term (1 year ahead) – NIPEIndustrial goods

Dependent Variable: GA_I_CM Method: Least Squares Date: 11/23/07 Time: 09:59 Sample (adjusted): 1986Q3 2007Q3 Included observations: 85 after adjustments Convergence achieved after 5 iterations GA_I_CM = C(1)+GA_I_CM(-1)+C(2)*(GA_I_CM(-1)- C(3) *GA_REMPT(-4)-C(4)*GA_MP_HARD(-2)-C(5)*GA_UMTO1P(-5)) +C(6)*D(GA_I_CM(-4))+C(7)*TUCBDF(-5)+RESCM

Coefficient Std. Error t-Statistic Prob. C(1) -3.552053 1.085872 -3.271154 0.0016

C(2) -0.122235 0.031360 -3.897829 0.0002 C(3) 0.244945 0.201851 1.213495 0.2286 C(4) 0.018917 0.020055 0.943274 0.3485 C(5) 0.114072 0.062951 1.812082 0.0738 C(6) -0.321027 0.095106 -3.375460 0.0012 C(7) 4.161255 1.310199 3.176048 0.0021

R-squared 0.974054 Mean dependent var 1.152626

Adjusted R-squared 0.972059 S.D. dependent var 1.211334 S.E. of regression 0.202483 Akaike info criterion -0.277558 Sum squared resid 3.197952 Schwarz criterion -0.076398 Log likelihood 18.79620 F-statistic 488.0473 Durbin-Watson stat 2.055118 Prob(F-statistic) 0.000000

Short term (1 year ahead) – NIPEPrivate services

Dependent Variable: GA_I_SER Method: Least Squares Date: 11/23/07 Time: 09:59 Sample: 1988Q2 2007Q3 Included observations: 78 Convergence achieved after 4 iterations GA_I_SER= C(1)+GA_I_SER(-1)+C(2)*(GA_I_SER(-1)-C(3) *GA_REMPT(-2)-C(31)*TXCHO_BIT(-4))+C(5)*DUM021_031+C(6) *D(GA_I_SER(-1))+RESSER

Coefficient Std. Error t-Statistic Prob. C(1) 0.524981 0.275875 1.902970 0.0610

C(2) -0.086988 0.023660 -3.676632 0.0005 C(3) 0.730296 0.237398 3.076251 0.0030

C(31) -0.518618 0.287777 -1.802153 0.0757 C(5) 0.505700 0.113300 4.463392 0.0000 C(6) 0.150281 0.077989 1.926964 0.0579

R-squared 0.988035 Mean dependent var 3.123346

Adjusted R-squared 0.987204 S.D. dependent var 1.406408 S.E. of regression 0.159092 Akaike info criterion -0.764868 Sum squared resid 1.822334 Schwarz criterion -0.583582 Log likelihood 35.82984 F-statistic 1189.106 Durbin-Watson stat 1.727423 Prob(F-statistic) 0.000000

The French retail sector specificities:

Banque de France

Macro-analysis and forecasting division

18 March 2008

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

The reform of the retail sector1. 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

The reform of the retail sector1. A sector with low competition

Processed food year on year inflation rate 1996 - 2004

-2

0

2

4

6

8

96 97 98 99 00 01 02 03 04

euro area France Germany

The inflation in processed food prices is higher in France than in the euro area

from 1996 to 2004

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

Producer Retailer Final consumer

Receives sell price

Receives

unit cost

Pays

unit cost

Margin 1

Pays

sell price

Pays commercial

services

Receives commercial

servicesMargin 2

The reform of the retail sector2. From 2004: a new competition environment

• In January 2004, sellers and retailers are urged by the French government to 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”)

The reform of the retail sector2. A new competition environment

• Consequently: the inflation in processed food was below 1% from 2005 to July 2007

-1

0

1

2

3

4

5

6

7

05M01 05M07 06M01 06M07 07M01 07M07

euro area France Germany

The negociation contextPrices 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.

Dealing with minimum wage indexation in the forecast

Date SMIC = Worker type 1 (w1)

is paid:

Worker type 2 (w2)

is paid:

Worker type 3 (w3)

is paid:

Latest wage agreement

issued:

Year NMARCHWage agreement

1000 € 1000 € 1009 € 1020 € W1 =1000€W2 = 1009€W3 = 1020€

Year N JULYSMIC raise

1010 € 1010 € 1010€(SMIC effect indexation)

1020 €(no indexation)

W1 =1000€W2 = 1010€W3 = 1020€

Year N+1MARCHWage agreement

1010 € 1010€ ?See below

?See below

?See below

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 €

Estimation of a labor share equation as a guard

Labor share equation

• Benchmark equation:• Y=F(K,BL)=Kf(l)• With l=BL/K• w/p=Bf’(l)• Labor share=s=lf’(l)/f(l)

• Bentolila & SaintPaul:

• s=g(k) with k=capital output ratio (SK) schedule

Shifts in the SK schedule

• Changes in oil prices: shifts in the SK schedule

• Effect of mark-up:

S=µ-1g(k)

• Increase in workers bargaining power shift the SK schedule upwards

Coeff Student

DLOG(TM1(-2)) 0.152061 2.487627

D(social wedge) -1.409215 -6.079143

DLOG(PRODT) 3.349982 9.439873

D(CURBDF(-1)) 0.464890 1.690725

D(D(UrateO_BIT)) 0.031071 2.136494

LOG(TM1(-1)) -0.090613 -3.838195

LOG(Globalization(-1)) 0.125471 4.370235

LOG(Minimum wage(-1)) -0.040488 -4.015430

Interest rate(-1) 1.029257 5.166875

Estimations

• Dependent variable: 1-labor share (TM)

Contributions

Variation du taux de marge et contributions entre 1995 et 2007

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

poin

ts

coin social productivité smic réeltaux d'intérêt réel lissé TUC tx chômagetx d'ouverture cale variation du taux de marge

Documents

Main points of the presentation