Transcript Diapositive 1

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
• 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 forecast
2. 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

a.
b.
c.
d.
2.
a.
b.
c.
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
The spirit of OPTIM (1/2)
• OPTIM = 1b + 1d
• + GETS procedure
1. Description of OPTIM
The 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 OPTIM
A 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 selection
Modelled 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
2. Modelling strategy and data selection
Modelled components (2/3)
A. 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
2. Modelling strategy and data selection
Modelled 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.
2. Modelling strategy and data selection
Monthly 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 selection
The data set (1/3)
•
Monthly or higher frequency data
•
Soft (survey) data and hard data
•
Recent information (less than 2 months)
2. Modelling strategy and data selection
The data set (2/3)
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
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
European Commission
Soft
Monthly
0
HICP in agri-food
Business and consumer surveys
2. Modelling strategy and data selection
The data set (3/3)
Nov. IPI
Dec. BdF
survey
Dec. cons.
in manuf.
goods
Dec. IPI
Jan. BdF
survey
Jan. cons.
in manuf.
goods
Feb.
Insee
and EC
surveys
Jan.
Insee
and EC
surveys
January
February
1st
forecast for
Q1
Q4 GDP
release
Jan. IPI
Feb. IPI
Feb. BdF
survey
Feb. cons.
in manuf.
goods
Mar.
Insee
and EC
surveys
Mar. IPI
Mar. BdF
survey
Mar. cons.
in manuf.
goods
Apr.
Insee
and EC
surveys
March
April
2nd forecast
for Q1
3rd
forecast
for Q1
Apr. BdF
survey
Apr. cons.
in manuf.
goods
May
Insee
and EC
surveys
May
Q1 GDP
release
2. Modelling strategy and data selection
General specification of the equations
•
Autoregressive-distributed-lag (ADL)
bridge equations
2. Modelling strategy and data selection
Data 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. Results
Root Mean Squared Errors
Component
GDP
Production Agri-food
Production Manufactured
Production Energy
Production Construction
Production Services
Household Consumption
Government Consumption
Investment
Imports
Exports
with IPI
without IPI
with IPI
without IPI
with IPI
without IPI
with IPI
without IPI
with IPI
without IPI
with IPI
without IPI
First
0.32
0.27
0.49
0.54
1.14
0.82
1.56
1.44
0.63
0.62
0.41
0.44
0.26
0.23
0.80
1.23
1.46
Second
0.31
0.25
0.47
0.54
1.07
0.79
1.48
1.34
0.57
0.60
0.41
0.39
0.19
0.23
0.77
1.13
1.32
Third
0.23
0.25
0.45
0.54
0.71
0.79
1.21
1.34
0.55
0.60
0.34
0.37
0.19
0.23
0.71
1.13
1.27
AR
0.38
Naive
0.51
0.57
0.68
1.28
1.73
1.44
2.52
0.67
0.76
0.45
0.59
0.33
0.23
0.87
1.31
1.62
0.45
0.28
1.24
1.54
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
H
Forecast : ZˆT  h h 1  ZˆT  h ZT , ZT 1 , ..., Z1


• SARMA processes (Use of tramoseats)
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
Very short term (3 months ahead)
Food
• Explanatory variables:
Wholesale prices
Producer 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
Communications
Least square on seasonal
dummies
0.362
Private services
ECM
0.014
Rail and road transports
Least square on seasonal
dummies
0.008
Air transports
Regression on brent prices
Short term (1 year ahead)- NIPE
The 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 yearon-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
Short term (1 year ahead) – NIPE
Sectors dependent on different factors
• Manufactured goods:
•
•
•
•
Wages
Prices of raw material
Import prices
Capacity utilization rate
Short term (1 year ahead) – NIPE
Sectors dependent on different factors
• Private services:
• Wages
• Unemployment rate
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.
Short term (1 year ahead)- NIPE
The 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)- NIPE
The 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)- NIPE
Research 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) – NIPE
Residuals – Industrial goods
.3
.2
.1
.0
-.1
-.2
-.3
00
01
02
03
04
05
RESCM
06
07
08
09
Short term (1 year ahead) – NIPE
Residuals – Private services
.2
.1
.0
-.1
-.2
-.3
-.4
00
01
02
03
04
05
06
RESSER
07
08
09
Short term (1 year ahead) – NIPE
Industrial 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
C(1)
C(2)
C(3)
C(4)
C(5)
C(6)
C(7)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
Coefficient
Std. Error
t-Statistic
Prob.
-3.552053
-0.122235
0.244945
0.018917
0.114072
-0.321027
4.161255
1.085872
0.031360
0.201851
0.020055
0.062951
0.095106
1.310199
-3.271154
-3.897829
1.213495
0.943274
1.812082
-3.375460
3.176048
0.0016
0.0002
0.2286
0.3485
0.0738
0.0012
0.0021
0.974054
0.972059
0.202483
3.197952
18.79620
2.055118
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
1.152626
1.211334
-0.277558
-0.076398
488.0473
0.000000
Short term (1 year ahead) – NIPE
Private 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
C(1)
C(2)
C(3)
C(31)
C(5)
C(6)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
Coefficient
Std. Error
t-Statistic
Prob.
0.524981
-0.086988
0.730296
-0.518618
0.505700
0.150281
0.275875
0.023660
0.237398
0.287777
0.113300
0.077989
1.902970
-3.676632
3.076251
-1.802153
4.463392
1.926964
0.0610
0.0005
0.0030
0.0757
0.0000
0.0579
0.988035
0.987204
0.159092
1.822334
35.82984
1.727423
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
3.123346
1.406408
-0.764868
-0.583582
1189.106
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 sector
1. 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 sector
1. 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
8
6
4
2
0
-2
96
97
98
99
euro area
00
01
France
02
03
Germany
04
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
Pays
commercial
services
Receives
commercial
services
Margin 2
Final
consumer
Receives
Pays
Pays
unit cost
unit cost
sell price
Receives sell
price
Margin 1
The reform of the retail sector
2. 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
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
7
6
5
4
3
2
1
0
-1
05M01
05M07
06M01
euro area
06M07
France
07M01
07M07
Germany
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
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 N
MARCH
Wage
agreement
1000 €
1000 €
1009 €
1020 €
W1 =1000€
W2 = 1009€
W3 = 1020€
Year N
JULY
SMIC
raise
1010 €
1010 €
1010€
(SMIC
effect
indexation)
1020 €
(no
indexation)
W1 =1000€
W2 = 1010€
W3 = 1020€
Year N+1
MARCH
Wage
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
Estimations
• Dependent variable: 1-labor share (TM)
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
Contributions
Variation du taux de marge et contributions entre 1995 et 2007
5.0
4.0
3.0
points
2.0
1.0
0.0
-1.0
-2.0
-3.0
-4.0
1995
1996
1997
coin social
taux d'intérêt réel lissé
tx d'ouverture
1998
1999
2000
2001
productivité
TUC
cale
2002
2003
2004
2005
2006
2007
smic réel
tx chômage
variation du taux de marge