MINISTERO DELL‟ECONOMIA E DELLE FINANZE
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STUDY VISIT ALBANIAN MINISTRY OF FINANCE
Econometric models at the
Department of the Treasury
Alessandra Caretta, Enrico D’Elia, Fabio Di Dio, Francesco Felici, Cecilia Frale, Libero Monteforte,
Francesco Nucci, Daniele Pacifico, Cristian Tegami
Macroeconomic models and forecasting
 Bridge Models
 TRILL
 ITEM Econometric Model on Italy's Economy
 JBM
 OXFORD Global Economic Model
 Quest III - Quarterly European Simulation Tool
 IGEM
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THE PROCESS
Technical steps
 Fixing of exogenous variables
 Renormalization: taking into account the most recent data
 Run forecast with different models
 Set a starting baseline for the macroeconomic projections
 Iterate with Public finance forecasts and budget measures
 Include the recent developments in revenues and expenditures
 From the “unchanged legislation” to the budget measures
 Produce potential output estimates and structural balance
indicators
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THE PROCESS
Institutional procedures
 A number of units are involved
 Main task for the unit in charge of the Planning documents
 Labour market unit, price unit and others contribute as well
 The common view of the Directorate: The Working group on
modelling and forecasting
 The interaction with other Departments:
 General Accounting Department (Ragioneria Generale dello Stato)
 Finance Department (Dipartimento delle Politiche fiscali)
 Feedback from the Public Debt Management Directorate
 Interest expenditure model
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TOOLS: BRIDGE MODELS
Short term GDP forecast: Bridge models
 Short time forecast models are created for the needs of a
continuous monitoring of Italian economy when the official
statistics are not yet disposable.
 The forecasts are one/two periods ahead. The variable to predict
is the Italian quarterly GDP and its components.
 Bridge models are a kind of models which link the high frequency
indicators (monthly) to the aggregate of interest for the prediction
(quarterly).
 The indicators are splitted in two categories: hard data (e.g.:
industrial production) and soft data (e.g.: confidence indicator)
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TOOLS: BRIDGE MODELS
Short term GDP forecast: Bridge models
 The relationship between GDP and the monthly indicators is
estimated at quarterly frequency. If the indicators are not available
for all the three months of the quarter, they are projected at
monthly frequency with satellite models (ARIMA).
 Let‟s the bridge equation Y=a + b1X1 + b2X2 + b3X3 with X1,
X2, X3 the monthly indicators, we could have the following set of
information (dashed lines indicate forecast):
Q1
Quarterly
Monthly
Q2
Y
M1
M2
M3
X1
X2
X3
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M4
M5
M6
TOOLS: BRIDGE MODELS
Bridge models: the indicators
SUPPLY SIDE
DEMAND SIDE
Value added of industry
Private consumption
Industrial production
Car registrations
Industrial production of durable goods
Italian stock market index
Volumes of consumer expenditures in services
and non-durable goods
Gross fixed investment
Non industry VA
Industrial production of investment goods
Car registrations
Price index of raw materials
Industrial production
Car registrations
World demand
Real short term interest rate
Total job positions in services
Import and Export
Monthly foreign trade statistics
Real effective exchange rate
PPI
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TOOLS: TRILL
Mixed Frequency Models
 After the recent financial and economic crisis there is an
increasing demand for macroeconometric models able to
predict the state of the economy and to capture early
signals of turning points, especially with the aim of defining
an effective economic policy.
 Classical models for short term forecast used by
Institutions, such as bridge models and standard factor
models, have shown some limitations, especially as
regards the time aggregation and the ragged-edge data
problem.
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TOOLS: TRILL
Treasury’s mixed frequency model: TRILL
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TOOLS: TRILL
Treasury’s mixed frequency model: TRILL
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TOOLS: TRILL
Treasury’s mixed frequency model: TRILL
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TOOLS: TRILL
GDP FORECASTS: FAN CHARTS
3
2
Monthly GDP (yoy)
1
0
-1
-2
-3
-4
-5
-6
2009
2009(4)
2009(7)
2009(10)
2010
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2010(4)
2010(7)
TOOLS: ITEM
The quarterly macroeconometric model:ITEM
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TOOLS: ITEM
Model Utilization
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TOOLS: ITEM
The supply side in ITEM
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TOOLS: ITEM
Production factors demand
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TOOLS: ITEM
TFP and the cycle
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TOOLS: ITEM
TFP modelling
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TOOLS: ITEM
TFP modelling
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TOOLS: ITEM
Demand side
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TOOLS: ITEM
Prices and wages
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TOOLS: ITEM
The labour market
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TOOLS: ITEM
Model properties as a response to different shocks
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TOOLS: ITEM
Demand shock simulations
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TOOLS: ITEM
World trade shock: Demand side view
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TOOLS: ITEM
World trade shock: Supply side view...”growth accounting” approach?
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TOOLS: JBM
How to reconcile in a unique forecast: JBM
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TOOLS: OTHER
International model: Oxford Global Economic Model
The „core‟ Oxford Global Model comprises forty-four country models together with
headline indicators for another 33.
There are also six trading blocs to complete the world coverage. The country models
are fully interlinked via trade, prices, exchange rates and interest rates, with the blocs
completing all the world coverage
In addition, the model includes a bloc of world variables such as oil and commodity
prices, world GDP and industrial production, OECD average inflation, aggregates
covering the Eurozone group etc.
The database is updated monthly with new historical data and a 10 years out of sample
forecast section.
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TOOLS: QUEST
Quest III - Quarterly European Simulation Tool
 QUEST is a large-scale Dynamic Stochastic General Equilibrium (DSGE)
model. It is one of the latest versions of the class of DSGE models developed
by the European Commission.
 It is a simulation tool mainly employed to analyze the effects of structural
reforms and the response of the economy to a variety of shocks.
 In our simulation exercises we use the version of the model calibrated for
Italy, already employed by the Commission in multi-country analyses of
structural reforms by the European Commission (e.g. D'Auria et al. 2009).
 This version of QUEST III is augmented with R&D. The endogenous
mechanism of growth allows to study policies aimed at increasing the rate of
knowledge creation
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TOOLS: QUEST
Quest III - Quarterly European Simulation Tool
 As DSGE models QUEST integrates typical Keynesian elements (such as
imperfect competition and frictions in price and wage setting) into a dynamic
general equilibrium framework
 Equilibrium conditions for the main aggregate variables are derived from the
optimising behaviour of households and firms, and combined with the market
clearing conditions.
 The calibrated (or estimated) parameters represent deep structural
parameters and are thus independent of the conduct of monetary and fiscal
policy (not subject to the Lucas (1976) critique)
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TOOLS: QUEST
Quest III - Quarterly European Simulation Tool
 By including several nominal and real frictions, modelling markets as
imperfectly competitive, the model can be used to study the effects of
competition-enhancing policy
 Distinction of employment in three skill categories (low, medium and high)
allows to analyze the effects of specific labor market policies like increasing the
social benefits for low-skilled workers, changing the skill composition of the labour
force, promoting high skilled immigration policies and subsidizing employment of
the high-skilled workers in the R&D sector
 Optimizing households (non liquidity constrained households) and hand-tomouth consumers (differentiation necessary to reproduce empirically relevant
Keynesian effects of fiscal policy)
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TOOLS: IGEM
IGEM: A new DSGE for Italy
 A new DSGE for the Italian economy has now been developed to match some
main features of Italy‟s economy (especially in the labor market)
 In this model we have heterogeneous workers with different contract types: two
types of employees (skilled and unkilled, very high firing and hiring costs, trade
unions), self-employed (high efficiency, low hiring and firing costs, no market
power) and atypical (low efficiency, low hiring and firing costs, no market power,
as consumers no access to financial markets)
 Employment and wage response to shocks will depend on the contract
 Except for the labor market, the model contains the main mechanisms of
standard DSGE models (e.g., real and nominal frictions as in QUEST)
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Microeconomic models for the labour market
and long term dynamics
 EconLav Microsimulation Model
 T-DYMM
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TOOLS: EconLav
Static and behavioral Microsimulation model for the Italian
households
 Microsimulation models are a powerful tools for the ex-ante evaluation of
fiscal reforms and for the assessment of the distributive impact of the
actual tax-benefit system.
 Static microsimulation models define a one-to-one mapping between gross
and net individual earnings, through a precise parameterization of the
Italian fiscal roles.
 Therefore, it is possible to evaluate a fiscal reform by studying how the
individual net income changes after the implementation of a new policy.
 Specifically, the static model allows determining winners and losers of a
given reform, something that is possible neither with macroeconomic
models nor from simulation models based on the fiscal declarations held by
the Ministry, which contain only few information about a given fiscal record.
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TOOLS: EconLav
EconLav: A Microsimulation model on workers
 EconLav is based on a representative survey of microdata, which
collects detailed information on individual net incomes and wealth.
 The static part of the model recovers the gross earnings of each
individual of the sample via an iterative procedure.
 The primary outcomes of the static part are:
1. A (single) gross income for each individual;
2. Net tax liabilities and their sub-components (i.e. tax deductions
and tax credits), Social contributions and benefit entitlements.
 Secondary outcomes are:
1. Fiscal households;
2. Tax evasion;
3. Distributive and poverty indices
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TOOLS: EconLav
EconLav: A Microsimulation model on workers
 EconLav simulates the most important Italian taxes (and all their
components):
1. Personal income tax (IRPEF)
2. Value added tax (IVA)
3. Local property tax (ICI)
4. Other minor direct and indirect taxes
 EconLav simulates the most important benefits:
1. Family-related benefits (ANF)
2. Additional allowance for families with more than 3 children
3. Other minor benefits (Social card, etc.)
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TOOLS: EconLav
EconLav: A Microsimulation model on workers
 The behavioral part of EconLav is based on a structural micro-econometric
model of labor supply and allows evaluating the effect of fiscal reforms in
terms of work incentives (either inner and outer margins).
 The econometric model is based on the assumptions of optimizing agents
and a (flexible) specification of a random utility function.
 The model uses a discrete approach, meaning that the household chooses
among a finite set of working hours in order to maximize its utility.
 A discrete approach implies estimating the probabilities of choosing each
hours point by recovering the structural parameters of the utility function.
 Therefore, it is possible to compute individual labor supply elasticities
numerically for each household after the estimation.
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TOOLS: EconLav
EconLav: A Microsimulation model on workers
 The econometric model allows for important innovation: errors in
wage predictions for non workers, unobserved preference
heterogeneity in the marginal utilities, fixed unobserved costs of
working, joint work decisions for married couples.
 Both modules of EconLav are written in Stata 11
 The Static and behavioral modules together give an overall
assessment of the reform from the household point of view: the static
part allows analyzing the distributive effects of the reform (winning
and losing households), whilst the behavioral part allows studying its
efficiency effects (i.e. the work incentives of each household)
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TOOLS: T-DYMM
T-DYMM: Tresury’s Dynamic Microsimulation model
The Model is developed inside a European project that aims at:
 Developing a unique and innovative dataset by matching, through
individual fiscal codes, administrative longitudinal data coming from
INPS (National Institute of Social Security) with survey data produced
by ISTAT (National InstituteofStatistics).
 Developing a dynamic micro‐simulation model for Italy.
Endowing the Ministry with a policy evaluation tool for the analysis of
adequacy and sustainability of the Pension System.
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TOOLS: T-DYMM
T-DYMM: The Institutional context
Italian pension system main reforms:
 Amato („92) - increase the retirement age and link the benefit to the average of the
overall working life remuneration
 Dini („95): relevant change to a system based on total social contribution along the
working life
 Tremonti - Maroni („04): cut the transition period for the full application of Dini („95),
relevant change of the role of the Tfr (leaving indemnity)
 Budget Law 2007 - smoother transition; introduction of a score system for eligibility to
retirement (combination of age and contributions years)
 Budget Law 2010 - Increase the age of retirement for female in the public sector so as
that for male
The introduction of the system based on total social contributions
requires to track the career path in order to evaluate the expected
benefit from the retirement
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TOOLS: T-DYMM
T-DYMM: Structure of the model
The Model is bases on sequential modules that reproduce the main
characteristics of the Italian society about:
 Demographic
 Labour market
 Pension System
 Taxation
Demographic and social relationships are supposed to evolve over time
according to ISTAT ( AWG) projections in the long run up to 2050
Dynamics is achieved essentially by updating attributes of each
micro-unit for each time interval
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TOOLS: T-DYMM
New features
Main features
T-DYMM: The Pension Module
 Classification of individuals in the different pension schemes (retributivo, misto,
contributivo) and funds (Fondo pensione lavoratori dipendenti, Autonomi, Gestione
separata, etc.)
 Specification of requirements for eligibility to retirement benefits according to the
reform process
 Computing different kinds of retirement benefits (IVS)
 Better prediction of future incomes/pensions thanks to the availability of
occupational history (INPS) at micro level linked to socio‐demographic
characteristics (IT SILC)
 Within group distributional analysis
 Adequacy analysis
 Allows to introduce migration and behavioral responses
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