SIMULATION MODELS
IN ECONOMY
SOME BASICS
Master Économie et Affaires Internationales
Cours “Modèles de Simulation”
Paris Dauphine –October 2012
Prof. Dr. Ramón Mahía
Applied Economics Department
www.uam.es/ramon.mahia
SIMULATION
MODELS:
SOME BASICS
OUTLINE
Part I: WHAT DOES SIMULATION MEAN? And
WHY DO WE NEED SIMULATION MODELS?
Part
II:
EXAMPLES
OF
(OWN)
REAL
SIMULATION MODELS
Part III: BASIC ELEMENTS, STAGES AND
ADVICES FOR BULDING UP A SIMULATION
MODEL
SIMULATION MODELS:
SOME BASICS
PART I of III
WHAT DOES SIMULATION MEAN? And WHY
DO WE NEED SIMULATION MODELS?
SIMULATION
MODELS:
SOME BASICS
WHAT DOES SIMULATION MEAN?
• A simulation shows the expected working of a
system based on a model (simulation model).
Simulation means to “run”, to put in practice a
“simulation model”
• A “simulation model” is a technical tool that
help
us
to
systems…in
decisions.
understand
order
to
real
take
or
complex
evaluate
SIMULATION
MODELS:
SOME BASICS
WHAT DOES SIMULATION MEAN?
 Using a simulation tool, we can experiment in
real systems:
To Understand how the system works: how “inputs”
become “outputs”
To Evaluate alternative decisions
….or to find out the best set of inputs (decision) for
achieving a particular result / goal = Optimization
SIMULATION
MODELS:
SOME BASICS
WHY DO WE NEED SIMULATION MODELS?
 A real system use to be complex (not chaotic) : different “agents”
affecting lots of variables (elements) greatly interrelated in a way that …
…even if we can understand (or model) every single relationship, it is
difficult to anticipate and figure out the joint result
Of course we can try to to anticipate the result of a given decision
just
relying
on
experience,
intuition
or
theoretical
conceptions…but IDEALLY …
.. to understand the system and/or evaluate decision’s outputs, we
would need IDEALLY to “try out”, to experiment with reality...
…But obviously, most of the times we CAN’T make real tries for
evaluating alternative decisions because it is simply impossible or very
risky and/or expensive.
SIMULATION
MODELS:
SOME BASICS

MORE ON SIMULATION DEFINITION
Simulations Vs. Optimization

There are not Simulation Vs Optimization models but different
ways of use models :

“what if” = Simulation is an open strategy that uses the links
between inputs and outputs without setting an objective a priori or
the conditions for an optimum solution.

“how to”= Optimization systems concentrates mainly on reaching a
well predefined objective given a set of restrictions.

That’s why we usually say that simulation models are “run” and
optimization models are “solved”.

Most of the times, simulation looks like a natural previous stage for
optimization….
SIMULATION
MODELS:
SOME BASICS
MORE ON SIMULATION DEFINITION
 Example: Simulation Vs. Optimization: Replace a quota
regime by a “tariff only” system:
1.- OPTIMIZATION LIKE: Which is the tariff level
equivalent to an existing quota regime?
2.- SIMULATION LIKE: Different tariff levels help us to
evaluate different impacts on domestic producers (as a
basis to negotiate other EU compensations), foreign
producers, NON EU exporters, EU re-exporters, changes
on export prices, wholesale prices, consumer prices…..
SIMULATION MODELS:
SOME BASICS
PART II of III
EXAMPLES OF REAL SIMULATION
MODELS
SIMULATION
MODELS:
SOME BASICS
3 REAL EXAMPLES
 Simulating the impact of migration on pension
system for 2007- 2025 (CES Project 2006-07):
 Very complex and simultaneous interrelations between migration,
native demographical trends, structural economics, short terms
conditions, ..politics (show or draw picture)
Very dynamic exercise:
 outcomes in “t” affects “t+1”, “t+2”,… etc
 “k” variables x “t” periods = “k” x “t” inputs and/or outputs
Once again,… impossible to try out and impossible to risk a single
forecast output .
Lack of a single theoretical framework to be applied
Different qualitative issues (politics) to be considered: migration policy
design and application, future welfare state design …..
LINK to International Migration Jouurnal Review:

"An Estimation of the Economic Impact of Migrant Access on GDP: the case of the
Madrid Region"
SIMULATION
MODELS:
SOME BASICS
3 REAL EXAMPLES
 Removal of EU import barriers and evaluation of
effects for third countries (exporters) (FEMISE – EC
projects 2003,2004,2005,2006):
Econometric models help us to anticipate new trade flows
(changes in prices ►new import demand ► export flows)
IO Models help us to evaluate chained sector impacts in third
countries (you will learn how) obtaining detailed VA (GDP) and
employment impacts.
 A complementary Computable General Equilibrium model (CGE)
could help us to spread simulation through the whole economy of
the third country.
Two links for examples:


“An equilibrium model for Free Trade Area creation economic impacts estimation”
"A Euro-Mediterranean Agricultural Trade Agreement: Benefits for the South and
Costs for the EU"
SIMULATION
MODELS:
SOME BASICS
3 REAL EXAMPLES
 A simulation of the economic impact of renewable
energy development in Morocco (2012)
 An evaluation of RES economic impact in Morocco 2010 -2040
We identify the renewable energy source (RES) demand scenarios
for Morocco ► the needs of RES installed capacity according to
those scenarios and ► the detailed FDI plans needed to achieve
such installed capacity supply.
Then, using a dynamic variant input–output model, we simulate the
macroeconomic impact of the foreign investment inflows needed to
make available these Moroccan RES generation capacity plans in
the medium and long term.
Alternatives of CSP, PV and WP are compared
Link to “Energy Policy” article:

"A Simulation of the Economic Impact of Renewable Energy Development in
Morocco".
SIMULATION MODELS:
SOME BASICS
PART II of III
BASIC ELEMENTS, STAGES AND
ADVICES FOR BULDING UP A
SIMULATION MODEL
SIMULATION
MODELS:
SOME BASICS
BASIC ELEMENTS & STAGES FOR BIUILDING UP
A SIMULATION MODEL
(i) Real system “draft”
(ii) Operative system “representation” (design)
(iii)
Identification
“variables
(Inputs
and
–
specification
Outputs)
(simulation flow)
(iv) Modeling (Technical core)
(v) Interface (platform of use)
(vi) Results (use of the model)
and
of
“links”
SIMULATION
MODELS:
SOME BASICS
BASIC ELEMENTS & STAGES FOR BIUILDING UP
A SIMULATION MODEL
 (i) Real (whole) system to be analyzed: The complete
collection of elements and interactions to be analysed by means of
the simulation.
My advice: The largest part of the technical decisions regarding the
estimation, calibration, design of scenarios and interface rely on and
are conditioned by a good comprehension of the elements and
interrelations of the whole system to be analysed….so
You will need to STUDY IN DEPTH until you get a complete sketch
of the real framework of the whole system: different parts (sub-
systems) should be recognized, every element and every relevant
connection properly acknowledged even if your fundamental interest
is focused in just a single part.
SIMULATION
MODELS:
SOME BASICS
BASIC STAGES FOR BIUILDING A SIMULATION
MODEL: ELEMENTS AND DECISIONS
SIMULATION
MODELS:
SOME BASICS
BASIC ELEMENTS & STAGES FOR BIUILDING UP
A SIMULATION MODEL
 (ii) System “representation”: Simplified and
limited version of the real system
A good simulation model BALANCE the compromise between
realism and simplicity…
…Then, in a second stage, you SHOULD identify the “reduced”
representation of the system that best fit YOUR simulation aims:
leave out some complete parts, reduce elements of interest and drop
useless relationships (never forget, of course, those rejected
variables and links, in case you need them later on, and bear them
always in mind for a broad and wide range comprehension of the
final results).
SIMULATION
MODELS:
SOME BASICS
BASIC STAGES FOR BIUILDING A SIMULATION
MODEL: ELEMENTS AND DECISIONS
SIMULATION
MODELS:
SOME BASICS
BASIC ELEMENTS & STAGES FOR BIUILDING UP
A SIMULATION MODEL
 (iii) Variables:
Inputs:
(***) Stimulus Inputs (decision or critical): main variables to
be changed when simulating
Exogenous Inputs (out of model, usually fixed or very limited in
variation, frequently qualitative, ideally not critical,..)
Outputs:
Intermediate outputs (state and auxiliary variables, or
estimated parameters)
(***) Final outputs
SIMULATION
MODELS:
SOME BASICS
BASIC STAGES FOR BIUILDING A SIMULATION
MODEL: ELEMENTS AND DECISIONS
SIMULATION
MODELS:
SOME BASICS
BASIC ELEMENTS & STAGES FOR BIUILDING UP
A SIMULATION MODEL
 (iii) Simulation flow structure: Structured
scheme that illustrate the connection between
different variables: cause – effect chains
Simplify the flow along the cause – effect chains (reduce
dimensionality, look for a semi - linear design)
Rationalize chain flows: prioritize inputs and outputs, give them
hierarchical order, and then…
Divide the system in homogeneous parts for planning the work
across areas. Locate the links between the different areas and order
the stages, identifying the priorities, bottlenecks and crucial points.
…(cont)
SIMULATION
MODELS:
SOME BASICS
BASIC ELEMENTS & STAGES FOR BIUILDING UP
A SIMULATION MODEL
 (iii) Simulation flow structure: (cont.)..
Plan a preliminary time work modeling schedule
according to:
“In model” factors: the previous identification of
lines, crossing points and bottlenecks
“Out of model” factors: existing organization of
areas, the resources available, the difficulty of
different tasks..
SIMULATION
MODELS:
SOME BASICS
BASIC STAGES FOR BIUILDING A SIMULATION
MODEL: ELEMENTS AND DECISIONS
SIMULATION
MODELS:
SOME
BASICS
t
BASIC STAGES FOR BIUILDING A SIMULATION
MODEL: ELEMENTS AND DECISIONS
t+1
t+2
t+3
t+4
t+5
t+6
t+7
t+8
SIMULATION
MODELS:
SOME BASICS
BASIC STAGES FOR BIUILDING A SIMULATION
MODEL: ELEMENTS AND DECISIONS
 (iv) Technical structure: Quantitative definition of
elements (variables) and links (equations) between
them including:
1.- Collection of data for every variable (element)
2.-
Mathematical
(for
deterministic
links)
and/or
statistical models (for randomness)
3.- Mathematical and/or statistical algorithms to describe
and
validate
convergence
and/or
simulation or optimization solutions.
equilibrium
of
SIMULATION
MODELS:
SOME BASICS
BASIC STAGES FOR BIUILDING A SIMULATION
MODEL: ELEMENTS AND DECISIONS
TARIFFS
IMPORT
PRICES
ECONOMETRIC
MODEL
IMPORT
DEMAND
DOMESTIC
GROWTH
ECONOMETRIC
MODEL
DOMESTIC
PRICES
SUBSIDIES
IDENTITY
DOMESTIC
DEMAND
NATIONAL PRODUCERS YIELDS
REST OF THE MODEL
SIMULATION
MODELS:
SOME BASICS

BASIC STAGES FOR BIUILDING A SIMULATION
MODEL: ELEMENTS AND DECISIONS
(v) Technical Structure (ADVICES):
Concentrate on data (Carpenters say "Measure twice, cut
once“).
 Carefully supervise your “raw material”: use homogeneous data,
ensure the future availability of them, choose the samples
carefully, be extremely scrupulous in the handling of data.

Use the data provided by the end user, agree with them if data
responds truthfully to “their” perception of reality.
Explore the analytical - mathematical – statistical
procedures that best adapt to the system and your aims.
(Cont.)
SIMULATION
MODELS:
SOME BASICS

BASIC STAGES FOR BIUILDING A SIMULATION
MODEL: ELEMENTS AND DECISIONS
(v) Technical Structure (ADVICES):
Try to adapt the analytical technique to the problem
and not the other way round (models MUST be useful
and suit the problem, not technically attractive or
handsome)
Let
simplicity
guide
your
decisions.
Do
not
complicate the technical models if doesn't lead to sound
benefits from the user perspective (“If your intention is to
discover the truth, do it with simplicity and lave the
elegance for the tailors“ A. Eisntein)
 (Cont.)
SIMULATION
MODELS:
SOME BASICS

BASIC STAGES FOR BIUILDING A SIMULATION
MODEL: ELEMENTS AND DECISIONS
(v) Technical Structure (ADVICES):
Be cautious with stochastic components:
If you can, try to avoid critical dependency on stochastic
estimations: if inferential statistics are used, not only the final,
BUT the INTERMEDIATE outcomes would vary in a confidence
interval so you should carefully check the “sensitivity” of the
WHOLE system to EVERY coefficient change
... Think “seriously” about if/how re-estimations will be
addressed in the future.
Try (never easy) to offer results in an confidence interval – way
(providing values and probabilities).
SIMULATION
MODELS:
SOME BASICS
BASIC ELEMENTS OF A SIMULATION MODEL
 (vi Interface: Platform for using the model
Sometimes is not necessary (self use)
Call for software professionals (if you have lots of money)
Let simplicity guide the design of the interface: The
interface is wished for using the model, not for understanding
the model: The “model” COULD be COMPLEX, but the
interface MUST be FRIENDLY:
Prioritise the wishes of users in all the stages and take their
advices
Set different levels of use: Decision makers, medium level
technicians, high skilled technical experts, etc... “There is no
inept user, only badly designed systems”.
SIMULATION
MODELS:
SOME BASICS
BASIC ELEMENTS OF A SIMULATION MODEL
 (vi) Interface: Platform for using the model
SIMULATION
MODELS:
SOME BASICS
BASIC ELEMENTS OF A SIMULATION MODEL
 (vi) Interface: Platform for using the model
SIMULATION
MODELS:
SOME BASICS
BASIC ELEMENTS OF A SIMULATION MODEL
 (vi) Using the model:
(**) Scenario: a set of inputs and parameters considered for
a simulation exercise
 When several inputs are taken, lots of potential variant
scenarios arises
 For reducing dimensionality:
Try to identify tree-structures (if possible) identifying
hierarchical connections of different inputs
“Pode the tree”: Drop impossible, hardly probable, not
interesting and not different scenarios.
Order the final list, select baseline and alternatives
(Cont.)
SIMULATION
MODELS:
SOME BASICS
BASIC ELEMENTS OF A SIMULATION MODEL
 (vi) Using the model:
Give probabilities to different scenarios (use conditional
probabilities if a tree scheme is used)
Evaluate the output:
Offer a kind of result that jointly evaluates the probability
of the outcome and the magnitude of it
Once you get results for each given scenario, clearly
identify the sensitivity of results to changes in every inputs.
Identify (and don’t underestimate) qualitative issues (or
simply out of model facts) that could affect results.
SIMULATION
MODELS:
SOME BASICS
BASIC ELEMENTS OF A SIMULATION MODEL
INPUTS
Host country demographics
Host country economic growth
Immigration restrictions
Time interest
TOTAL SCENARIOS
VALUES
High fertility variant
Medium fertility variant
Low fertility variant
High growth
Medium growth
Poor growth
Crisis
None
Medium
High
Short term
Medium term
Long term
108
Possible combinations 108
Economic
Time
Demographics growth
Short term Medium
Medium
Medium
Term
Medium
Long Term High
Medium
Low
Selected = 10
Restrictions Scenario
1
Medium
Prob.
15%
Poor
High
2
85%
Medium.
Medium
3
50%
Poor
Crisis
High
High
High
None
4
30%
5
6
20%
30%
Medium
Poor
Crisis
None
Medium
Medium
High
7
40%
8
9
10
15%
10%
5%
# 2,4,8 = Baselines