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Master Économie et Affaires Internationales Cours Modèles de Simulation Paris Dauphine –October 2012 Prof. Dr. Ramón Mahía Applied Economics Department.

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Presentation on theme: "Master Économie et Affaires Internationales Cours Modèles de Simulation Paris Dauphine –October 2012 Prof. Dr. Ramón Mahía Applied Economics Department."— Presentation transcript:

1 Master Économie et Affaires Internationales Cours Modèles de Simulation Paris Dauphine –October 2012 Prof. Dr. Ramón Mahía Applied Economics Department SIMULATION MODELS IN ECONOMY SOME BASICS

2 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

3 SIMULATION MODELS: SOME BASICS WHAT DOES SIMULATION MEAN? And WHY DO WE NEED SIMULATION MODELS? PART I of III

4 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 understand real complex systems…in order to take or evaluate decisions.

5 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

6 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 decisions outputs, we would need IDEALLY to try out, to experiment with reality... …But obviously, most of the times we CANT make real tries for evaluating alternative decisions because it is simply impossible or very risky and/or expensive.

7 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. Thats 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….

8 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…..

9 SIMULATION MODELS: SOME BASICS EXAMPLES OF REAL SIMULATION MODELS PART II of III

10 SIMULATION MODELS: SOME BASICS 3 REAL EXAMPLES Simulating the impact of migration on pension system for (CES Project ): 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" "An Estimation of the Economic Impact of Migrant Access on GDP: the case of the Madrid Region"

11 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" "A Euro-Mediterranean Agricultural Trade Agreement: Benefits for the South and Costs for the EU"

12 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 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". "A Simulation of the Economic Impact of Renewable Energy Development in Morocco".

13 SIMULATION MODELS: SOME BASICS BASIC ELEMENTS, STAGES AND ADVICES FOR BULDING UP A SIMULATION MODEL PART II of III

14 SIMULATION MODELS: SOME BASICS BASIC ELEMENTS & STAGES FOR BIUILDING UP A SIMULATION MODEL (i) Real system draft (ii) Operative system representation (design) (iii) Identification and specification of variables (Inputs – Outputs) and links (simulation flow) (iv) Modeling (Technical core) (v) Interface (platform of use) (vi) Results (use of the model)

15 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.

16 SIMULATION MODELS: SOME BASICS BASIC STAGES FOR BIUILDING A SIMULATION MODEL: ELEMENTS AND DECISIONS

17 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).

18 SIMULATION MODELS: SOME BASICS BASIC STAGES FOR BIUILDING A SIMULATION MODEL: ELEMENTS AND DECISIONS

19 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

20 SIMULATION MODELS: SOME BASICS BASIC STAGES FOR BIUILDING A SIMULATION MODEL: ELEMENTS AND DECISIONS

21 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)

22 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..

23 SIMULATION MODELS: SOME BASICS BASIC STAGES FOR BIUILDING A SIMULATION MODEL: ELEMENTS AND DECISIONS

24 SIMULATION MODELS: SOME BASICS BASIC STAGES FOR BIUILDING A SIMULATION MODEL: ELEMENTS AND DECISIONS tt+1t+2t+3t+4t+5t+6t+7t+8

25 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 equilibrium of simulation or optimization solutions.

26 SIMULATION MODELS: SOME BASICS BASIC STAGES FOR BIUILDING A SIMULATION MODEL: ELEMENTS AND DECISIONS NATIONAL PRODUCERS YIELDS TARIFFS IMPORT PRICES IMPORT DEMAND DOMESTIC GROWTH ECONOMETRIC MODEL DOMESTIC DEMAND SUBSIDIES DOMESTIC PRICES ECONOMETRIC MODEL IDENTITY REST OF THE MODEL

27 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.)

28 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.)

29 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).

30 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.

31 SIMULATION MODELS: SOME BASICS BASIC ELEMENTS OF A SIMULATION MODEL (vi) Interface: Platform for using the model

32 SIMULATION MODELS: SOME BASICS BASIC ELEMENTS OF A SIMULATION MODEL (vi) Interface: Platform for using the model

33 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.)

34 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 dont underestimate) qualitative issues (or simply out of model facts) that could affect results.

35 SIMULATION MODELS: SOME BASICS BASIC ELEMENTS OF A SIMULATION MODEL INPUTSVALUES Host country demographicsHigh fertility variant Medium fertility variant Low fertility variant Host country economic growthHigh growth Medium growth Poor growth Crisis Immigration restrictionsNone Medium High Time interestShort term Medium term Long term TOTAL SCENARIOS108 TimeDemographics Economic growthRestrictions ScenarioProb. Short termMedium 115% PoorHigh 285% Medium TermMediumMedium.Medium 350% PoorHigh 430% CrisisHigh 520% Long TermHigh None 630% Medium None 740% LowPoorMedium 815% CrisisMedium 910% High 105% Possible combinations 108Selected = 10 # 2,4,8 = Baselines


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