Master Économie et Affaires Internationales Cours “ Modèles de Simulation ”

1. 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

2. OUTLINE • PartI: WHAT DOES SIMULATION MEAN? And WHY DO WE NEED SIMULATION MODELS? • PartII: EXAMPLES OF (OWN) REAL SIMULATION MODELS • PartIII: BASIC ELEMENTS, STAGES AND ADVICES FOR BULDING UP A SIMULATION MODEL SIMULATION MODELS: SOME BASICS

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

4. 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 toolthat help us to understandreal complex systems…in order to take or evaluate decisions. SIMULATION MODELS: SOME BASICS

5. 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

6. 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 IDEALLYto “try out”, to experiment with reality... • …But obviously, most of the times we CAN’T make real triesfor evaluating alternative decisions because it is simply impossible or very risky and/or expensive. SIMULATION MODELS: SOME BASICS

7. 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”= Optimizationsystems 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

8. 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

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

10. 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 MigrationJouurnalReview: • "AnEstimation of theEconomicImpact of Migrant Access on GDP: the case of the Madrid Region" SIMULATION MODELS: SOME BASICS

11. 3 REAL EXAMPLES • Removal of EU import barriers and evaluation of effects for third countries (exporters) (FEMISE – EC projects 2003,2004,2005,2006): • Econometricmodelshelpustoanticipate new tradeflows (changes in prices ►new importdemand ► exportflows) • 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 forexamples: • “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

12. 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 “EnergyPolicy” article: • "A Simulation of the Economic Impact of Renewable Energy Development in Morocco". SIMULATION MODELS: SOME BASICS

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

14. 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) SIMULATION MODELS: SOME BASICS

15. BASIC ELEMENTS & STAGES FOR BIUILDING UP A SIMULATION MODEL • (i) Real (whole) systemto 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

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

17. 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

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

19. 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

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

21. 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

22. 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

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

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

25. 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. SIMULATION MODELS: SOME BASICS

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

27. 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

28. 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

29. 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

30. 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

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

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

33. 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

34. 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

35. BASIC ELEMENTS OF A SIMULATION MODEL SIMULATION MODELS: SOME BASICS Possible combinations 108 Selected = 10 # 2,4,8 = Baselines