1. Simulation Models in Economics: Issues, Design, and Implementation Sherman Robinson International Food Policy Research Institute (IFPRI)

2. Outline Simulation models: – Types – issues – design – Implementation Impact model CGE models Estimation and validation 2

3. Simulation Models Long history in economics – Econometric Models used in “simulation mode” – Models designed for simulation Level of aggregation – World models – Country models – Regional/sub-regional models – Enterprise/farm models 3

4. Types of Simulation Models Stylized: “putting numbers to theory” – Small, focused models—close to theory Applied – Larger, more detail (including institutions) – Broader range of issues Policy models – Explicit links between policy parameters and economic outcomes 4

5. Types of Simulation Models “Reduced form” versus “structural” Dynamic versus static Partial versus general equilibrium Coverage – household/village/region/country/globe Domain of application – “Universe” of the model 5

6. “Reduced Form” Models Vague theoretical specification of relationships among variables – Econometric estimation: hypothesis testing – Unidentified/unidentifiable structural model Simulation mode: forecasting – E.g., macroeconometric models – Goal is to forecast endogenous variables, given projections of exogenous variables – Less interested in “how” the economy works 6

7. Structural Models Goal is to simulate “how” the economy works – “Counterfactual” analysis: “What if” scenarios – Controlled experiments: parameters/policies causal chains/large numbers Model elements – Specify agents, technology, markets, institutions, signals, motivation, and behavior – “Domain” of the model 7

8. Structural Models Model elements: structural models – Agents interacting, usually across markets – Specification of agent behavior – Specify institutional structure – Notions of equilibrium Partial versus general equilibrium Static versus dynamic 8

9. Structural Models Partial equilibrium: commodity models – Single market models – Multimarket models Economywide models – “Economy” may vary in size and domain – Macro models – General equilibrium models – Microsimulation household models 9

10. Structural Models In a structural model, must specify: – Agents (producers, households) Economic actors in the model – Motivation (profit maximizing producers, utility maximizing consumers) – Signals (prices in markets) – Institutional structure (competitive markets) “Rules of the game” 10

11. Structural Models Describe agent behavior mathematically – Producers: supply behavior Production/cost functions, profit maximization – Input demand (K, L, Land, intermediate inputs) Supply curves (marginal cost function?) – Consumers: demand behavior Utility functions, utility maximization – Income, expenditure equations Demand curves (Marshallian?) 11

12. Deep/Shallow Structural Models “Deep” structural models – explicit description of agent behavior – Utility functions, production/cost functions – Relevant factor and commodity markets “Shallow” structural models – Supply/demand functions which summarize agent behavior (“reduced form” equations) – Only loosely based on theory 12

13. Structural Models Agent based models: – Opportunity, motive, ability – Not enough to describe operation of the economy Additional “constraints” on the economy – System constraints Supplies of primary factors (land, labor, capital) – Equilibrium conditions Supply-demand balance in all markets 13

14. Structural Models Market equilibrium: how markets work – Equilibrium conditions Supply = demand – Equilibrating mechanisms Price responsive supply and demand functions International trade – Equilibrating variables Commodity and factor prices, domestic and global 14

15. Market Equilibrium in Models A descriptive feature: If market clearing is a reasonable assumption, then we can use the specification to describe a realistic result – Solve for market-clearing prices in the model, which then correspond to actual prices No need to specify the exact process by which markets equilibrate, just the result – Powerful tool to simplify structural models 15

16. Partial Equilibrium Models Single commodity or multimarket – Do not cover the entire economy Supply and demand curves – Linear or nonlinear, loosely based on theory – Expenditure functions may or may not be based on demand theory – “Shallow” structural models: reduced form equations 16

17. Macro-Simulation Models Set up as a system of nonlinear, dynamic, difference/differential equations (e.g., Prescott, Sargent, Fair): DSGE models Simple representation of markets and high level of aggregation: very “stylized” Complicated adjustment mechanisms – Stochastic: Monte Carlo methods Macroeconometric vs “structural” models 17

18. Simulation Models: Issues Growth and structural change – Investment/education – Role of trade – Productivity growth – Agriculture/water/land – Industrialization Long-run development strategies 18

19. Simulation Models : Issues Macro shocks and structural adjustment Income distribution – Long run: poverty and growth – Short run: impact of macro adjustment Fiscal policy – Tax system design and/or reform – Government expenditure policy 19

20. Simulation Models : Issues Globalization – Trade policy reform: GATT/WTO – Regional trade agreements Customs unions: EU, Mercosur FTA’s: NAFTA, bilaterals, etc. Preferential access: Cotonou, EBA, AGOA,etc – Domestic policy reforms and trade system Impact of OECD agricultural policies 20

21. Simulation Models: Issues Energy – Energy “system” and the economy – Oil price shocks – Biofuels Environment/climate change – Costs of environmental policy – Climate change: mitigation/adaptation 21

22. Model Design: Aggregation Macro (aggregates: C, I, G, E, M) – Macroeconometric models – Asset markets and financial variables Micro (household/firm/farm analysis) – Microsimulation models Mezzo (sectors: multi-market and CGE) – Structure of production, employment, trade, etc. 22

23. Implementation: Construction Explicit mathematical statement of theoretical model – Specify functional forms, endogenous variables, parameters, and exogenous variables – Transforms inputs to outputs Computer code: modeling languages – GAMS, Matlab, Mathematica, Stella, Vensim, system dynamics 23

24. Implementation: Validation Validation is linked to issues to be analyzed – Focus of the model application – Intended “domain of applicability” of the model Need to “test” the model with historical data relevant to its domain of applicability – How well does the model “explain” past events? – How well does it capture the important causal chains? Validity of the underlying deep/shallow structural model 24

25. Multi-Market: IMPACT Model Impact is a suite of models: – Core Impact multi-market global trade model – “Water" model of FPU river basins, – “Water stress" model that converts hydrological output into yield shocks – Crop models – Biofuels, livestock, and fish models – Links to GCM climate change models 25

26. Economywide CGE Models “General equilibrium”: many markets, factors and commodities – Simultaneous equilibrium across inter-dependent markets “Behavior” consistent with general equilibrium theory – Deep structural relations 26

27. CGE Model Design: Theory Walras-neoclassical-structuralist-Keynes: theoretical roots – Role of product and factor markets – Role of assets and financial markets Dynamic versus static – Time horizon: short, medium, long – Notion of equilibrium: flows and stocks Rational expectations, forward looking, etc. 27

28. CGE Models Numerical application of the Walrasian general equilibrium model – Market economy where a many agents maximize their objective functions (utility or profit) subject to their constraints (budget or technology) – Single-period, static model Equilibrium model – No global objective function – Optimizing, price-responsive behavior of individual actors – Complete specification of both supply and demand sides of all markets (goods and factors) 28

29. Background Johansen 1960: MSG Model of Norway – Still used for planning and forecasting 1970s: Confined mostly to universities and research institutes 1980s and beyond: wider use (including government agencies in many countries) 29

30. 30 What do we want to capture? Factor markets Factor market functioning Segmentation Wage determination Economywide Environment Households Structural features Binding macro constraints General Equilibrium effects Heterogeneity Human and physical capital Demographic Composition Preferences Access to Markets

31. Typical CGE Model Features Simulation model – No forecasting or macro cyclical analysis “Micro-macro” model in structure – Explicit specification of micro/agent behavior – Simultaneous economywide and micro outcomes Set up in “real” terms: – No asset markets, – Money is neutral, – Decisions are a function of relative prices Representative household assumption 31

32. CGE Models Actors: producers, consumers, government, rest of the world Motivation: profit maximization, utility maximization Institutions and signals: competitive markets and prices Agent constraints: technology, factor endowments (budget constraints) 32

33. CGE Models System constraints: – Resources (land, labor, capital), – International: foreign trade balance Equilibrium conditions: – Supply-demand balance in all markets – Macro balances: government, savings-investment, foreign trade balance 33

34. Stylized Model Structure 34 Activities Commodity Markets Factor Markets Rest of the World HouseholdsGovernment Sav./Inv. Factor Costs Wages & Rents Intermediate Input Cost Sales Private Consumption Taxes Domestic Private Savings Government Consumption Gov. Savings Investment Demand ImportsExports Foreign Savings Transfers Foreign Transfers

35. 35 SAM Structure

36. Solving CGE Models Direct approaches – Scarf algorithm – Log linearization (Johansen, Orani, GTAP) – Simultaneous nonlinear equations Scarf algorithm. Tâtonnement algorithms Newton techniques (GAMS) Optimization methods – Negishi Theorem (Ginsburgh-Waelbroeck-Keyzer) – Nonlinear programming problem (NLP) – Shadow prices = market prices 36

37. Calibration of CGE Models Equivalent to a “backward” solution of the model in order to determine the set of parameter values consistent with the initial structure of the economy. Assume that the initial data (e.g., SAM) represent an equilibrium model solution. – Share parameters from SAM data. – Elasticity parameters from other sources. 37

38. Estimation and Validation Define “domain of applicability” of model Econometric models: simultaneous estimation and validation – Sample data used for both parameter estimation and within-sample “prediction” of endogenous variables (validation). With lots of data, one can save some data for separate validation exercise. Notion of “information” for estimation and validation 38

39. Estimation and Validation Structural versus reduced-form models – “Deep” behavioral parameters for structural simulation models Tastes, technology, and institutions – Issue of use of prior information about parameters in estimation Separation of estimation and validation Not enough data to do both simultaneously Need to use variety of information 39

40. Estimation and Validation Estimation using MaxEnt econometrics – Zellner: “Efficient” information processing rule. Use all, but only, the information available. Do not assume information you do not have. – Use of prior information on parameters Bayesian in spirit, but not formal Bayesian estimation Distinction between “precision” and “prediction” – Tradeoffs, different from classical regression analysis 40

41. Conclusion Gap between theory and empirical implementation has narrowed Simulation models are widely used, and will become even more common Advances in econometrics applicable to structural parameter estimation: – Information theoretic estimation methods 41