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Simulation Models in Economics:
Issues, Design, and
Implementation
Sherman Robinson
International Food Policy Research
Ins...
Outline
• Simulation models:
– Types
– issues
– design
– Implementation
• Impact model
• CGE models
• Estimation and valid...
Simulation Models
• Long history in economics
– Econometric Models used in “simulation mode”
– Models designed for simulat...
Types of Simulation Models
• Stylized: “putting numbers to theory”
– Small, focused models—close to theory
• Applied
– Lar...
Types of Simulation Models
• “Reduced form” versus “structural”
• Dynamic versus static
• Partial versus general equilibri...
“Reduced Form” Models
• Vague theoretical specification of relationships
among variables
– Econometric estimation: hypothe...
Structural Models
• Goal is to simulate “how” the economy works
– “Counterfactual” analysis: “What if” scenarios
– Control...
Structural Models
• Model elements: structural models
– Agents interacting, usually across markets
– Specification of agen...
Structural Models
• Partial equilibrium: commodity models
– Single market models
– Multimarket models
• Economywide models...
Structural Models
• In a structural model, must specify:
– Agents (producers, households)
• Economic actors in the model
–...
Structural Models
• Describe agent behavior mathematically
– Producers: supply behavior
• Production/cost functions, profi...
Deep/Shallow Structural Models
• “Deep” structural models
– explicit description of agent behavior
– Utility functions, pr...
Structural Models
• Agent based models:
– Opportunity, motive, ability
– Not enough to describe operation of the economy
•...
Structural Models
• Market equilibrium: how markets work
– Equilibrium conditions
• Supply = demand
– Equilibrating mechan...
Market Equilibrium in Models
• A descriptive feature: If market clearing is a
reasonable assumption, then we can use the
s...
Partial Equilibrium Models
• Single commodity or multimarket
– Do not cover the entire economy
• Supply and demand curves
...
Simulation Models: Issues
• Growth and structural change
– Investment/education
– Role of trade
– Productivity growth
– Ag...
Simulation Models : Issues
• Macro shocks and structural adjustment
• Income distribution
– Long run: poverty and growth
–...
Simulation Models : Issues
• Globalization
– Trade policy reform: GATT/WTO
– Regional trade agreements
• Customs unions: E...
Simulation Models: Issues
• Energy
– Energy “system” and the economy
– Oil price shocks
– Biofuels
• Environment/climate c...
Model Design: Aggregation
• Macro (aggregates: C, I, G, E, M)
– Macroeconometric models
– Asset markets and financial vari...
Implementation: Construction
• Explicit mathematical statement of theoretical
model
– Specify functional forms, endogenous...
Implementation: Validation
• Validation is linked to issues to be analyzed
– Focus of the model application
– Intended “do...
Multi-Market: IMPACT Model
• Impact is a suite of models:
– Core Impact multi-market global trade model
– “Water" model of...
Economywide CGE Models
• “General equilibrium”: many markets, factors
and commodities
– Simultaneous equilibrium across in...
CGE Model Design: Theory
• Walras-neoclassical-structuralist-Keynes:
theoretical roots
– Role of product and factor market...
CGE Models
• Numerical application of the Walrasian general
equilibrium model
– Market economy where a many agents maximiz...
Background
• Johansen 1960: MSG Model of Norway
– Still used for planning and forecasting
• 1970s: Confined mostly to univ...
30
What do we want to capture?
Factor markets
Factor market functioning
Segmentation
Wage determination
Economywide
Enviro...
Typical CGE Model Features
• Simulation model
– No forecasting or macro cyclical analysis
• “Micro-macro” model in structu...
CGE Models
• Actors: producers, consumers, government,
rest of the world
• Motivation: profit maximization, utility
maximi...
CGE Models
• System constraints:
– Resources (land, labor, capital),
– International: foreign trade balance
• Equilibrium ...
Stylized Model Structure
34
Activities
Commodity
Markets
Factor
Markets
Rest of the
World
Households Government Sav./Inv.
...
35
SAM Structure
Expenditures
Receipts
Activities Commodities Factors
Domestic
Institutions
Rest of
World
Totals
Activitie...
Solving CGE Models
• Direct approaches
– Scarf algorithm
– Log linearization (Johansen, Orani, GTAP)
– Simultaneous nonlin...
Calibration of CGE Models
• Equivalent to a “backward” solution of the
model in order to determine the set of
parameter va...
Estimation and Validation
• Define “domain of applicability” of model
• Econometric models: simultaneous estimation
and va...
Estimation and Validation
• Structural versus reduced-form models
– “Deep” behavioral parameters for structural
simulation...
Estimation and Validation
• Estimation using MaxEnt econometrics
– Zellner: “Efficient” information processing rule.
Use a...
Conclusion
• Gap between theory and empirical
implementation has narrowed
• Simulation models are widely used, and will
be...
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Core Training Presentations- 2 Models and Model History

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Global Futures & Strategic Foresight (GFSF) program enhances and uses a coordinated suite of biophysical and socioeconomic models to assess potential returns to investments in new agricultural technologies and policies. These models include IFPRI’s International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), hydrology and water supply-demand models, and the DSSAT suite of process-based crop models.

The program also provides tools and trainings to scientists and policy makers to undertake similar assessments.

GFSF program is a Consultative Group on International Agricultural Research (CGIAR) program led by the International Food Policy Research Institute (IFPRI)

Published in: Government & Nonprofit
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Core Training Presentations- 2 Models and Model History

  1. 1. Simulation Models in Economics: Issues, Design, and Implementation Sherman Robinson International Food Policy Research Institute (IFPRI)
  2. 2. Outline • Simulation models: – Types – issues – design – Implementation • Impact model • CGE models • Estimation and validation 2
  3. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 17. Simulation Models: Issues • Growth and structural change – Investment/education – Role of trade – Productivity growth – Agriculture/water/land – Industrialization • Long-run development strategies 18
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. 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
  27. 27. 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
  28. 28. 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
  29. 29. 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
  30. 30. 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
  31. 31. 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
  32. 32. 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
  33. 33. Stylized Model Structure 34 Activities Commodity Markets Factor Markets Rest of the World Households Government 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
  34. 34. 35 SAM Structure Expenditures Receipts Activities Commodities Factors Domestic Institutions Rest of World Totals Activities Market sales Home con- sumption Activity income Commodities Intermediate inputs Trans- actions costs Final market demands Exports Commodity demand Factors Value added Transfers Factor income Domestic Institutions Taxes Tariffs, Taxes Income, Taxes Transfers, Taxes, Savings Transfers, Savings Institution income Rest of World Imports Foreign exchange outflow Totals Activity spending Commodity supply Factor spending Institution spending Foreign exchange inflow
  35. 35. 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
  36. 36. 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
  37. 37. 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
  38. 38. 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
  39. 39. 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
  40. 40. 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

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