moorthy ms

Mixed Structural and Behavioral Models for Predicting the Future Behavior of some Aspects of the Macroeconomy: 

Mixed Structural and Behavioral Models for Predicting the Future Behavior of some Aspects of the Macroeconomy Mukund Moorthy 2nd February 1999

Contents: 

Contents Economic Modeling System Dynamics Fuzzy Inductive Reasoning Proposed Macroeconomic Model Food Demand Modeling Conclusion

Economic Modeling: 

Economic Modeling Economic Forecasting Techniques Time Series Data Neural Networks

Time Series Data: 

Time Series Data Time Series Components Trend ( T ) Cyclical ( C ) Seasonal ( S ) Irregular ( I )

Curve Fitting: 

Curve Fitting Linear Trend Equation

Curve Fitting: 

Curve Fitting Exponential Trend Equation Polynomial Trend Equation

Smoothing Techniques: 

Smoothing Techniques Moving Average each point is average of N points Exponential Smoothing

Time Series Forecasting: 

Time Series Forecasting Box-Jenkins Method

Economic Forecasting: 

Economic Forecasting Step-wise Auto-regressive method Neural Networks

System Dynamics: 

System Dynamics Modeling Dynamic Systems Information feedback loops

System Dynamics: 

System Dynamics Levels Flow Rates Decision Functions

System Dynamics: 

System Dynamics Levels and Rates Laundry List

Structure Diagram: 

Structure Diagram

Forrester’s World Model: 

Forrester’s World Model Population Capital Investment Unrecoverable Natural Resources Fraction of Capital Invested in the Agricultural Sector Pollution

Structure Diagram of Forrester’s World Model: 

Structure Diagram of Forrester’s World Model

Shortcomings of the World Model: 

Shortcomings of the World Model Levels and Rates Laundry List

Fuzzy Inductive Reasoning: 

Fuzzy Inductive Reasoning Discretization of quantitative information (Fuzzy Recoding) Reasoning about discrete categories (Qualitative Modeling) Inferring consequences about categories (Qualitative Simulation) Interpolation between neighboring categories using fuzzy logic (Fuzzy Regeneration)

Fuzzy Inductive Reasoning: 

Fuzzy Inductive Reasoning Mixed Quantitative/Qualitative Modeling

Fuzzification: 

Fuzzification

Inductive Modeling: 

Inductive Modeling

Inductive Simulation: 

Inductive Simulation

Modeling the Error: 

Modeling the Error Making predictions is easy! Knowing how good the predictions are: That is the real problem! A modeling/simulation methodology that doesn’t assess its own error is worthless! Modeling the error can only be done in a statistical sense … because otherwise, the error could be subtracted from the prediction leading to a prediction without the error.

Food Demand Model: 

Food Demand Model Naïve Model Enhanced Macroeconomic Model

Naïve Model: 

Naïve Model

Population Dynamics: 

Population Dynamics

Population Dynamics: 

Population Dynamics Predicting Growth Functions k(n+1) = FIR [ k(n), P(n), k(n-1), P(n-1), … ]

Population Dynamics: 

Population Dynamics

Macroeconomy: 

Macroeconomy

Macroeconomy: 

Macroeconomy

Food Demand/Supply: 

Food Demand/Supply

Enhanced Macroeconomic Model: 

Enhanced Macroeconomic Model

Population Layer: 

Population Layer

Population Layer: 

Population Layer

Economy Layer: 

Economy Layer

Food Demand/Supply Layer: 

Food Demand/Supply Layer

Results: 

Results Annual / Quarterly Data Layer One - Population Layer Layer two - Economy Layer Layer three - Food Demand Layer Layer Four - Food Supply Layer Optimization

Population Dynamics: 

Population Dynamics

Population Dynamics: 

Population Dynamics

Economy Layer: 

Economy Layer

Food Supply Layer: 

Food Supply Layer

Food Demand Layer: 

Food Demand Layer Population Dynamics Macroeconomy Food Demand Food Supply

Optimization: 

Optimization

Optimization: 

Optimization

Conclusion and Future Work: 

Conclusion and Future Work Mixed SD/FIR offers the best of both worlds. Application to any U.S. industry with change of demand and supply layers alone. Application to any new country or region with new data for layers 1 and 2. Fuzzy Inductive Reasoning features a model synthesis capability rather than a model learning approach. It is therefore quite fast in setting up the model.

Conclusion and Future Work: 

Conclusion and Future Work Fuzzy Inductive Reasoning is highly robust when used correctly. Fuzzy Inductive Reasoning offers a self-assessment feature, which is easily the most important characteristic of the methodology. Optimization with data collected at more frequent intervals.