Title: Mixed Structural and Behavioral Models for Predicting the Future Behavior of some Aspects of the Mac
1Mixed Structural and Behavioral Models for
Predicting the Future Behavior of some Aspects of
the Macroeconomy
- Mukund Moorthy
- 2nd February 1999
2Contents
- Economic Modeling
- System Dynamics
- Fuzzy Inductive Reasoning
- Proposed Macroeconomic Model
- Food Demand Modeling
- Conclusion
3Economic Modeling
- Economic Forecasting Techniques
- Time Series Data
- Neural Networks
4Time Series Data
- Time Series Components
- Trend ( T )
- Cyclical ( C )
- Seasonal ( S )
- Irregular ( I )
5Curve Fitting
6Curve Fitting
- Exponential Trend Equation
- Polynomial Trend Equation
7Smoothing Techniques
- Moving Average
- each point is average of N points
- Exponential Smoothing
8Time Series Forecasting
9Economic Forecasting
- Step-wise Auto-regressive method
- Neural Networks
10System Dynamics
- Modeling Dynamic Systems
- Information feedback loops
11System Dynamics
- Levels
- Flow Rates
- Decision Functions
12System Dynamics
13Structure Diagram
14Forresters World Model
- Population
- Capital Investment
- Unrecoverable Natural Resources
- Fraction of Capital Invested in the Agricultural
Sector - Pollution
15Structure Diagram of Forresters World Model
16Shortcomings of the World Model
- Levels and Rates
- Laundry List
17Fuzzy 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)
18Fuzzy Inductive Reasoning
Mixed Quantitative/Qualitative Modeling
19Fuzzification
20Inductive Modeling
21Inductive Simulation
22Modeling the Error
- Making predictions is easy!
- Knowing how good the predictions are That is the
real problem! - A modeling/simulation methodology that doesnt
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.
23Food Demand Model
- Naïve Model
- Enhanced Macroeconomic Model
24Naïve Model
25Population Dynamics
26Population Dynamics
- Predicting Growth Functions
k(n1) FIR k(n), P(n), k(n-1), P(n-1),
27Population Dynamics
28Macroeconomy
29Macroeconomy
30Food Demand/Supply
31Enhanced Macroeconomic Model
32Population Layer
33Population Layer
34Economy Layer
35Food Demand/Supply Layer
36Results
- Annual / Quarterly Data
- Layer One - Population Layer
- Layer two - Economy Layer
- Layer three - Food Demand Layer
- Layer Four - Food Supply Layer
- Optimization
37Population Dynamics
38Population Dynamics
39Economy Layer
40Food Supply Layer
41Food Demand Layer
Food Supply
Food Demand
Macroeconomy
Population Dynamics
42Optimization
43Optimization
44Conclusion 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.
45Conclusion 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.