Summary
Getting started:
Define the modeling objectives
Developing the assumptions
Sufficient relevant data
Sample size
Evaluate data integrity and reliability
Building the model
Designing the model to address the objectives or stated problem
Continuity with historic data
Integrated progressive submodels
Movable and scalable models
Evaluating precision
Precision vs. reasonable certainty
Establishing a baseline
Minimizing forecasting errors
Identifying issues
Data-based errors
Calculation Errors
Outlier values
Anomalies and random events
Common sense and professional judgment ...
Prospective Financial Modeling with Excel
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