Difference between revisions of "Statistical Models"

Revision as of 13:59, 8 April 2021

Definition

Statistical Models denotes a very broad category of models that are primarily based on empirical (historical) data. The class encompasses econometric models, regression models, Machine Learning, predictive models , typical VaR models etc.

The behaviour of the data is systematized in a mathematical model, usually by making some essential assumptions about what is the underlying distribution (parametric models) but with minimal assumptions if sufficient volumes of data are available (non-parametric models)

Examples on non-statistical models are the variety of Structural Models and the No-arbitrage pricing Models where various logical constructs (e.g. causal relations, constraints) are included in the model as opposed to be inferred from data.

Issues and Challenges

Availability and quality of historical data
Correlation versus Causation
Forward looking applicability

References

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 == Definition ==
-'''Statistical Models''' denotes a very broad category of models that are primarily based on empirical (historical) data.  The class encompasses econometric models, regression models, machine learning, typical VaR models etc.
+'''Statistical Models''' denotes a very broad category of models that are primarily based on empirical (historical) data.  The class encompasses econometric models, regression models, [[Machine Learning]], [[Predictive Model | predictive models ]], typical VaR models etc.
 The behaviour of the data is systematized in a mathematical model, usually by making some essential assumptions about what is the underlying distribution (parametric models) but with minimal assumptions if sufficient volumes of data are available (non-parametric models)