model

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Model

Any mathematical formula or other structure that economists use to explain or predict occurrences. Economists test their models with real world facts before they gain wide acceptance, but, even then, there is no guarantee that a model will always be a correct predictor. See also: Model risk.

model

An abstraction of reality, generally referring in investments to a mathematical formula designed to determine security values. Economists also use models to project trends in economic variables such as interest rates, economic activity, and inflation rates.

model

see ECONOMIC MODEL.
References in periodicals archive ?
Yet causal models have to be tested for the analyses to provide actionable insights to improve performance.
In short, replacing the inaccurate assumptions and causal models underlying the war on drugs with better alternatives points to a different way of understanding drug use and abuse and to different drug policy options.
This section contrasts a causal model in the adoption of downsizing practices carried out by Spanish companies from 1994 to 2000.
15) Though the direct effect of the increasing percentage side-loaders is to increase the net cost per tonne, in the causal model there are paths flowing from the percentage side-loader variable that decrease the net cost per tonne.
Arguably, Stone's portrayal of the function of causal models in policy politics is reflected in policy documents about child unintentional-injury prevention.
The same causal model applied to the whole sample and the split samples of students born in Hong Kong and on the mainland.
TABLE 12 Total Standardised Effects Ranked From Largest to Smallest for the Recycling Causal Model Which Predicts Net Cost Per Tonne variable Variable Total Direct Plus Indirect Effects Residents are required to set out full bins only -.
We have motivated this discussion of desistance with the Rubin/Holland causal model to highlight the extraordinary barriers to causal inference in etiological investigations.
Both time series and causal models use analytical procedures to determine underlying patterns, and thus, their validity rests on the assumption that the future will continue to behave like the past.
Based on the atmospheric chemistry of ozone, Figure 1B is the more appropriate causal model.
Arguing that Bayesian models can have direct neural implementations, David Huber, in 'Causality and Time: Explaining Away the Future and the Past', relies on rational analysis and graph theory to construct a causal model which explains priming and provides insight into perception and inference over time.