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Stochastic: The models take into consideration the transmission of random shocks into the economy and the consequent economic fluctuations. General : referring to the entire economy as a whole (within the model) in that price levels and output levels are determined jointly.
An econometric model specifies the statistical relationship that is believed to hold between the various economic quantities pertaining to a particular economic phenomenon. An econometric model can be derived from a deterministic economic model by allowing for uncertainty, or from an economic model which itself is stochastic. However, it is ...
This page is concerned with the stochastic modelling as applied to the insurance industry. For other stochastic modelling applications, please see Monte Carlo method and Stochastic asset models. For mathematical definition, please see Stochastic process. "Stochastic" means being or having a random variable.
The definition of a stochastic process varies, [67] but a stochastic process is traditionally defined as a collection of random variables indexed by some set. [68] [69] The terms random process and stochastic process are considered synonyms and are used interchangeably, without the index set being precisely specified.
Block and Marschak [9] proved that, when there are at most 3 alternatives, the random utility model is unique ("identified"); however, when there are 4 or more alternatives, the model may be non-unique. [11] For example, [12] we can compute the probability that the agent prefers w to x (w>x), and the probability that y>z, but may not be able to ...
Two simulated time series processes, one stationary and the other non-stationary, are shown above. The augmented Dickey–Fuller (ADF) test statistic is reported for each process; non-stationarity cannot be rejected for the second process at a 5% significance level. White noise is the simplest example of a stationary process.
In contrast, some authors have argued that randomization can only improve a deterministic algorithm if the deterministic algorithm was poorly designed in the first place. [21] Fred W. Glover [22] argues that reliance on random elements may prevent the development of more intelligent and better deterministic components. The way in which results ...
Thus, in a statistical model specified via mathematical equations, some of the variables do not have specific values, but instead have probability distributions; i.e. some of the variables are stochastic. In the above example with children's heights, ε is a stochastic variable; without that stochastic variable, the model would be deterministic.