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Function Description

Returns an array providing simulated output from a multivariate time series model of the world involving one or more states or regimes, each of which is characterised by a Gaussian (i.e. multivariate normal) distribution, with a Markov chain process indicating how likely it is to move between each state over a given time period. The output is 2 dimensional, with the first dimension characterising the simulation and the time period and the second dimension providing a vector of the variables themselves.


Models where each state itself consists of a predefined (distributional) mixture of multivariate normal distributions can be accommodated in such a model by defining the Markov chain appropriately.


The function includes parameters that:


(a)    define the starting state or how it may itself be simulated

(b)   include a random number seed so that the results can be reproduced subsequently

(c)    include sampling algorithms that help to reduce run times by sampling in a uniform manner across the quantile range that the individual random variables can take


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Links to:

-          Interactively run function

-          Interactive instructions

-          Example calculation

-          Output type / Parameter details

-          Illustrative spreadsheet

-          Other Markov processes functions

-          Computation units used

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