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Copy file name to clipboardExpand all lines: lectures/multivariate_normal.md
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\end{aligned}
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$$
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If we shift the first equation forward one period and then substitute the expression for $\tilde \Sigma_t$ on the right side of the fifth equation
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into it we obtain
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$$
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\Sigma_{t+1}= C C' + A \Sigma_t A' - A \Sigma_t G' (G \Sigma_t G' +R)^{-1} G \Sigma_t A' .
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$$
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This is a matrix Riccati difference equation that is closely related to another matrix Riccati difference equation that appears in a quantecon lecture on the basics of linear quadratic control theory.
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That equation has the form
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```{math}
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P_{t-1} =R + A' P_t A - A' P_t B
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(B' P_t B + Q)^{-1} B' P_t A .
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```
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Stare at the two preceding equations for a moment or two, the first being a matrix difference equation for a conditional covariance matrix, the
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second being a matrix difference equation in the matrix appearing in a quadratic form for an intertemporal cost of value function.
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Although the two equations are not identical, they display striking family resemblences.
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* the first equation tells dynamics that work **forward** in time
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* the second equation tells dynamics that work **backward** in time
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* while many of the terms are similar, one equation seems to apply matrix transformations to some matrices that play similar roles in the other equation
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The family resemblences of these two equations reflects a transcendent **duality** between control theory and filtering theory.
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### An example
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We can use the Python class *MultivariateNormal* to construct examples.
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Here is an example for a single period problem at time $0$
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