AD model
Min Kim (min.kim@rutgers.edu)
which can be rewritten as
where
Recall that we have one forward-looking variable, that is,
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begin
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ρ = 0.5
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θ = 1.0
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b = 0.5
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ϕ = θ/(θ+b)
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end;
x
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F = [ρ 0;-1 1/ϕ];
BK is satisfied?
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md""" BK is satisfied?"""
Yes!
x
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begin
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BK = 1 == sum(eigvals(F) .> 1)
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if BK
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md""" Yes!"""
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else
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md""" No!"""
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end
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end
Let's check the decomposition as well
x
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md"""
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Let's check the decomposition as well
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"""
2×2 Matrix{Float64}:
0.5 0.0
0.0 1.5
x
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Λ = diagm(eigvals(F))
2×2 Matrix{Float64}:
0.707107 0.0
0.707107 1.0
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D = eigvecs(F)
true
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F ≈ (D)*Λ*inv(D)