Stuff I had worked on previously but not touched recently. Cleanup before resuming work.

temporaryWork
youainti 5 years ago
parent ff97df59e6
commit 2f8613f5bf

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@ -56,7 +56,7 @@ First, find the single optimality condition
\cdot
\nabla_{x^i_t} [ \vec s_{t+1}, \vec x^{\sim i}_{t+1}, D_{t+1} ]
\right] \label{EQ:OptimalityCondition}\\
0 =& -\der{F}{x^i_t}{} + \beta \nabla V^i_t \cdot \vec a_t
0 =& -\der{F}{x^i_t}{} + \beta \nabla V^i_{t+1} \cdot \vec a_t
\label{EQ:SimplifiedOptimalityCondition}
\end{align}
@ -92,7 +92,7 @@ is a $2N \times 2N$ matrix of first derivatives.
By solving for $\vec V^i_{t+1}$ as a function of $\vec V^i_{t}$ we get the
intertemporal condition:
\begin{align}
\frac{1}{\beta} A^{-1} \left(\nabla \vec V^i_t - \vec u^i \right)
\frac{1}{\beta} A^{-1} \left(\nabla \vec V^i_t - \vec u^i_t \right)
= \nabla \vec V^i_{t+1}
\end{align}
Thus one crucial condition for the existence of a solution is that $A^{-1}$ exists for

@ -14,18 +14,21 @@ The Social (Fleet) Planner's problem can be written in the belman form as:
+ \Gamma \sum^N_{i=1} x^i_t
\end{align}
The resulting $N$ optimality conditions are:
\subsubsection{Euler Equation}
First find the $N$ optimality conditions:
\begin{align}
0 =& -\der{F(x^i_t)}{s^i_t}{}
0 =& -\der{F(x^i_t)}{x^i_t}{}
+ \beta \left[
\nabla_{\vec s_{t+1}, D_{t+1}} W(\vec s_{t+1}, D_{t+1})
\cdot
\nabla_{\vec s_{t}, D_{t}}[\vec s_{t+1} ~ D_{t+1}]
\parder{}{x^I_t}{}[\vec s_{t+1} ~ D_{t+1}]
\right]
~~\forall~~i \\
~~\forall~~i
\end{align}
Which in vector form is:
\begin{align}
0 =& -\vec f +\beta \left[B\cdot \nabla W_{t+1} \right]
\end{align}
And the $N+1$ envelope conditions are:
\begin{align}
\nabla_{\vec s_{t}, D_{t}} W(\vec s_t, D_t) =&
@ -37,5 +40,10 @@ And the $N+1$ envelope conditions are:
\right] \\
\nabla W_t =& \vec U + \beta \left[C \cdot \nabla W_{t+1} \right]
\end{align}
Which gives us the iteration format
\begin{align}
\nabla W_{t+1} =& (\beta C)^{-1} \cdot \left(\nabla W_t - \vec U \right)
\end{align}
\end{document}

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