Correcting errors regarding envelope conditions

temporaryWork
youainti 5 years ago
parent df8499c1e6
commit 521671ea0c

@ -57,7 +57,7 @@ I hope to write a section clearly explaining assumptions, caveats, and shortcomi
Needs completed. Needs completed.
\subsection{Derivations} \subsection{Derivations}
\subsubsection{Marginal Survival Rates}\label{APX:Derivations:SurvivalRates} \subsubsection{Marginal Survival Rates}\label{APX:Derivations:SurvivalRates}
\subfile{sections/appedicies/apx_01_MarginalSurvivalRates} %\subfile{sections/appedicies/apx_01_MarginalSurvivalRates}
\end{document} \end{document}

@ -65,8 +65,6 @@ envelope conditions can also be found:
\begin{align} \begin{align}
\nabla_{\vec s_t, \vec x^{\sim i}_t, D_t} V^i(\vec s_t, \vec x^{\sim i}_t, D_t) \nabla_{\vec s_t, \vec x^{\sim i}_t, D_t} V^i(\vec s_t, \vec x^{\sim i}_t, D_t)
=& \nabla_{\vec s_t, \vec x^{\sim i}_t, D_t} u^i(\vec s_t, D_t) \notag \\ =& \nabla_{\vec s_t, \vec x^{\sim i}_t, D_t} u^i(\vec s_t, D_t) \notag \\
&- \der{}{x}{}F(x^i(\vec s_t, \vec x^{\sim i}_t, D_t)) \cdot
\nabla_{\vec s_t, \vec x^{\sim i}_t, D_t} x^i(\vec s_t, \vec x^{\sim i}_t, D_t) \notag\\
&+ \beta \left[ &+ \beta \left[
\nabla_{\vec s_{t+1}, \vec x^{\sim i}_{t+1}, D_{t+1} } \nabla_{\vec s_{t+1}, \vec x^{\sim i}_{t+1}, D_{t+1} }
V^i(\vec s_{t+1}, \vec x^{\sim i}_{t+1}, D_{t+1}) V^i(\vec s_{t+1}, \vec x^{\sim i}_{t+1}, D_{t+1})
@ -75,25 +73,26 @@ envelope conditions can also be found:
[ \vec s_{t+1}, \vec x^{\sim i}_{t+1}, D_{t+1} ] [ \vec s_{t+1}, \vec x^{\sim i}_{t+1}, D_{t+1} ]
\right] \label{EQ:EnvelopeConditions} \right] \label{EQ:EnvelopeConditions}
\\ \\
\nabla \vec V^i_t =& \vec u^i - \vec f + \beta A \cdot \nabla \vec V^i_{t+1} \nabla \vec V^i_t =& \vec u^i
+ \beta A \cdot \nabla \vec V^i_{t+1}
\label{EQ:SimplifiedEnvelopeConditions} \label{EQ:SimplifiedEnvelopeConditions}
\end{align} \end{align}
When interpreting this, note that When interpreting this, note that
$$ $$
\nabla \vec V^i_{t+1} = \nabla_{\vec s_{t+1}, \vec x^{\sim i}_{t+1}, D_{t+1} } \nabla \vec V^i_{t+1} = \nabla_{[\vec s_{t+1}~ \vec x^{\sim i}_{t+1}~ D_{t+1}] }
V^i(\vec s_{t+1}, \vec x^{\sim i}_{t+1}, D_{t+1}) V^i(\vec s_{t+1}, \vec x^{\sim i}_{t+1}, D_{t+1})
$$ $$
is a $2N \times 1$ vector of first derivatives but is a $2N \times 1$ vector of first derivatives but
$$ $$
A = \nabla_{\vec s_t, \vec x^{\sim i}_t, D_t} A = \nabla_{\vec s_t, \vec x^{\sim i}_t, D_t}
[ \vec s_{t+1}, \vec x^{\sim i}_{t+1}, D_{t+1} ] [ \vec s_{t+1}~ \vec x^{\sim i}_{t+1}~ D_{t+1} ]
$$ $$
is a $2N \times 2N$ matrix of first derivatives. 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 By solving for $\vec V^i_{t+1}$ as a function of $\vec V^i_{t}$ we get the
intertemporal condition: intertemporal condition:
\begin{align} \begin{align}
\frac{1}{\beta} A^{-1} \left(\nabla \vec V^i_t - \vec u^i +\vec f \right) \frac{1}{\beta} A^{-1} \left(\nabla \vec V^i_t - \vec u^i \right)
= \nabla \vec V^i_{t+1} = \nabla \vec V^i_{t+1}
\end{align} \end{align}
Thus one crucial condition for the existence of a solution is that $A^{-1}$ exists for Thus one crucial condition for the existence of a solution is that $A^{-1}$ exists for
@ -116,6 +115,5 @@ By substituting this defined value of $\nabla V^i_t$ into
\cref{EQ:SimplifiedOptimalityCondition} \cref{EQ:SimplifiedOptimalityCondition}
one final time, we get a function that fully determines the policy function. one final time, we get a function that fully determines the policy function.
\cref{EQ:SimplifiedOptimalityConditions,EQ:SimplifiedEnvelopeConditions}
\end{document} \end{document}

@ -2,7 +2,40 @@
\graphicspath{{\subfix{Assets/img/}}} \graphicspath{{\subfix{Assets/img/}}}
\begin{document} \begin{document}
Introduction goes here The Social (Fleet) Planner's problem can be written in the belman form as:
\subsection{testing} \begin{align}
This is a subsection of the introduction. W(\vec s_t, D_t) =& \max_{\vec x_t} \left[
\left(\sum^N_{i=1} u^i(\vec s_t, D_t) - F(x^i_t) \right)
+ \beta \left[ W(\vec s_{t+1}, D_{t+1}) \right]\right] \notag \\
&\text{subject to:} \notag \\
& s^i_{t+1} = (1-l^i(\vec s_t, D_t))s^i_t +x^i_t ~~~ \forall i \notag \\
& D_{t+1} = (1-\delta)D_t + g(D_t)
+ \gamma \sum^N_{i=1} l^i(\vec s_t, D_t)
+ \Gamma \sum^N_{i=1} x^i_t
\end{align}
The resulting $N$ optimality conditions are:
\begin{align}
0 =& -\der{F(x^i_t)}{s^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}]
\right]
~~\forall~~i \\
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) =&
\sum^N_{i=1} \nabla_{\vec s_{t}, D_{t}} u^i(\vec s_t, D_t)
%- \der{}{x^i_t}{}F(x^i_t) \nabla_{\vec s_{t}, D_{t}}x^i_t %This equals zero due to the envelope theorem
\notag \\
&+ \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}]
\right] \\
\nabla W_t =& \vec U + \beta \left[C \cdot \nabla W_{t+1} \right]
\end{align}
\end{document} \end{document}

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