DP_SL - Dynamic Programming Prof. Lutz Hendricks September...

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Dynamic Programming Prof. Lutz Hendricks September 15, 2009 L. Hendricks () Dynamic Programming September 15, 2009 1 / 42
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Introduction to Dynamic Programming Useful theorems to characterize the solution to a DP problem. There is no reason to remember these results. But you need to know they exist and can be looked up when you need them. L. Hendricks () Dynamic Programming September 15, 2009 2 / 42
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Generic problem Problem P1: (The sequence problem) V ( x ( 0 )) = max f x ( t + 1 ) g t = 0 t = 0 β t U ( x ( t ) , x ( t + 1 )) subject to x ( t + 1 ) 2 G ( x ( t )) x ( 0 ) given x ( t ) 2 X ± R k is the set of allowed states. The correspondence G : X X L. Hendricks () Dynamic Programming September 15, 2009 3 / 42
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Generic problems Assumptions that could be relaxed at a cost 1 Stationarity: U and G do not depend on t . 2 Utility is additively separable. Time consistency L. Hendricks () Dynamic Programming September 15, 2009 4 / 42
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Mapping into the growth model max f k ( t + 1 ) , c ( t ) g t = 0 t = 0 β t u ( c ( t )) subject to k ( t + 1 ) = f ( k ( t )) c ( t ) ± 0 k ( 0 ) given L. Hendricks () Dynamic Programming September 15, 2009 5 / 42
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Mapping into the growth model U ( k ( t ) , k ( t + 1 )) = u ( k ( t + 1 ) f ( k ( t ))) G ( k ( t )) = f k ( t + 1 ) : k ( t + 1 ) 2 [ 0, f ( k ( t ))] g L. Hendricks () Dynamic Programming September 15, 2009 6 / 42
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Recursive problem Problem P2: V ( x ) = max y 2 G ( x ) U ( x , y ) + β V ( y ) , 8 x 2 X This is a Bellman equation. The question: When is solving P1 equivalent to solving P2? L. Hendricks () Dynamic Programming September 15, 2009 7 / 42
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Solution A solution is a policy function π : X X and a value function V ( x ) such that V ( x ) = U ( x , π ( x )) + β V ( π ( x )) , 8 x 2 X When y = π ( x ) , now and forever, the max value is attained. L. Hendricks () Dynamic Programming September 15, 2009 8 / 42
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Dynamic Programming Theorems The payo/ of DP: it is easier to prove that solutions exist, are unique, monotone, etc. We state some assumptions and theorems using them. L. Hendricks () Dynamic Programming September 15, 2009 9 ± 42
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Assumption 1 x ( 0 ) by Φ ( x ( 0 )) . G ( x ) is nonempty for all x 2 X . needed to prevent a currently good looking path from running into "dead ends" lim n ! n t = 0 β t U ( x ( t ) , x ( t + 1 )) x ( 0 ) 2 X and feasible paths x 2 Φ ( x ( 0 )) . cannot have unbounded utility L. Hendricks () Dynamic Programming September 15, 2009 10 / 42
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The set X in which x lives is compact. G is compact valued and continuous. U is continuous. Notes : Compactness avoids existence issues: without it, there could always be a slightly better x Compact X creates trouble with endogenous growth, but can be relaxed. L. Hendricks ()
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DP_SL - Dynamic Programming Prof. Lutz Hendricks September...

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