10.07.2015 Views

Intertemporal Substitution and Recursive Smooth Ambiguity ...

Intertemporal Substitution and Recursive Smooth Ambiguity ...

Intertemporal Substitution and Recursive Smooth Ambiguity ...

SHOW MORE
SHOW LESS

Create successful ePaper yourself

Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.

This relation holds true because i is more risk averse than j in the first stage.Turn to the proof of the converse statement. Fix a set E ⊂ S such that λ = ∫ π(E)dµ P s t(π) ∈s t(0, 1). Suppose (c, δ[l]) ≽ j s(c, λδ[l ′ ] + (1 − λ)δ[l ′′ ]) for l, l ′ , l ′′ ∈ L. Let h t +1 be the one-stepaheadact that gives l ′ if event E happens <strong>and</strong> gives l ′′ , otherwise. Then by definitionwe can show that b(h +1 , µ s t) = λδ[l ′ ] + (1 − λ)δ[l ′′ ]. Using the representation in Theorem3, we can verify that (c, b(h +1 , µ s t)) ∼ j s(c, δ[h t +1 ]) or (c, λδ[l ′ ] + (1 − λ)δ[l ′′ ]) ∼ j s t(c, δ[h +1 ]), which implies (c, δ[l]) ≽ j s(c, δ[h t +1 ]). By comparative ambiguity aversion, wehave (c, δ[l]) ≽ i s(c, δ[h t +1 ]). Since (c, λδ[l ′ ] + (1 − λ)δ[l ′′ ]) ∼ i s(c, δ[h t +1 ]) holds as well, weobtain (c, δ[l]) ≽ i s(c, λδ[l ′ ] + (1 − λ)δ[l ′′ ]). Hence we havet(c, δ[l]) ≽ j s(c, λδ[l ′ ] + (1 − λ)δ[l ′′ ]) =⇒ (c, δ[l]) ≽ i t s (c, t λδ[l′ ] + (1 − λ)δ[l ′′ ]).We can extend this result to all λ ∈ (0, 1) by continuity (Axiom B1) <strong>and</strong> Axiom B4 (FirstStage Independence). We can also extend this result to all finite lotteries over L by repeatedlyapplying the above argument. We finally extend it to all lotteries over L by continuity ofpreferences (Axiom B1).E Appendix: Proofs for Section 5Proof of Proposition 7:Defineφ t (α) = V t (c + αδ) ,for an adapted process (δ t ) . Using equations (24)-(26), we have:φ t (α) = W (c t + αδ t , R t (V t+1 (c + αδ))) .Taking derivatives in the preceding equation yieldsφ ′ t (0) = W 1 (c t , R t (V t+1 )) δ t{+ W 2 (c t , R t (V t+1 )) v ′ ◦ u ( −1 E πz,t [u (V t+1 )] )Ev ′ µt(R t (V t+1 )) u ( ′ u ( −1 E πz,t [u ′ (V t+1 )] ))E [π z,t u ′ (V t+1 ) φ ′ t+1 (0) ]}Define λ t as in (22) <strong>and</strong> E z tas in (23). We obtain:[ ]Eφ ′ zt (0) = λ t δ t + E t+1t φ ′Etz t+1 (0) ,where E t is the conditional expectation operator with respect to the predictive distribution∑z µ t (z) π z (·|s t ) . From this equation <strong>and</strong> the definition in (21), we can derive that ξ z t =E z t λ t .51

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!