Appendix 1. Supplementary Data

Testing Identifying Assumptions in Nonseparable Panel Data Models Supplementary Appendix

(i) βt(xt → xt|Xi = x) = E[Yit|Xi = x′]− E[Yit|Xi = x], (ii) FYiτ |Xi(.|x) = FYiτ |Xi(.|x) Proof (i) follows by the correlated-random-effects assumption and h(x) = h(x′) βt(xt → xt|Xi = x′) = ∫ (ξt(x ′ t, a, u)− ξt(x, a, u))FAi,Uit|Xi(a, u|x ′) = E[Yit|Xi = xt]− ∫ ξt(xt, a, u))dFAi,Uit|Xi(a, u|h(x)) = E[Yit|Xi = x]− E[Yit|Xi = x]. ∗University of California, Davis, One Shields Ave, Davis CA 9561...

Supplementary Appendix to LEARNING UNDER AMBIGUITY ∗

This appendix provides a detailed treatment of the portfolio choice problem studied in our paper “Learning under Ambiguity”, filling in details of calculations that were omitted in the text of the paper.

Supplement to “ Functional Differencing ” : Supplementary Appendix

Example 1B. Here we extend the analysis of Example 1B to censored regression models with random coefficients. Let us assume for simplicity that B (x, θ) has full-column rank q, for all θ, almost surely in x. Let V be a T × q matrix such that Q = V V ′ and V ′V = Iq. Let also U be a T × (T − q) matrix such that W = UU ′, and U ′U = IT−q. Lastly, let (μ, ν) = ( V ′Σ− 1 2 y, U ′Σ− 1 2 y ) . Then, ...

Supplementary Appendix to Dynamic Random Utility

Supplementary Appendix to “ Dynamic Belief Elicitation ”

This Supplementary Appendix includes additional results and the proofs omitted from the main text. Throughout, references are to the main text and to the appendix of the main text, unless they include the prefix “SA.” It proceeds as follows. In Section SA.1, we provide several examples that illustrate the general method of the main text to special environments. In Section SA.2, we consider the ...