The Econometric SocietyEdited by: Guido W. Imbens • Print ISSN: 0012-9682 • Online ISSN: 1468-0262
This zip file contains the replication files for the manuscript.
This appendix contains the proofs for Lemmas and Theorems that were omitted in the main paper. Equation numbers refer to formulae and expressions in the main text.
This appendix contains farsighted stable sets: existence and some connections.
This appendix provides the proofs of all the lemmas, theorems and propositions stated in the main text. Additional results on consistent sieve variance estimators and bootstrap sieve t statistics are also presented.
This zip file contains the replication files for the manuscript.
This supplementary appendix presents an additional Monte Carlo exercise and additional diagnostics for the empirical application in the paper as well as estimation results for alternative stochastic volatility model specifications. It also provides further details regarding the computations in the empirical part and the Monte Carlo study.
This zip file contains the replication files for the manuscript.
This supplementary appendix is divided into seven sections. In Section S1 we describe the computational algorithms. In Section S2 we deal with inference, both from a large-N, T perspective and from a large-N, fixed-T perspective. In Section S3 we treat the issues of misspecification of the number of groups G and its choice. In Section S4 we study two extensions of the baseline model, which allow for unit-specific heterogeneity and for group-specific coefficients, respectively. In Section S5 we deal with several other issues, including the connection with mixture models, and how to incorporate prior information in estimation. In Section S6 we report the results of a simulation study. Lastly, in Section S7 we show a number of additional results related to the empirical application.
This supplemental appendix contains (i) sufficient conditions for point identification when X is a vector and Z is continuously distributed; (ii) sufficient conditions for point identification when X is a vector and Z is binary; and (iii) examples that illustrate the restrictiveness of the identifying assumptions.
This supplement discusses the link with group theory and the freeness and nonfreeness properties. It then discusses the extension to a multivariate X. The third section gathers all proofs.
