By Richard Blundell, Whitney Newey, Torsten Persson
This is often the 3rd e-book of 3 volumes containing edited types of papers and a statement offered on the 9th global Congress of the Econometric Society, held in London in August 2005. The papers summarise and interpret key advancements, and so they speak about destiny instructions for a large choice of subject matters in economics and econometrics. The papers conceal either conception and functions. Written through top experts of their fields, those volumes supply a special survey of development within the self-discipline.
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Additional info for Advances in Economics and Econometrics: Theory and Applications, Ninth World Congress, Volume III (Econometric Society Monographs)
Ma and Koenker (2006) study a weighted average version of this estimator in a semiparametric model. 2 Discrete Y2 The joint independence model is useful when X is discrete because, unlike the marginal independence model, it can identify the ceteris paribus effect of continuous Y2 on h. This advantage apparently vanishes once Y2 is discrete. The difficulty is that when Y2 is discrete the conditional independence model does not point identify the structural function without further restriction. This is so however rich the support of discrete Y2 .
Such assumptions typically include exclusion and monotonicity restrictions and (conditional) independence assumptions. In this paper I will discuss part of this literature. I will focus on a two-equation triangular (recursive) system of simultaneous equations with a single endogenous regressor and a single instrument. Although much of the earlier literature on simultaneous equations focused on larger systems (in fact, much of the theoretical literature studied systems with an arbitrary number of endogenous regressors and an arbitrary number of instruments despite the rare occurrence of systems with more than two endogenous variables in empirical work and the practical difficulty of even finding a single credible instrument), many applications have this two-equation form and the framework is sufficiently rich for discussing the nature of the identification problems that are studied here.
The value of h at any set of values of its arguments is an explicit functional of quantile regressions associated with the joint distribution of Y1 and Y2 given X . There is no ill-posed inverse problem and estimation and inference are relatively simple. The latent variable U2 can appear as an argument of h. The joint independence condition implies the marginal independence conditions: U1 ⊥ X and U2 ⊥ X . It is a stronger restriction and so can have greater identifying power. In the continuous endogenous variable case it removes the requirement for a completeness condition.
Advances in Economics and Econometrics: Theory and Applications, Ninth World Congress, Volume III (Econometric Society Monographs) by Richard Blundell, Whitney Newey, Torsten Persson