Chapter 73 Nonparametric identification

By Rosa L. Matzkin. 2007

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In Statistics and Probability

Citations in the corpus

Semiparametric Inference in Dynamic Binary Choice Models, Second Version (2014, methodology): “Identification under known distributions of unobserved states Following existing literature on semi-and non-parametric identification (Roehrig (1988) and Matzkin (2007) among others), we refer to parameters (u, G, β, F ) as a (model) structure.”
Random Coefficients on Endogenous Variables in Simultaneous Equations Models (2014, background): “See Matzkin (2007) for the formal definition of identification.”
A Test of the Conditional Independence Assumption in Sample Selection Models: A TEST OF THE CONDITIONAL INDEPENDENCE ASSUMPTION (2012, unknown): “Advances have been reported in selection on observables, instrumental variables, and panel data models, among many others (for a discussion, see e.g. Matzkin 2007).”
The Statistics of Causal Inference: A View from Political Methodology (2015, unknown): “Informally, we say a parameter in a model is identiﬁed if it is theoretically possible to learn the true value of that parameter with an inﬁnite number of observations (Matzkin 2007, sec.”
How many types are there (2013, background): “These models are di¢ cult to implement, and, as yet, only Matzkin (2003, 2007) has implemented them.”
Geographic Boundaries as Regression Discontinuities (2015, background): “Informally, a parameter is said to be identifiable if changing the value of the true parameter that generated the data implies a different distribution of the observed data (see Matzkin 2007, § 3.1).”