Identification and Inference for Econometric Models: Unobserved Heterogeneity and Estimation of Average Partial Effects

By Jeffrey M. Wooldridge. 2005

136 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/839740a51284b20c0947b966a281e4f598ad286e

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Citations in the corpus

Incremental causal effects (2019, background): “In the econometrics literature, under weak ignorability, incremental treatment effects are often identified via E[∂tE[Y |X,T ]] for some covariates X , which is sometimes called average marginal effect or average partial effect (Powell et al., 1989; Cameron and Trivedi, 2005; Wooldridge, 2005).”
Grouped effects estimators in fixed effects models (2016, background): “Another common restriction is that unobservables depend upon observables only through a linear index, such as Mundlak (1978), Chamberlain (1980), and Wooldridge (2005).”
Correlated random effects models with unbalanced panels (2019, background): “CRE approaches for the case of lagged dependent variables, but with otherwise strictly exogenous covariates, are available for balanced panels; see Wooldridge (2005) for a summary.”
Panel data methods for fractional response variables with an application to test pass rates (2008, methodology): “Wooldridge (2005) showed how the Rivers and Vuong control function (CF) approach extends readily to the fractional response case for crosssectional data; in effect, we would set t = 1 and drop c i1 from (4.1).”
Complementarity and Aggregate Implications of Assortative Matching: A Nonparametric Analysis (2014, unknown): “This redundancy condition will be most plausible when Si is a good proxy for νi (cf. Wooldridge (2005)).”
Fixed effects estimation of structural parameters and marginal effects in panel probit models (2007, background): “In empirical analysis, the ultimate quantities of interest are often the marginal effects of specific changes in the regressors on the conditional response probability (see, e.g., Ruud, 2000; Angrist, 2001; Wooldridge, 2002; and Wooldridge, 2005).”
Identification and Estimation of Average Partial Effects in “Irregular” Correlated Random Coefficient Panel Data Models (2012, background): “V () = 2E  W∗0  X ∗Σ (X)X∗0W∗   = 00+  ()  (54)”
Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables (2014, methodology): “5), asymptotic analysis follows from standard results on M-estimation [for example, Wooldridge (2010, Chapter 12)]. In general, one needs to use the White (1982) sandwich variance estimator for misspecified maximum likelihood.”
Instrumental variables estimation of the average treatment effect in the correlated random coefficient model (2008, methodology): “Using a control function approach, Wooldridge (2005) shows how (2.11) can be”