Inverse probability weighted M-estimators for sample selection, attrition, and stratification

By Jeffrey M. Wooldridge. 2002

613 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5489da037cd2f846c7ee525d2433fd819157eb47

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Citations in the corpus

Causal Inference in Urban and Regional Economics (2014, neutral): “For example, Wooldridge (2005) suggests the “sample-weighted” treatment effect, which is identical to the ATE if each agent is sampled the same number of times, as XI i¼1 Ni N β̂ i .”
Insurance within the Firm (2005, positive): “Our procedure delivers consistent esti- 1p̂it mates under the conditions provided in theorem 4.1 of Wooldridge (2002).”
Inverse probability weighted estimation for general missing data problems (2004, methodology): “The following result assumes that the objective function qw, is twice continuously differentiable on the interior of Θ, as in Wooldridge (2002a).”
Measurement Error Models with Auxiliary Data (2005, methodology): “A closely related assumption is used in Wooldridge (1995) and Wooldridge (2002) .”
Decomposing Scale and Technique Effects of Financial Development and Foreign Direct Investment on Renewable Energy Consumption (2022, methodology): “For instance, Wu and Broadstock (2015) applied the panel GMM technique to examine the impact of financial development, institutional quality on renewable energy consumption for a panel of 22 emerging market economies for the period of 1990-2010.”
Individual Investor Perceptions and Behavior During the Financial Crisis (2013, positive): “*** Statistical significance at the 1% level. tion in significant differences, as a robustness check we apply an inverse-probability-weighted estimator (Robins and Rotnitzky, 1995; Wooldridge, 2002).”
Semiparametric Efficiency in GMM Models of Nonclassical Measurement Errors, Missing Data and Treatment Effects (2008, neutral): “In panel data attrition analysis, Wooldridge (2002) and Wooldridge (2003) consider estimation of parameters identified by a condition such as (2) in presence of missing data.”
The Impact of Academic Patenting on the Rate, Quality and Direction of (Public) Research Output (2009, methodology): “In contrast to two-step selection correction methods, Wooldridge [2002] has shown that the standard errors obtained in this case are conservative.”
What is China's true unemployment rate? (2005, background): “…weighted (IPW) M-estimator which produces consistent estimates given an ignorability assumption that selection can be predicted by observables (Wooldridge, 2002a, 2002b).8 We allow the attrition bias to be different in different cities, since many factors (e.g., population mobility, survey…”
Health‐related non‐response in the British Household Panel Survey and European Community Household Panel: using inverse‐probability‐weighted estimators in non‐linear models (2006, methodology): “Wooldridge (2002a) also showed that using the estimated p̂it rather than the true pit and ignoring the implied adjustment to the estimated standard errors leads to ‘conservative inference’ so that the standard errors are larger than they would be with an adjustment for the use of fitted rather than…”
Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas (2009, methodology): “In the estimation of models with missing data, several authors have used the identifying assumption that the probability of having a complete observation conditional on a set of auxiliary variables is constant (see e.g. Rubin (1976), Little and Rubin (2002), Wooldridge (2002b)).”
Corporate Purpose and Financial Performance (2019, positive): “First, we include the lagged value of the dependent variable, which controls for past factors that have influenced the performance of the firm and tend to have a persistent impact on a firm’s performance (Wooldridge 2002).”