Micro-Econometrics for Policy, Program, and Treatment Effects

By Myoung‐jae Lee. 2005

383 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

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Citations in the corpus

The Econometrics of Randomized Experiments (2016, background): “Such quantile treatment effects have been studied in Doksum (1974) and Lehman (1974), and more recently in Abadie, Angrist and Imbens (2002), Chernozhukov and Hansen (2005), Firpo (2007), Bitler, Gelbach and Hoynes (2002).”
Is Export Promotion Effective in Developing Countries? Firm-Level Evidence on the Intensive and Extensive Margins of Exports (2008, methodology): “…sample counterpart for the missing information on the outcomes had the firms not been treated when no randomized control groups are available (see, e.g., Heckman et al., 1998; Heckman et al., 1999; Klette et al., 2000; Jaffe, 2002; Blundell and Costa Dias, 2002; Lee, 2005; Smith and Todd, 2005a).”
Matching Methods in Practice: Three Examples (2015, background): “…reviews and general discussions in the econometrics literature see Angrist and [5] Krueger (2000), Angrist and Pischke (2009), Caliendo (2006), Heckman and Vytlacil (2007), Imbens (2004), Imbens and Wooldridge (2009), Caliendo and Kopeinig (2011), Frölich (2004), Wooldridge (2002), and Lee (2005).”
Dealing with limited overlap in estimation of average treatment effects (2009, neutral): “For example, Rubin (1977) and Lee (2005b), in situations where there is a single discrete covariate, suggest simply discarding all units with covariate values with either no treated or no control units.”
The Sloping Land Conversion Program in China: Effect on the Livelihood Diversification of Rural Households (2015, methodology): “One of the most popular treatment effect analysis methods is difference in differences (DID) (Lee, 2005), which involves comparing the before–after change of the treated group with the before–after change of the control group.”
Moving the Goalposts: Addressing Limited Overlap in Estimation of Average Treatment Effects by Changing the Estimand (2006, positive): “There is a large literature on estimating average treatment effects (ATE) under assumptions of unconfoundedness, ignorability, or exogeneity following the seminal work by Rubin (1974, 1978) and Rosenbaum and Rubin (1983a). Researchers have developed estimators based on regression methods (e.”
SHARP BOUNDS ON THE DISTRIBUTION OF TREATMENT EFFECTS AND THEIR STATISTICAL INFERENCE (2009, methodology): “Because of this missing data problem, most work in the treatment effect literature has focused on the evaluation of various average treatment effects such as the mean of the treatment effects; see the recent book by Lee (2005) for discussion and references.”
PARTIAL IDENTIFICATION OF THE DISTRIBUTION OF TREATMENT EFFECTS AND ITS CONFIDENCE SETS (2009, background): “The sharp bounds on the treatment effect distribution implies bounds on the class of ‘‘D-parameters’’ introduced in Manski (1997a), see also Manski (2003).”
Quasi-Experiments and Hedonic Property Value Methods (2009, positive): “…to the potential outcomes model, we may now proceed to discuss specific ways in which researchers estimate treatment effects and tackle the selection problem in hedonic quasiexperiments. a more detailed and excellent review of treatment effects in micro econometric settings is lee (2005).”
The WTO trade effect (2011, methodology): “We generalize the test to explicitly allow for heterogeneous treatment effects in constructing the confidence intervals.”
Taking a new look at empirical models of adoption: average treatment effect estimation of adoption rates and their determinants (2007, background): “…their official release in Cote 5 The asymptotic distribution of the weighting estimator with a first stage probit estimation can be found in its general form in Lee (2005) d’Ivoire only in late 2001, the diffusion of the NERICA varieties was mostly through WARDA PVS activities (see Dalton, 2004).”