The Effects of Birth Inputs on Birthweight

By Jason Abrevaya, Christian M. Dahl. 2008

277 citations recorded by the source corpus.

Subfields: General Health Professions, Health, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/0ff5615ab3376f940168da2d750e69df3a79d09e

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In General Health Professions

In Health

In Management Science and Operations Research

Citations in the corpus

Nonlinear Panel Data Estimation via Quantile Regressions (2015, methodology): “The linear quantile specifications (1) and (2) allow for flexible patterns of interactions between covariates and heterogeneity at various quantiles.”
Asymptotics for panel quantile regression models with individual effects (2012, background): “This intrinsic difficulty has been recognized by Abrevaya and Dahl (2008), among others, and was clarified by Koenker and Hallock (2000).”
Panel data quantile regression with grouped fixed effects (2018, methodology): “Abrevaya and Dahl (2008) take a random effect view of these individual latent effects. They consider a correlated random-effect model in the spirit of Chamberlain (1982) where the individual effects are modeled through a linear regression of some covariates.”
A quantile correlated random coefficients panel data model (2016, background): “Table 2: Union wage premium (1) (2) (3) (4) (5) Pooled Pooled GMM CRC CRC OLS OLS Ch/AB Avg.”
The Poverty Impact of Rural Roads: Evidence from Bangladesh (2006, methodology): “We also offer an empirical assessment on the impact and role of roads on economic behavior across different income groups by using a fixed-effects quantile estimation approach proposed by Abrevaya and Dahl (2008).”
VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles (2015, background): “…to a vector autoregressive (VAR) structure: 1 = 0 1 + 11 1 1 + 12 2 1 2 = 0 2 + 21 1 1 + 22 2 1 1Some relevant and important papers are Koenker and Xiao (2004, 2006), Xiao (2009) in the time-series domain and Abrevaya and Dahl (2008), Lamarche (2010), Galvao (2011) in the panel data setting.”
Estimating Conditional Average Treatment Effects (2014, background): “For instance, Abrevaya and Dahl (2008) reported that the negative effect of smoking on birth weight is more pronounced at the median of the birth weight distribution than at the 90th percentile.”
Penalized Quantile Regression for Dynamic Panel Data (2010, background): “This intrinsic difficulty has been recognized by Abrevaya and Dahl (2008), among others, and is clarified by Koenker and Hallock (2000, p. 19): ‘‘Quantiles of convolutions of random variables are rather intractable objects, and preliminary differencing strategies familiar from Gaussian models have…”
A Simple Approach to Quantile Regression for Panel Data (2011, methodology): “These are a standard quantile regression estimator of Yit on Xit (QR), a correlated random effect estimator (CRE) from Abrevaya and Dahl (2008), an infeasible estimator of Y ∗it ≡ Yit − αi on Xit (INFE) and the two-step estimator (2-STEP) from Section 4.8 Note that QR and CRE are inconsistent…”
Unconditional Quantile Regression for Panel Data with Exogenous or Endogenous Regressors (2010, unknown): “Abrevaya and Dahl [2008] introduce this technique. Graham and Powell [2008] discuss a similar estimation strategy. Similarly, Chernozhukov et al. [2009] discuss identification of bounds on quantile effects in nonseparable panel models where the quantiles are defined by (αi, it). There is also a smal”
Doubly robust uniform confidence band for the conditional average treatment effect function (2016, methodology): “The program evaluation approach is employed by, for example, Almond, Chay, and Lee (2005), da Veiga and Wilder (2008) and Walker, Tekin, and Wallace (2009), and panel data analysis is carried out by Abrevaya (2006) and Abrevaya and Dahl (2008).”