Quantile regression for duration data: A reappraisal of the Pennsylvania Reemployment Bonus Experiments

By Roger W. Koenker, Yannis Bilias. 2001

151 citations recorded by the source corpus.

Subfields: Demography, Economics and Econometrics

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Citations in the corpus

Double/Debiased Machine Learning for Treatment and Causal Parameters (2016, methodology): “ US Department of Labor in the 1980s to test the incentive eects of alternative compensation schemes for unemployment insurance (UI). This experiment has been previously studied by Bilias (2000) and Bilias and Koenker (2002). In these experiments, UI claimants were randomly assigned either to a cont”
A SINGLE-INDEX QUANTILE REGRESSION MODEL AND ITS ESTIMATION (2012, background): “See Koenker and Bilias (2001) for more details.”
Double/debiased machine learning for treatment and structural parameters (2017, unknown): “This experiment has been previously studied by Bilias (2000) and Bilias and Koenker (2002).”
Quantiles for Counts (2002, methodology): “by Koenker and Geling (1999), Koenker and Bilias (2001) and Machado and Portugal”
An IV Model of Quantile Treatment Effects (2002, methodology): “A4 also facilitates interpretation of the QTE as a measure of the interaction between the latent ex-ante ability τ and the treatment, following Doksum (1974) and Koenker and Bilias (2001) .”
Quantile Regression: Overview and Selected Applications ∗ (2004, background): “Lehmann (1974) was the first to propose a general model of treatment response instead of the classical experimental design in which the treatment induces a simple location shift of the response distribution.”
Using quantile regression for duration analysis (2006, methodology): “Quantile regression is recently emerging as an attractive alternative to these popular models (Koenker and Bilias, 2001; Koenker and Geling, 2001; Portnoy; 2003).”
Inference on the Quantile Regression Process (2000, methodology): “Recall that a stochastic process x = fxt : t 0g that is i right continuous with left limits; ii Inference on the Quantile Regression Process integrable sup 0tt1 Ejxtj 1; and iii adapted to the ltration fF t : t 0g, i s called a submartingale if Ext + sjF t xt a:s: and is called a martingale if Ext +”
Subsampling Inference on Quantile Regression Processes (2005, background): “Further details are given in Koenker and Bilias (2001).”
What Firms Make vs. What They Know: How Firms' Production and Knowledge Boundaries Affect Competitive Advantage in the Face of Technological Change (2012, unknown): “Quantile regression methods have been applied to survival analysis by Koenker and Geling (2001) and Koenker and Bilias (2001), where the dependent variable is logtransformed time to event.”