Set identification of the censored quantile regression model for short panels with fixed effects

Li, Tong; Oka, Tatsushi

doi:10.1016/j.jeconom.2015.03.005

Cited by 14 publications

(8 citation statements)

References 55 publications

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“…D 'Haultfoeuille et al (2015) present identification of nonseparable models using repeated cross-sections. Other recent contributions to panel quantile regression include Abrevaya and Dahl (2008), Canay (2011), Kato et al (2012, Rosen (2012), Galvao et al (2013), Galvao and Kato (2014), Chernozhukov et al (2015), Graham et al (2015), Li and Oka (2015), Arellano and Bonhomme (2016), Chetverikov et al (2016) and Khan et al (2016) among others.…”

Section: Introductionmentioning

confidence: 99%

Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods

Callaway

Oka

2018

Journal of Econometrics

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This paper shows that the Conditional Quantile Treatment Effect on the Treated can be identified using a combination of (i) a conditional Distributional Difference in Differences assumption and (ii) an assumption on the conditional dependence between the change in untreated potential outcomes and the initial level of untreated potential outcomes for the treated group. The second assumption recovers the unknown dependence from the observed dependence for the untreated group. We also consider estimation and inference in the case where all of the covariates are discrete. We propose a uniform inference procedure based on the exchangeable bootstrap and show its validity. We conclude the paper by estimating the effect of state-level changes in the minimum wage on the distribution of earnings for subgroups defined by race, gender, and education.

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Section: Introductionmentioning

confidence: 99%

Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods

Callaway

Oka

2018

Journal of Econometrics

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“…He shows how inequalities implied by the conditional quantile restriction can be differenced across time to obtain inequalities involving conditional moments of observable quantities from which the fixed effects are absent. Li and Oka (2015) extend similar ideas to analyze short panels with censoring. Chernozhukov, Fernandez-Val, Hahn, and Newey (2013) derive bounds on average and quantile treatment effects in a variety of nonseparable panel data models, both nonparametric and semiparametric.…”

Section: Resultsmentioning

confidence: 90%

Partial identification in applied research: benefits and challenges

Rosen

2016

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may UCL and CEMMAP October 7, 2015Abstract Advances in the study of partial identification allow applied researchers to learn about parameters of interest without making assumptions needed to guarantee point identification. We discuss the roles that assumptions and data play in partial identification analysis, with the goal of providing information to applied researchers that can help them employ these methods in practice. To this end, we present a sample of econometric models that have been used in a variety of recent applications where parameters of interest are partially identified, highlighting common features and themes across these papers. In addition, in order to help illustrate the combined roles of data and assumptions, we present numerical illustrations for a particular application, the joint determination of wages and labor supply. Finally we discuss the benefits and challenges of using partially identifying models in empirical work and point to possible avenues of future research.

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“…Median regression models when the outcome variables are endogenously censored are discussed in Khan and Tamer (2009) and Khan, Ponomareva, and Tamer (2011). Quantile panel data models when the outcome variables are censored are discussed in Li and Oka (2015).…”

Section: Introductionmentioning

confidence: 99%

“…Our framework, however, allows for a very large range of endogenous censoring for arbitrary set-valued outcomes. More recently, Li and Oka (2015) consider linear quantile regressions with a censored outcome variable in the framework of Rosen (2012). We do not deal with panel data, but the model considered in this paper includes the case of censored outcome variables as argued above.…”

Section: Introductionmentioning

confidence: 99%

Quantile Regression with Interval Data

Beresteanu¹,

Sasaki²

2017

Preprint

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This paper investigates the identification of quantiles and quantile regression parameters when observations are set valued. We define the identification set of quantiles of random sets in a way that extends the definition of quantiles for regular random variables. We then give sharp characterization of this set by extending concepts from random set theory. For quantile regression parameters, we show that the identification set is characterized by a system of conditional moment inequalities. This characterization extends that of parametric quantile regression for regular random variables. Estimation and inference theories are developed for continuous cases, discrete cases, nonparametric conditional quantiles, and parametric quantile regressions.A fast computational algorithm of set linear programming is proposed. Monte Carlo experiments support our theoretical properties.

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Set identification of the censored quantile regression model for short panels with fixed effects

Cited by 14 publications

References 55 publications

Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods

Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods

Partial identification in applied research: benefits and challenges

Quantile Regression with Interval Data

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