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Exogenous impact and conditional quantile functions

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Author Info

  • Andrew Chesher

    (University College London)

Abstract

An exogenous impact function is defined as the derivative of a structural function with respect to an endogenous variable, other variables, including unobservable variables held fixed. Unobservable variables are fixed at specific quantiles of their marginal distributions. Exogenous impact functions reveal the impact of an exogenous shift in a variable perhaps determined endogenously in the data generating process. They provide information about the variation in exogenous impacts across quantiles of the distributions of the unobservable variables that appear in the structural model. This paper considers nonparametric identification of exogenous impact functions under quantile independence conditions. It is shown that, when valid instrumental variables are present, exogenous impact functions can be identified as functionals of conditional quantile functions that involve only observable random variables. This suggests parametric, semiparametric and nonparametric strategies for estimating exogenous impact functions.

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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0108001.

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Length: 13 pages
Date of creation: 17 Aug 2001
Date of revision:
Handle: RePEc:wpa:wuwpem:0108001

Note: Type of Document - Acrobat PDF; prepared on Windows 2000 Professional PC; to print on A4 paper; pages: 13 ; figures: none
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Web page: http://128.118.178.162

Related research

Keywords: endogeneity; quantile regression; identification; structural models; instrumental variables; quantile independence;

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References

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  1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  2. Heckman, James J & Smith, Jeffrey, 1997. "Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 487-535, October.
  3. Powell, James L, 1983. "The Asymptotic Normality of Two-Stage Least Absolute Deviations Estimators," Econometrica, Econometric Society, vol. 51(5), pages 1569-75, September.
  4. Newey, Whitney K. & Powell, James L., 1990. "Efficient Estimation of Linear and Type I Censored Regression Models Under Conditional Quantile Restrictions," Econometric Theory, Cambridge University Press, vol. 6(03), pages 295-317, September.
  5. Amemiya, Takeshi, 1982. "Two Stage Least Absolute Deviations Estimators," Econometrica, Econometric Society, vol. 50(3), pages 689-711, May.
  6. repec:cup:etheor:v:6:y:1990:i:3:p:295-317 is not listed on IDEAS
  7. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
  8. Khan, Shakeeb, 2001. "Two-stage rank estimation of quantile index models," Journal of Econometrics, Elsevier, vol. 100(2), pages 319-355, February.
  9. Jesse Levin, 2001. "For whom the reductions count: A quantile regression analysis of class size and peer effects on scholastic achievement," Empirical Economics, Springer, vol. 26(1), pages 221-246.
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Citations

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Cited by:
  1. Lingjie Ma & Roger Koenker, 2004. "Quantile regression methods for recursive structural equation models," CeMMAP working papers CWP01/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Andrew Chesher, 2002. "Instrumental Values," CeMMAP working papers CWP17/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. Brunello, Giorgio & Fort, Margherita & Weber, Guglielmo, 2007. "“For One More Year with You”: Changes in Compulsory Schooling, Education and the Distribution of Wages in Europe," IZA Discussion Papers 3102, Institute for the Study of Labor (IZA).

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