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Exogenous impact and conditional quantile functions

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  • Andrew Chesher

    ()
    (Institute for Fiscal Studies and 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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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP01/01.

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Length: 14 pp.
Date of creation: Aug 2001
Date of revision:
Handle: RePEc:ifs:cemmap:01/01

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  1. 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.
  2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  3. 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.
  4. Powell, James L, 1983. "The Asymptotic Normality of Two-Stage Least Absolute Deviations Estimators," Econometrica, Econometric Society, vol. 51(5), pages 1569-75, September.
  5. 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.
  6. repec:cup:etheor:v:6:y:1990:i:3:p:295-317 is not listed on IDEAS
  7. Khan, Shakeeb, 2001. "Two-stage rank estimation of quantile index models," Journal of Econometrics, Elsevier, vol. 100(2), pages 319-355, February.
  8. 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.
  9. Amemiya, Takeshi, 1982. "Two Stage Least Absolute Deviations Estimators," Econometrica, Econometric Society, vol. 50(3), pages 689-711, May.
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Cited by:
  1. 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).
  2. Andrew Chesher, 2002. "Instrumental Values," CeMMAP working papers CWP17/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.

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