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Asymptotic theory for M-estimators of boundaries

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  • Knight, Keith

Abstract

We consider some asymptotic distribution theory for M-estimators of the parameters of a linear model whose errors are non-negative; these estimators are the solutions of constrained optimization problems and their asymptotic theory is non-standard. Under weak conditions on the distribution of the errors and on the design, we show that a large class of estimators have the same asymptotic distributions in the case of i.i.d. errors; however, this invariance does not hold under non-i.i.d. errors.

Suggested Citation

  • Knight, Keith, 2003. "Asymptotic theory for M-estimators of boundaries," SFB 373 Discussion Papers 2003,37, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  • Handle: RePEc:zbw:sfb373:200337
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    References listed on IDEAS

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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. B. Nielsen & N. Shephard, 2003. "Likelihood analysis of a first-order autoregressive model with exponential innovations," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(3), pages 337-344, May.
    3. Donald, Stephen G. & Paarsch, Harry J., 2002. "Superconsistent estimation and inference in structural econometric models using extreme order statistics," Journal of Econometrics, Elsevier, vol. 109(2), pages 305-340, August.
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