Nonparametric Instrumental Variables Estimation of Structural Quantile Effects

By Scaillet Olivier and Gagliardini Patrick
Econometrica (2012)

  • Olivier Scaillet

    University of Geneva and Swiss Finance Institute

    Switzerland

  • Patrick Gagliardini

    University of Lugano and Swiss Finance Institute

    Switzerland

Created

September 13, 2017

Last update

September 14, 2017

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1) GAUSS codes for Section 5.1 Monte-Carlo results: Each GAUSS code is annotated and contains a step-by-step description of the successive computations. We use normality_sin_15.prg and normality_sin_15_order2.prg to generate the inputs of Table 1 and Figure 1. (2) Data used for Section 5.2 Empirical illustration: agec.txt : age in years; att.txt, sprint.txt, mci.txt : 0/1 variables indicating long-distance interexchange carrier identity; edhhc.txt : years of education; incomec.txt : annual income in dollars; ixdtemin.txt = use in minutes; ixdtechg.txt = price of use in dollars; (3) GAUSS codes for Section 5.2 Empirical illustration: Each GAUSS code is annotated and contains a step-by-step description of the successive computations. We use Normality_QTiR_HS_15.prg and Normality_QTiR_HS_15_order2.prg to generate the inputs of Figure 2, and QTiR_HS_15.prg to generate the inputs of Figure 3. For the NIVR results, we use TiR_HS_7.prg to generate the inputs of Figure 3, and empirical_boot_HS_7.prg and empirical_boot_pen_HS_7.prg to get the results of the specification tests based on J-type statistics. (4) MATLAB codes to display figures: We use Figure1.m, Figure2.m and Figure3.m to load the inputs and generate the displays of Figures 1, 2 and 3. The folder graphic_input_files contains the inputs that are used to generate the figures.

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