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Introduction

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Average Treatment Effect Bounds with an Instrumental Variable: Theory and Practice

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Abstract

This chapter introduces the purpose and main content of this book. It also presents the general idea behind using partial identification or bounds in econometrics. It ends with the organization of this book.

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Notes

  1. 1.

    The marginal treatment effect parameter bridges the gap between structural models and treatment effects.

  2. 2.

    Point identfication means that we can theoretically learn the true parameter value in infinite samples. Intuitively, it implies that we can provide a single-valued estimate of our parameter of interest.

  3. 3.

    Partial identification approaches are also used in other fields of economics, for example, game theory and auction models (see, e.g., Tamer 2010; Ho and Rosen 2015).

  4. 4.

    For reviews on inference for partially identified models see, for example, Tamer (2010) and Canay and Shaikh (2017).

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Author information

Authors and Affiliations

  1. Department of Economics, Orfalea College of Business, California Polytechnic State University, San Luis Obispo, CA, USA

    Carlos A. Flores

  2. School of Labor and Human Resources, Renmin University of China, Beijing, China

    Xuan Chen

Authors
  1. Carlos A. Flores
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  2. Xuan Chen
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Corresponding author

Correspondence to Xuan Chen .

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Flores, C.A., Chen, X. (2018). Introduction. In: Average Treatment Effect Bounds with an Instrumental Variable: Theory and Practice. Springer, Singapore. https://doi.org/10.1007/978-981-13-2017-0_1

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  • DOI: https://doi.org/10.1007/978-981-13-2017-0_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2016-3

  • Online ISBN: 978-981-13-2017-0

  • eBook Packages: Economics and FinanceEconomics and Finance (R0)

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