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An alternative approach for introducing instrumental variables based on ordinary least squares omitted variable bias

Quinn Keefer

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Abstract

An alternative approach for introducing instrumental variables in econometrics courses is presented in this article. The method is based on the ordinary least squares omitted variable bias formula. The intuition for the approach capitalizes on students’ understanding and intuition of omitted variables. Thus, if students understand omitted variable bias, they can understand instrumental variables. Furthermore, using omitted variable bias to derive the instrumental variables estimator clearly highlights the critical assumptions of instrumental variables. The author illustrates the proposed method mathematically and discusses the intuition in both simple and multiple regression cases.

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What this paper is about

An alternative approach for introducing instrumental variables in econometrics courses is presented in this article. The method is based on the ordinary least squares omitted variable bias formula. The intuition for the approach capitalizes on students’ understanding and intuition of omitted variables. Thus, if students understand omitted variable bias, they can understand instrumental variables. Furthermore, using omitted variable bias to derive the instrumental variables estimator clearly highlights the critical assumptions of instrumental variables. The author illustrates the proposed method mathematically and discusses the intuition in both simple and multiple regression cases.

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Available abstract

An alternative approach for introducing instrumental variables in econometrics courses is presented in this article. The method is based on the ordinary least squares omitted variable bias formula. The intuition for the approach capitalizes on students’ understanding and intuition of omitted variables. Thus, if students understand omitted variable bias, they can understand instrumental variables. Furthermore, using omitted variable bias to derive the instrumental variables estimator clearly highlights the critical assumptions of instrumental variables. The author illustrates the proposed method mathematically and discusses the intuition in both simple and multiple regression cases.

Key concepts: Instrumental variable, Ordinary least squares, Omitted-variable bias, Mathematics, Statistics, Econometrics, Least-squares function approximation, Variable (mathematics)

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