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 | This article may be too technical for most readers to understand. (January 2023) |
In econometrics, endogeneity broadly refers to situations in which an explanatory variable is correlated with the error term.[1] The distinction between endogenous and exogenous variables originated in simultaneous equations models, where one separates variables whose values are determined by the model from variables which are predetermined.[a][2] Ignoring simultaneity in the estimation leads to biased estimates as it violates the exogeneity assumption of the Gauss–Markov theorem. The problem of endogeneity is often ignored by researchers conducting non-experimental research and doing so precludes making policy recommendations.[3] Instrumental variable techniques are commonly used to mitigate this problem.
Besides simultaneity, correlation between explanatory variables and the error term can arise when an unobserved or omitted variable is confounding both independent and dependent variables, or when independent variables are measured with error.[4]
- ^ Wooldridge, Jeffrey M. (2009). Introductory Econometrics: A Modern Approach (Fourth ed.). Australia: South-Western. p. 88. ISBN 978-0-324-66054-8.
- ^ Kmenta, Jan (1986). Elements of Econometrics (Second ed.). New York: MacMillan. pp. 652–53. ISBN 0-02-365070-2.
- ^ Antonakis, John; Bendahan, Samuel; Jacquart, Philippe; Lalive, Rafael (December 2010). "On making causal claims: A review and recommendations" (PDF). The Leadership Quarterly. 21 (6): 1086–1120. doi:10.1016/j.leaqua.2010.10.010. ISSN 1048-9843.
- ^ Johnston, John (1972). Econometric Methods (Second ed.). New York: McGraw-Hill. pp. 267–291. ISBN 0-07-032679-7.
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