Сравнение методов

	Робастная оценка инструментальными переменными	Панельные инструментальные переменные (Панельные IV / 2МНК)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	1949–2019	1978-1991
Автор метода	Anderson & Rubin (1949); Stock, Wright & Yogo (2002); Andrews, Stock & Sun (2019)	Hausman (1978); Anderson & Hsiao (1982); Arellano & Bond (1991)
Тип	Causal inference / robust estimation	Causal inference / panel regression
Основополагающий источник	Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics, 20(4), 518-529.	Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277-297.
Другие названия	Robust IV, Weak-instrument-robust IV, Robust 2SLS, Weak-instrument-robust inference	Panel IV, Panel 2SLS, Within-IV, Fixed-Effects IV
Связанные	4	4
Сводка	Robust Instrumental Variables estimation extends standard IV and two-stage least squares (2SLS) by guarding against weak-instrument bias and non-standard inference. Methods such as the Anderson-Rubin test, Limited Information Maximum Likelihood (LIML), and the Conditional Likelihood Ratio test provide valid confidence sets and hypothesis tests even when instruments are weak or only partially identified, making IV inference reliable in settings where standard 2SLS breaks down.	Panel data instrumental variables combines the bias-correcting power of instrumental variables (IV) with the within-unit variation exploited by panel data methods. It addresses endogeneity — omitted variables, reverse causation, or measurement error — in longitudinal settings where observations are repeated across units and time. Seminal contributions come from Hausman (1978) on specification testing and Arellano and Bond (1991) on GMM-based panel IV.