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| Инструментални променливи чрез двуетапни най-малки квадрати (IV/2SLS)× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|
| Област≠ | Причинно-следствено заключение | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2009 | 2019 |
| Създател≠ | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Instrumental-variables regression | Linear regression |
| Основополагащ източник≠ | Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Други названия≠ | instrumental variables, IV estimation, 2SLS, instrumental variable regression | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани | 5 | 5 |
| Резюме≠ | IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009). | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateНабор от данни ↗ |
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