Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Two-Stage Least Squares (2SLS)× | Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)× | |
|---|---|---|
| Nyanja≠ | Uhitimisho wa Kisababishi | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2009 | 2019 |
| Mwanzilishi≠ | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) | Wooldridge (textbook treatment); classical least squares |
| Aina≠ | Instrumental-variables regression | Linear regression |
| Chanzo asilia≠ | 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 |
| Majina mbadala≠ | instrumental variables, IV estimation, 2SLS, instrumental variable regression | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | 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). |
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