Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Pienimmän neliösumman menetelmä (OLS)× | Instrumentaalimuuttujamenetelmä (IV) kausaalisen päättelyn menetelmänä× | |
|---|---|---|
| Tieteenala≠ | Tilastotiede | Terveystaloustiede |
| Menetelmäperhe≠ | Regression model | Process / pipeline |
| Syntyvuosi≠ | 1805 | 1990s (modern applications) |
| Kehittäjä≠ | Adrien-Marie Legendre (1805); Carl Friedrich Gauss (1809) | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Tyyppi≠ | Linear parameter estimation | Method |
| Alkuperäislähde≠ | Legendre, A.-M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot, Paris. [Appendix: Sur la Méthode des moindres quarrés, pp. 72–80.] link ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Rinnakkaisnimet≠ | OLS, OLS regression, linear least squares, classical linear regression | IV, two-stage least squares, TSLS, causal estimation |
| Liittyvät≠ | 8 | 3 |
| Tiivistelmä≠ | Ordinary Least Squares (OLS) is the canonical method for estimating the parameters of a linear regression model by minimizing the sum of squared differences between observed and predicted values. First published by Adrien-Marie Legendre in 1805 and independently developed by Carl Friedrich Gauss (who claimed priority from 1795), OLS is provably optimal under the Gauss-Markov theorem: given its assumptions, it yields the Best Linear Unbiased Estimator (BLUE) of the regression coefficients. | Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes. |
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