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| 비선형 하우즈만 모형 적합성 검정× | 인과 추론을 위한 도구 변수(IV) 방법× | |
|---|---|---|
| 분야≠ | 계량경제학 | 보건경제학 |
| 계열≠ | Regression model | Process / pipeline |
| 기원 연도≠ | 1978 (nonlinear extension developed through 1980s–1990s) | 1990s (modern applications) |
| 창시자≠ | Jerry A. Hausman | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| 유형≠ | Specification / endogeneity test | Method |
| 원전≠ | Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| 별칭 | Hausman specification test (nonlinear), nonlinear endogeneity test, Wu-Hausman test (nonlinear), NL-Hausman test | IV, two-stage least squares, TSLS, causal estimation |
| 관련 | 3 | 3 |
| 요약≠ | The Nonlinear Hausman test extends Hausman's (1978) endogeneity specification test to nonlinear models such as probit, logit, Tobit, and count-data regressions. It tests whether suspected regressors are endogenous — i.e., correlated with the error term — in a model where the outcome or the relationship is inherently nonlinear, ensuring that IV-corrected estimates are necessary. | 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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