Linganisha mbinu

	Njia ya Vigezo vya Ala (IV) kwa Utafutaji wa Kifungo	Regresheni ya Logistiki	Kielelezo cha Athari Zilizowekwa za Data ya Paneli
Nyanja	Uchumi wa Afya	Takwimu za Utafiti	Ekonometriki
Familia	Process / pipeline	Process / pipeline	Regression model
Mwaka wa asili	1990s (modern applications)	1958	2014
Mwanzilishi	Angrist & Pischke (applied econometrics); rooted in econometric theory	David Roxbee Cox	Hsiao (textbook treatment); within transformation of panel data
Aina	Method	Method	Panel data regression
Chanzo asilia	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Majina mbadala	IV, two-stage least squares, TSLS, causal estimation	logit model, binomial logistic regression, LR	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Zinazohusiana	3	3	5
Muhtasari	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).