Linganisha mbinu

	Nonlinear Difference GMM	Njia ya Vigezo vya Ala (IV) kwa Utafutaji wa Kifungo
Nyanja	Ekonometriki	Uchumi wa Afya
Familia	Regression model	Process / pipeline
Mwaka wa asili	1991–2010	1990s (modern applications)
Mwanzilishi	Wooldridge; building on Arellano and Bond (1991)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Aina	Nonlinear panel estimator	Method
Chanzo asilia	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 9780262232586	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Majina mbadala	nonlinear diff-GMM, nonlinear Arellano-Bond GMM, first-difference nonlinear GMM, NL-GMM	IV, two-stage least squares, TSLS, causal estimation
Zinazohusiana	5	3
Muhtasari	Nonlinear Difference GMM extends the Arellano-Bond difference GMM estimator to models where the structural relationship between the outcome and its predictors is inherently nonlinear. By first-differencing to eliminate individual fixed effects and then applying GMM moment conditions with lagged levels as instruments, it consistently estimates parameters in dynamic panel settings without requiring a linear functional form.	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.