Salīdzināt metodes

	GMM sistēma ar Furjē sistēmu	Dinamiskais paneļa datu modelis
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2000s–2010s	1988–1991
Autors	Blundell & Bond (System GMM, 1998); Fourier augmentation adapted from Gallant (1981) and Becker, Enders & Lee (2006)	Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988)
Tips	Dynamic panel GMM with Fourier smooth-break regressors	Dynamic regression / GMM estimation
Pirmavots	Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.	Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.
Citi nosaukumi	Fourier System GMM, Fourier-augmented Blundell-Bond GMM, smooth-break system GMM, Fourier SGMM	dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model
Saistītās	6	5
Kopsavilkums	Fourier system GMM embeds Fourier trigonometric terms into the System GMM estimator of Blundell and Bond (1998) to accommodate smooth, gradual structural breaks in dynamic panel data. By adding sine and cosine components as regressors, the estimator captures unknown, potentially multiple regime shifts without requiring prior knowledge of break dates, while preserving the instrument-based controls for endogeneity and individual fixed effects.	The dynamic panel data model extends standard panel regression by including a lagged value of the outcome variable as a regressor, capturing persistence and adjustment dynamics. Because the lagged dependent variable is correlated with the unit-specific fixed effect, ordinary OLS or within estimators are biased; GMM-based methods using internal instruments are the standard remedy.