Salīdzināt metodes

	Parastā mazāko kvadrātu (OLS) regresija	Mūsdienu pārejas autoregresijas (STAR) modelis	System GMM (Arellano-Bover / Blundell-Bond)
Nozare	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	2019	1994	1998
Autors	Wooldridge (textbook treatment); classical least squares	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)	Arellano & Bover (1995); Blundell & Bond (1998)
Tips	Linear regression	Nonlinear time-series regime-switching model	Dynamic panel data estimator
Pirmavots	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218.	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	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR	Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond)
Saistītās	5	4	4
Kopsavilkums	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	The Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purchasing-power-parity deviations.	System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small.