Comparar métodos

	Modelo Autorregressivo de Transição Suave (STAR)	Regressão por Mínimos Quadrados Ordinários (MQO)	Autoregressores Vetoriais de Painel (Panel VAR)	Regressão Quantílica
Área	Econometria	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model	Regression model
Ano de origem	1994	2019	1988	1978
Autor original	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)	Wooldridge (textbook treatment); classical least squares	Holtz-Eakin, Newey & Rosen	Koenker & Bassett
Tipo	Nonlinear time-series regime-switching model	Linear regression	Panel vector autoregression	Conditional quantile regression
Fonte seminal	Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Outros nomes	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	PVAR, panel vector autoregression, Panel VAR (PVAR)	conditional quantile regression, regression quantiles, Kantil Regresyon
Relacionados	4	5	3	5
Resumo	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.	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).	Panel VAR extends the vector autoregression model to panel data, modelling the dynamic interactions among several variables while controlling for cross-unit heterogeneity through fixed effects. It was introduced by Holtz-Eakin, Newey and Rosen in 1988 and produces impulse-response functions and variance decompositions at the panel level.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.