Comparar métodos

	Suavização Exponencial Simples e Dupla (SES / Holt)	Heterocedasticidade Condicional Autorregressiva Generalizada (GARCH)	Regressão por Mínimos Quadrados Ordinários (MQO)
Área	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model
Ano de origem	1957	1986	2019
Autor original	Robert G. Brown (SES); Charles C. Holt (linear trend)	Tim Bollerslev	Wooldridge (textbook treatment); classical least squares
Tipo	Exponential smoothing forecasting model	Conditional volatility model	Linear regression
Fonte seminal	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Outros nomes	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relacionados	3	5	5
Resumo	Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta.	GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.	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).