ScholarGate
Assistente

Comparar métodos

Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.

Modelo ARIMA (Autoregressive Integrated Moving Average)×Regressão por Mínimos Quadrados Ordinários (MQO)×Modelo de Vetor de Correção de Erros (VECM)×
ÁreaEconometriaEconometriaEconometria
FamíliaRegression modelRegression modelRegression model
Ano de origem201520191987
Autor originalBox & Jenkins (Box-Jenkins methodology)Wooldridge (textbook treatment); classical least squaresEngle & Granger
TipoUnivariate time-series modelLinear regressionMultivariate time-series model
Fonte seminalBox, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276. DOI ↗
Outros nomesBox-Jenkins model, ARIMA(p,d,q), ARIMA Modeliordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonuvector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli)
Relacionados554
ResumoARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).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 Vector Error Correction Model is a multivariate time-series model for cointegrated series that captures both their short-run dynamics and their long-run equilibrium relationship. It was introduced by Engle and Granger in 1987 as part of the cointegration and error-correction framework.
ScholarGateConjunto de dados
  1. v1
  2. 1 Fontes
  3. PUBLISHED
  1. v1
  2. 1 Fontes
  3. PUBLISHED
  1. v1
  2. 1 Fontes
  3. PUBLISHED

Ir para a pesquisa Baixar slides

ScholarGateComparar métodos: ARIMA · OLS Regression · VECM. Recuperado em 2026-06-18 de https://scholargate.app/pt/compare