Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Байєсівська модель ARMA× | Байєсівська OLS (Байєсівська звичайна регресія методом найменших квадратів)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1970s–1980s | 1971 |
| Автор методу≠ | Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s | Arnold Zellner |
| Тип≠ | Bayesian time series model | Bayesian linear regression |
| Основоположне джерело≠ | Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70. link ↗ | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376 |
| Інші назви | Bayesian ARMA, B-ARMA, Bayesian autoregressive moving average, ARMA with Bayesian inference | Bayesian linear regression, Bayesian normal regression, BLR, Bayesian least squares |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | The Bayesian ARMA model applies Bayesian inference to the classical autoregressive moving average framework for stationary univariate time series. Rather than producing single point estimates for the AR and MA parameters, it yields full posterior distributions, naturally incorporating prior knowledge and providing coherent uncertainty quantification over forecasts and impulse responses. | Bayesian OLS combines the classical linear regression likelihood with prior distributions over the coefficients and error variance. Rather than reporting point estimates, it produces full posterior distributions that quantify both estimated effects and their uncertainty. The approach is especially valuable when prior knowledge is available or when samples are small. |
| ScholarGateНабір даних ↗ |
|
|