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| Model Markowa z przełączaniem reżimów (MS-AR / MS-VAR)× | Model ARIMA (Autoregressive Integrated Moving Average)× | Regresja metodą najmniejszych kwadratów (OLS)× | |
|---|---|---|---|
| Dziedzina | Ekonometria | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model | Regression model |
| Rok powstania≠ | 1989 | 2015 | 2019 |
| Twórca≠ | Hamilton (1989); Kim & Nelson (1999) | Box & Jenkins (Box-Jenkins methodology) | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Regime-switching time series model | Univariate time-series model | Linear regression |
| Źródło pierwotne≠ | Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. DOI ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Inne nazwy≠ | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Pokrewne | 5 | 5 | 5 |
| Podsumowanie≠ | The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions. | ARIMA 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). |
| ScholarGateZbiór danych ↗ |
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