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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Autoregressione Vettoriale Bayesiana (BVAR)× | Modello a commutazione di regime di Markov (MS-AR / MS-VAR)× | Regression with Ordinary Least Squares (OLS)× | |
|---|---|---|---|
| Campo | Econometria | Econometria | Econometria |
| Famiglia | Regression model | Regression model | Regression model |
| Anno di origine≠ | 1986 | 1989 | 2019 |
| Ideatore≠ | Litterman (1986); Bańbura, Giannone & Reichlin (2010) | Hamilton (1989); Kim & Nelson (1999) | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Bayesian multivariate time-series model | Regime-switching time series model | Linear regression |
| Fonte seminale≠ | Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗ | 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Correlati | 5 | 5 | 5 |
| Sintesi≠ | Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts. | 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. | 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). |
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