方法对比
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| 因子增广向量自回归模型 (FAVAR)× | 马尔可夫状态转换模型 (MS-AR / MS-VAR)× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|---|
| 领域 | 计量经济学 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 2005 | 1989 | 2019 |
| 提出者≠ | Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes | Hamilton (1989); Kim & Nelson (1999) | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Multivariate time-series model | Regime-switching time series model | Linear regression |
| 开创性文献≠ | Bernanke, B. S., Boivin, J. & Eliasz, P. (2005). Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach. The Quarterly Journal of Economics, 120(1), 387-422. 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 |
| 别名≠ | factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR) | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关≠ | 4 | 5 | 5 |
| 摘要≠ | FAVAR is a multivariate time-series model that first compresses information from a very large set of variables into a few common factors, then includes those factors alongside the observed variables in a vector autoregression. It was introduced by Bernanke, Boivin and Eliasz in 2005 to study monetary policy using hundreds of macroeconomic indicators at once. | 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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