방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 금융 시계열을 위한 마르코프 회귀 전환 모형× | 최소제곱법(OLS) 회귀× | |
|---|---|---|
| 분야≠ | 재무학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1989 | 2019 |
| 창시자≠ | James D. Hamilton | Wooldridge (textbook treatment); classical least squares |
| 유형≠ | Markov regime-switching time-series model | Linear regression |
| 원전≠ | 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 |
| 별칭≠ | Markov switching model, Hamilton regime-switching model, MS-AR, hidden Markov regime model | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 관련≠ | 1 | 5 |
| 요약≠ | The Markov regime-switching model, introduced by James D. Hamilton in 1989, is a hidden-state time-series model in which financial series such as returns or volatility behave with different parameters across distinct economic regimes (bull/bear or high/low volatility). It is the financial application of Hamilton's MS-AR model, where an unobserved Markov state governs which parameter set is active at each point in time. | 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). |
| ScholarGate데이터셋 ↗ |
|
|