পদ্ধতির তুলনা করুন

	আর্থিক সিরিজের জন্য মার্কভ রিজিমি-সুইচিং মডেল	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন
ক্ষেত্র	অর্থায়ন	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1989	2019
প্রবর্তক	James D. Hamilton	Wooldridge (textbook treatment); classical least squares
ধরন	Markov regime-switching time-series model	Linear regression
মৌলিক উৎস	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
অপর নাম	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
সম্পর্কিত	1	5
সারসংক্ষেপ	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).