State-space Models With Regime Switching : Classical and Gibbs-sampling Approaches With Applications
State-space models and Markov-switching models have both been highly productive paths for research in econometrics because they address primary issues in our attempts to understand the economy. Unobserved variables are important actors in our stories about consumption behavior, unemployment, inflati...
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| Autors principals: | , |
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| Format: | Llibre |
| Idioma: | English |
| Publicat: |
MIT Press
2012
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| Matèries: | |
| Accés en línia: | https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/30575 |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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| Sumari: | State-space models and Markov-switching models have both been highly productive paths for research in econometrics because they address primary issues in our attempts to understand the economy. Unobserved variables are important actors in our stories about consumption behavior, unemployment, inflation dynamics, indices of economic activity, monetary policy, and financial markets. In these situations the state-space framework, made operational by the Kalman filter, is the only one we have for making statistical inference in the time series context. There is also compelling empirical evidence that economic systems exhibit occasional jumps from one regime to another. When such a switch occurs the distribution of the data seems to change. For example, the macroeconomy periodically switches from boom to recession and back again, and dynamics differ between these two regimes. Financial markets periodically switch from a low-volatility regime to a high-volatility regime, and then back again. It is attractive to model such transitions as a Markov process. |
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