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220922 ||| eng |
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|a 0262277115
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|a 9780262277112
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|a 0585087164
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|a 9780585087160
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4 |
|a HB135
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| 100 |
1 |
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|a Kim, Chang-Jin
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| 245 |
0 |
0 |
|a State-space models with regime switching
|h Elektronische Ressource
|b classical and Gibbs-sampling approaches with applications
|c Chang-Jin Kim and Charles R. Nelson
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| 260 |
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|a Cambridge, Mass.
|b MIT Press
|c 1999
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| 300 |
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|a xii, 297 pages
|b illustrations
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| 653 |
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|a State-space methods
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| 653 |
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|a ECONOMICS/General
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| 653 |
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|a Sampling (Statistics)
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| 653 |
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|a Markov processes
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| 653 |
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|a Econometric models
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| 653 |
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|a Economics / Mathematical models
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| 653 |
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|a Econometrics
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| 653 |
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|a Heteroscedasticity
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| 700 |
1 |
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|a Nelson, Charles R.
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b MITArchiv
|a MIT Press eBook Archive
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| 028 |
5 |
0 |
|a 10.7551/mitpress/6444.001.0001
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| 856 |
4 |
0 |
|u https://doi.org/10.7551/mitpress/6444.001.0001?locatt=mode:legacy
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 330/.01/5118
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| 520 |
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|a "Both state-space models and Markov-switching models have been highly productive paths for empirical research in macroeconomics and finance. This book presents recent advances in econometric methods that make feasible the estimation of models that have both features. One approach, in the classical framework, approximates the likelihood function; the other, in the Bayesian framework, uses Gibbs-sampling to simulate posterior distributions from data."--Jacket
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