Estimating the parameters of the Markov probability model from aggregate time series data
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Estimating the parameters of the Markov probability model from aggregate time series data

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Published by North-Holland in Amsterdam .
Written in English

Subjects:

  • Econometrics.,
  • Estimation theory.,
  • Markov processes.

Book details:

Edition Notes

StatementT. C. Lee, G. G. Judge, A. Zellner
SeriesContributions to economic analysis -- v. 65
ContributionsJudge, George G., Zellner, Arnold.
Classifications
LC ClassificationsHB"74"M3"L396"1977
The Physical Object
Pagination260 p.
Number of Pages260
ID Numbers
Open LibraryOL20047072M
LC Control Number77110503

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Contributions to Economic Analysis: Estimating the Parameters of the Markov Probability Model from Aggregate Time Series Data (Volume 65) by T. C. Lee and a great selection of related books, art and collectibles available now at Estimating the parameters of the Markov probability model from aggregate time series data (Contributions to economic analysis) [Lee, T. C] on *FREE* shipping on qualifying offers. Estimating the parameters of the Markov probability model from aggregate time Cited by: Estimating the parameters of the Markov probability model from aggregate time series data. Amsterdam, North-Holland Pub. Co., (OCoLC) Material Type: Internet resource: Document Type: Book, Internet Resource: All Authors / Contributors: T C Lee; George G Judge; Arnold Zellner. Estimating the parameters of the Markov probability model from aggregate time series data. Amsterdam: North-Holland Pub. Co. MLA Citation. Lee, T. C. and Judge, George G. and Zellner, Arnold. Estimating the parameters of the Markov probability model from aggregate time series data. [By] T. C. Lee, G. G. Judge [and] A. Zellner North-Holland Pub.

Estimating the parameters of the Markov probability model from aggregate time series data. Responsibility [By] T. C. Lee, G. G. Judge [and] A. Zellner. Imprint Amsterdam, North-Holland Pub. Co., Physical description p. 23 cm. $\begingroup$ For a continuous state space the problem becomes much more complicated, as you then need to estimate a transition function rather than a matrix. In that case, since the marginal probability of being in any particular state is 0 (similarly to how the probability of taking any particular point in the sample space is 0 for any continuous distribution) what I've described above doesn. The parameters of a discrete stationary Markov model are transition probabilities between states. Traditionally, data consist in sequences of observed states for a given number of individuals over. This paper considers the implementation of a nonstationary, heterogeneous Markov model for the analysis of a binary dependent variable in a time series of independent cross sections.

His work has spanned many research questions in econometrics, including the estimation of parameters for a Markov probability model from time series data; inference from spatial and temporal price and allocation models; and application of information theory to recover systematic behavior from noisy data. Judge has written a number of Citizenship: United States of America. Markov-switching model Hamilton () Finite number of unobserved states Suppose there are two states 1 and 2 Let s t denote a random variable such that s t = 1 or s t = 2 at any time s t follows a rst-order Markov process Current value of s t depends only on the immediate past value We do not know which state the process is in but can only estimate the.   Lee TC, Judge GG, Zellner A. Estimating the Parameters of the Markov Probability Model From Aggregate Time Series Data. North Holland; [2] Dent W, Ballintine R. A review of the estimation of transition probabilities in Markov chains. The Australian Journal of Agricultural Economics. ; 69– [3] MacRae : Arie Ten Cate. Estimating the Parameters of the Markov Probability Model from Aggregate Time Series Data. North-Holland, Amsterdam.]. The problem of estimating a nonstationary process however has not been solved, although Hallberg [Hallberg, M. C. Cited by: 3.