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Consistent Estimation of Linear and Non‐linear Autoregressive Models with Markov Regime
Author(s) -
Krishnamurthy Vikram,
Ryden Tobias
Publication year - 1998
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/1467-9892.00093
Subject(s) - mathematics , autoregressive model , identifiability , estimator , consistency (knowledge bases) , star model , markov chain , econometrics , likelihood function , statistics , markov model , markov property , estimation theory , autoregressive integrated moving average , time series , geometry
An autoregressive process with Markov regime is an autoregressive process for which the regression function at each time‐point is given by a (non‐observable) Markov chain. We examine maximum likelihood estimation for such models and show consistency of a conditional maximum likelihood estimator. Also identifiability issues are discussed