Consistent Estimation of Linear and Non‐linear Autoregressive Models with Markov Regime | Zendy

Krishnamurthy Vikram | Zendy; Ryden Tobias | Zendy

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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

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