Estimation in nonstationary random coefficient autoregressive models

By István Berkes, Lajos Horv'ath, S. Ling. 2009

52 citations recorded by the source corpus.

Abstract.  We investigate the estimation of parameters in the random coefficient autoregressive (RCA) model Xk = (ϕ + bk)Xk−1 + ek, where (ϕ, ω2, σ2) is the parameter of the process, , . We consider a nonstationary RCA process satisfying E log |ϕ + b0| ≥ 0 and show that σ2 cannot be estimated by the quasi‐maximum likelihood method. The asymptotic normality of the quasi‐maximum likelihood estimator for (ϕ, ω2) is proven so that the unit root problem does not exist in the RCA model.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c07eff09bca734da6cf88c1241d7095d4896f436

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Lajos Horv'ath
2. Alexander Aue
3. Gregory Rice
4. Piotr Kokoszka
5. Matthew Reimherr
6. Xianyang Zhang
7. S. Ling
8. Christian Francq

In Statistics and Probability

1. Alexander Aue
2. Lajos Horv'ath
3. Gregory Rice
4. Matthew Reimherr
5. Piotr Kokoszka
6. Marie Husková
7. Haeran Cho
8. Claudia Kirch

Citations in the corpus

• QUASI-LIKELIHOOD ESTIMATION IN STATIONARY AND NONSTATIONARY AUTOREGRESSIVE MODELS WITH RANDOM COEFFICIENTS (2011, background): “The weak consistency and asymptotic normality of θ̂ (R) n (y) have been established in Berkes, Horváth, and Ling (2009).”