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Gaussian autoregressive process with dependent innovations. Some asymptotic results
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gobbi, Fabio Mulinacci, Sabrina |
| Copyright Year | 2017 |
| Abstract | In this paper we introduce a modified version of a gaussian standard first-order autoregressive process where we allow for a dependence structure between the state variable $Y_{t-1}$ and the next innovation $\xi_t$. We call this model dependent innovations gaussian AR(1) process (DIG-AR(1)). We analyze the moment and temporal dependence properties of the new model. After proving that the OLS estimator does not consistently estimate the autoregressive parameter, we introduce an infeasible estimator and we provide its $\sqrt{T}$-asymptotic normality. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://arxiv.org/pdf/1704.03262v1.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
