Back to Search
Start Over
Log-linear Poisson autoregression
- Publication Year :
- 2011
-
Abstract
- We consider a log-linear model for time series of counts. This type of model provides a framework where both negative and positive association can be taken into account. In addition time dependent covariates are accommodated in a straightforward way. We study its probabilistic properties and maximum likelihood estimation. It is shown that a perturbed version of the process is geometrically ergodic, and, under some conditions, it approaches the non-perturbed version. In addition, it is proved that the maximum likelihood estimator of the vector of unknown parameters is asymptotically normal with a covariance matrix that can be consistently estimated. The results are based on minimal assumptions and can be extended to the case of log-linear regression with continuous exogenous variables. The theory is applied to aggregated financial transaction time series. In particular, we discover positive association between the number of transactions and the volatility process of a certain stock.
Details
- Database :
- OAIster
- Notes :
- Fokianos, K. and Tjøstheim, D. (2011) Log-linear Poisson autoregression. Journal of Multivariate Analysis, 102 (3). pp. 563-578. ISSN 0047-259X
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1099175076
- Document Type :
- Electronic Resource