Conditional Heteroskedasticity in Count Data Regression: Self-Feeding Activity in Fish
The paper introduces a new approach to incorporating time dependent overdispersion for Poisson related regression models. To handle the added flexibility in conditional heteroskedasticity in time series count data some wellknown estimators are adapted and a GMM type estimator is suggested. The estim...
| Main Authors: | , |
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| Format: | Report |
| Language: | unknown |
| Subjects: | |
| Online Access: | http://www.econ.umu.se/DownloadAsset.action?contentId=73628&languageId=3&assetKey=ues595 |
| Summary: | The paper introduces a new approach to incorporating time dependent overdispersion for Poisson related regression models. To handle the added flexibility in conditional heteroskedasticity in time series count data some wellknown estimators are adapted and a GMM type estimator is suggested. The estimators are applied to a time series of self-feeding activity in Arctic charr. There is strong support for both a dynamic conditional mean function and a dynamic model for the overdispersion. Poisson; Overdispersion; ARCH; Estimation; Self-Feeding; Arctic Charr |
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