A generalised linear space–time autoregressive model with space–time autoregressive disturbances
| Autor: | Oscar O. Melo, Jorge Mateu, Carlos E. Melo |
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| Rok vydání: | 2015 |
| Předmět: |
Statistics and Probability
Nonlinear autoregressive exogenous model Variables media_common.quotation_subject 05 social sciences Autocorrelation 0211 other engineering and technologies 0507 social and economic geography 021107 urban & regional planning SETAR 02 engineering and technology Random effects model Autoregressive model Econometrics Autoregressive integrated moving average Statistics Probability and Uncertainty 050703 geography STAR model Mathematics media_common |
| Zdroj: | Journal of Applied Statistics. 43:1198-1225 |
| ISSN: | 1360-0532 0266-4763 |
| DOI: | 10.1080/02664763.2015.1092506 |
| Popis: | We present a solution to problems where the response variable is a count, a rate or binary using a generalised linear space–time autoregressive model with space–time autoregressive disturbances (GLSTARAR). The possibility to test the fixed effect specification against the random effect specification of the panel data model is extended to include space–time error autocorrelation or a space–time lagged dependent variable. Space-time generalised estimating equations are used to estimate the spatio-temporal parameters in the model. We also present a measure of goodness of fit, and show the pseudo-best linear unbiased predictor for prediction purposes. Additionally, we propose a joint space–time modelling of mean and dispersion to give a solution when the variance is not constant. In the application, we use social, economic, geographic and state presence variables for 32 Colombian departments in order to analyse the relationship between the number of armed actions (AAs) per 1000 km2 committed by the guerrillas... |
| Databáze: | OpenAIRE |
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