Interpreting Dynamic Space-Time Panel Data Models
AbstractThere is a great deal of literature regarding the asymptotic properties of various approaches to estimating simultaneous space-time panel models, but little attention has been paid to how the model estimates should be interpreted. The motivation for use of space-time panel models is that they can provide us with information not available from cross-sectional spatial regressions. LeSage and Pace (2009) show that cross-sectional simultaneous spatial autoregressive models can be viewed as a limiting outcome of a dynamic space-time autoregressive process. A valuable aspect of dynamic space-time panel data models is that the own- and cross-partial derivatives that relate changes in the explanatory variables to those that arise in the dependent variable are explicit. This allows us to employ parameter estimates from these models to quantify dynamic responses over time and space as well as space-time diffusion impacts. We illustrate our approach using the demand for cigarettes over a 30 year period from 1963-1992, where the motivation for spatial dependence is a bootlegging effect where buyers of cigarettes near state borders purchase in neighboring states if there is a price advantage to doing so.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by HAL in its series Working Papers with number hal-00525740.
Date of creation: 25 Aug 2010
Date of revision:
Note: View the original document on HAL open archive server: http://hal.archives-ouvertes.fr/hal-00525740/en/
Contact details of provider:
Web page: http://hal.archives-ouvertes.fr/
Dynamic space-time panel data model; MCMC estimation; dynamic responses over time and space;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-10-23 (All new papers)
- NEP-ECM-2010-10-23 (Econometrics)
- NEP-ETS-2010-10-23 (Econometric Time Series)
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD).
If references are entirely missing, you can add them using this form.