《《uk13_hughes》.pdf

Estimating spatial panel models using unbalanced data Gordon Hughes University of Edinburgh Andrea Piano Mortari Federico Belotti CEIS, Universita Roma Tor Vergata th 12 September 2013 Outline Reasons for using spatial panel models? Spatial interactions – e.g. tax environmental policies Spatial spillovers – migration or relocation of industrial activity Controlling for spatially-correlated omitted variables Econometric models, data and software Spatial lags errors – parallels with time series models Stata, R Matlab – community routines Unbalanced panels Changes in population of countries, states, etc Spatial interactions with missing data US electricity demand by state Price effects and regulation 12 September 2013 2 Spatial analysis in Stata Variety of special purpose routines written by users and available through SSC Manipulation of spatial data Cross-section spatial regressions StataCorp-related routines – also through SSC shp2dta converts ESRI shapefiles to dta files – similar to programs converting to csv or xls files spmat, spreg, spivreg, etc for construction manipulation of spatial weights and for cross-section spatial regressions 12 September 2013 3 Nature of spatial panel data Large N and/or large T? Missing data and spatial weights Contiguity vs inverse distance To (row) standardise or not? Examples: Energy demand – gasoline, electricity, etc State tax and fiscal policies Cross-country models of economic development Spatial hedonic models hedonic valuation 12 September 2013