The used.cars data frame has 48 rows and 2 columns. The data set includes a neighbours list for the 48 states excluding DC from poly2nb().

used.cars

Format

This data frame contains the following columns:

  • tax.charges taxes and delivery charges for 1955-9 new cars

  • price.1960 1960 used car prices by state

Source

Hanna, F. A. 1966 Effects of regional differences in taxes and transport charges on automobile consumption, in Ostry, S., Rhymes, J. K. (eds) Papers on regional statistical studies, Toronto: Toronto University Press, pp. 199-223.

References

Hepple, L. W. 1976 A maximum likelihood model for econometric estimation with spatial series, in Masser, I (ed) Theory and practice in regional science, London: Pion, pp. 90-104.

Examples

if (requireNamespace("spdep", quietly = TRUE)) {
  library(spdep)
  data(used.cars)
  moran.test(used.cars$price.1960, nb2listw(usa48.nb))
  moran.plot(used.cars$price.1960, nb2listw(usa48.nb),
           labels=rownames(used.cars))
  uc.lm <- lm(price.1960 ~ tax.charges, data=used.cars)
  summary(uc.lm)

  lm.morantest(uc.lm, nb2listw(usa48.nb))
  lm.morantest.sad(uc.lm, nb2listw(usa48.nb))
  lm.LMtests(uc.lm, nb2listw(usa48.nb))
  #if (requireNamespace("spatialreg", quietly = TRUE)) {
  #  library(spatialreg)
  #  uc.err <- errorsarlm(price.1960 ~ tax.charges, data=used.cars,
  #                     nb2listw(usa48.nb), tol.solve=1.0e-13, 
  #                     control=list(tol.opt=.Machine$double.eps^0.3))
  #  summary(uc.err)
  #  uc.lag <- lagsarlm(price.1960 ~ tax.charges, data=used.cars,
  #                   nb2listw(usa48.nb), tol.solve=1.0e-13, 
  #                   control=list(tol.opt=.Machine$double.eps^0.3))
  #  summary(uc.lag)
  #  uc.lag1 <- lagsarlm(price.1960 ~ 1, data=used.cars,
  #                    nb2listw(usa48.nb), tol.solve=1.0e-13, 
  #                    control=list(tol.opt=.Machine$double.eps^0.3))
  #  summary(uc.lag1)
  #  uc.err1 <- errorsarlm(price.1960 ~ 1, data=used.cars,
  #                      nb2listw(usa48.nb), tol.solve=1.0e-13, 
  #                      control=list(tol.opt=.Machine$double.eps^0.3))
  #  summary(uc.err1)
  #}
}

#> 
#> 	Lagrange multiplier diagnostics for spatial dependence
#> 
#> data:  
#> model: lm(formula = price.1960 ~ tax.charges, data = used.cars)
#> weights: nb2listw(usa48.nb)
#> 
#> LMErr = 31.793, df = 1, p-value = 1.715e-08
#>