HSAR (version 0.3.6)

sar: SAR model estimation

Description

A Bayesian MCMC approach for estimation of the SAR model (spatial lag model) of the form: $$Y = \rho W Y + X \beta + \epsilon$$ where Y is an Nx1 vector of the outcome variable, X is an NxK matrix of the independent variables and W is the weight matrix.

Usage

sar( X, y, W, burnin=5000, Nsim=10000 )

Arguments

X

matrix of independent variables

y

vector of outcome variable values

W

spatial weight matrix

burnin

Number of samples before start collecting points

Nsim

Total number of samples in MC

Value

• a list with
• Mbetas
  a vector with the mean values of the vector of the regression coefficients estimated
• SDbetas
  a vector with the standard deviation of the vector of the regression coefficients estimated
• Mrho
  Mean values of the strength of the spatial interaction rho
• SDrho
  Standard deviation of rho
• Msigma2e
  Mean value of $\sigma^{2}_{e}$
• SDsigma2e
  Standard deviation of $\sigma^{2}_{e}$
• DIC
  deviance information criterion (DIC)
• pd
  effective number of parameters
• Log_Likelihood
  Log likelihood
• R_Squared
  pseudo R squared
• impact_direct
  Direct effect
• impact_idirect
  Indirect effect
• impact_total
  Total effect

References

Anselin, L. 1988 Spatial econometrics: methods and models.(Dordrecht: Kluwer); Dong, D. and Harris, R. 2014. Spatial Autoregressive Models for Geographically Hierarchical Data Structures. Geographical Analysis, 1-19.

See Also

hsar

Examples

data(land_prices)
result <- sar( X, y, W, 50, 100)
result$Mrho