Front ends for the SAC model \(y = \rho W y + X\beta + u\),
\(u = \lambda W u + e\); R arm of the Python modules
morie.fn.sac*. Sacdet, Sacjac: \(\log|I - \rho W|
+ \log|I - \lambda W|\). Sacconv: are both parameters inside
\((1/e_{min}, 1/e_{max})\)? Saclrt: likelihood ratio against the
nested SAR or SEM. Sacwald: joint Wald test \(b'V^{-1}b\).
Sacres: Moran's I of residuals. Sacimp: LeSage-Pace
impacts (those of the lag part; \(\lambda\) does not enter).
Sacrob: GS2SLS with the HC sandwich covariance
(spatialreg::gstsls(robust = TRUE)). Sacvar: inverse
analytic information matrix of \((\beta, \rho, \lambda, \sigma^2)\).
Sacboot: residual-bootstrap percentile interval for \(\rho\).
Usage
Sacdet(W, rho, lam)
Sacjac(W, rho, lam)
Sacconv(W, rho, lam)
Saclrt(ll_sac, ll_sar, df = 1)
Sacwald(params, vcov)
Sacres(resid, W, X = NULL)
Sacimp(coef, rho, lam, W)
Sacrob(y, X, W, robust = "HC0")
Sacvar(X, W, rho, lam, sigma2, beta)
Sacboot(y, X, W, B = 99, seed = 0, level = 0.95)Arguments
- W
Spatial weights matrix.
- rho
Spatial lag parameter.
- lam
Spatial error parameter.
- ll_sac, ll_sar
Log-likelihoods of the SAC and the nested model.
- df
Degrees of freedom.
- params
Estimates tested jointly against zero.
- vcov
Their covariance matrix.
- resid
Model residuals.
- X
Design matrix including any intercept column (optional for
Sacres).- coef
Slopes without intercept.
- y
Response.
- robust
"HC0"or"HC1".- sigma2
Error variance.
- beta
Regression coefficients including the intercept.
- B
Bootstrap replicates.
- seed
Philox seed.
- level
Confidence level.
Value
A list whose statistic is the headline value, with the
components of the Python result's extra.
References
Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer, Dordrecht.
Lee, L.-F. (2004). Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models. Econometrica 72, 1899-1925.
Kelejian, H. H. and Prucha, I. R. (2010). Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Econometrics 157, 53-67.
LeSage, J. and Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press, Boca Raton.