+-------+ ----+ Title +------------------------------------------------------------
spmstar: (m-STAR) Spatial Multiparametric Spatio Temporal AutoRegressive Regres > sion
+-------------------+ ----+ Table of Contents +------------------------------------------------
Syntax Options Other Options Description Saved Results References
*** Examples
Author
+--------+ ----+ Syntax +-----------------------------------------------------------
spmstar depvar indepvars [if] [in] [weight], wmfile(weight_file) wmat(weight_matrix_name_W) eigw(eig_var_name_eW) nwmat(#) [ stand inrho(real 0) predict(new_var) resid(new_var) nolog robust noconstant level(#) vce(vcetype) ] maximize specify other maximization options constraint apply specified linear constraints
+---------+ ----+ Options +----------------------------------------------------------
options Description -------------------------------------------------------------------------
* wmfile(weight_file) weight matrix file name
* wmat(weight_matrix_name) name of the new spatial weight matrix to be used from importing wmfile(), it has two types; row-standardized, and binary weight matrix.
* eigw(eig_var_name) new eigenvalues variable name
* nwmat(1, 2, 3, 4) number of Rho's matrixes to be used with: model(mstar) , that can use more than Weight Matrix: (Border, Language, Currency, Trade...)
+---------------+ ----+ Other Options +----------------------------------------------------
options Description -------------------------------------------------------------------------
stand new row-standardized weight matrix within each row sum equals 1. Default is Binary spatial weight matrix which each element is 0 or 1
inrho(real 0) set initial value for rho. Default is 0
predict(new_variable) predicted values variable
resid(new_variable) residuals values variable
nolog suppress iteration of the log likelihood.
robust Use Huber-White standard errors.
noconstant Exclude Constant Term from Equation.
level(#) confidence intervals level. Default is level(95)
vce(vcetype) vcetype may be ols, robust, cluster clustvar, bootstrap, jackknife, hc2, or hc3
+-------------+ ----+ Description +------------------------------------------------------
spmstar estimate Spatial econometric regression (MSTAR) "Multiparametric Spatio Temporal AutoRegressive Regression" models for Cross Section data.
spmstar can generate: - Binary Weight Matrix. - Binary Eigenvalues Variable.
- Row-Standardized Weight Matrix. - Row-Standardized Eigenvalues Variable.
spmstar predicted values are obtained from conditional expectation expression.
Yh = E(y|x) = inv(I-Rho*W) * X*Beta
R2, R2 Adjusted, and F-Test, are obtained from two ways: 1- squared correlation between predicted (Yh) and observed dependent variable (Y). 2- Ratio of variance between predicted (Yh) and observed dependent variable (Y). - R2 Adjusted: R2_a=1-(1-R2)*(N-1)/(N-K-1). - F-Test=R2/(1-R2)*(N-K-1)/(K).
Other maximization_options allows the user to specify other maximization options (e.g., difficult, trace, iterate(#), constraint(#), etc.). However, you should rarely have to specify them, though they may be helpful if parameters approach boundary values.
+---------------+ ----+ Saved Results +----------------------------------------------------
spmstar saves the following results in e():
Scalars e(chi2) chi-squared e(fth) F-test due to r2h e(ftv) F-test due to r2v e(ic) number of iterations e(k) number of parameters e(ll) log likelihood e(ll_0) log likelihood for OLS e(N) number of observations e(p) significance of model of test e(p_wald) p-value for Wald test e(r2_a) Adjusted R-squared e(r2c) Centered R-squared, 1-rss/yyc e(r2h) R2 between predicted and observed depvar e(r2h_a) adjusted r2h e(r2u) Uncentered R-squared, 1-rss/yy e(r2v) R2 variance ratio between predicted and observed depvar e(r2v_a) adjusted r2v e(rank) rank of e(V)
Matrixes e(b) coefficient vector e(V) variance-covariance matrix of the estimators
Functions e(sample) marks estimation sample
+------------+ ----+ References +-------------------------------------------------------
Anselin, L., Kelejian, H. H. (1997) "Testing for Spatial Error Autocorrelation in the Presence of Endogenous Regressors", International Regional Science Review, (20); 153-182.
Anselin, L. (2001) "Spatial Econometrics", In Baltagi, B. (Ed).: A Companion to Theoretical Econometrics Basil Blackwell: Oxford, UK.
Anselin, L. (2007) "Spatial Econometrics", In T. C. Mills and K. Patterson (Eds).: Palgrave Handbook of Econometrics. Vol 1, Econometric Theory. New York: Palgrave MacMillan.
Hays, Jude C., Aya Kachi & Robert J. Franzese, Jr (2010) "A Spatial Model Incorporating Dynamic, Endogenous Network Interdependence: A Political Science Application", Statistical Methodology 7(3); 406-428.
James LeSage and R. Kelly Pace (2009) "Introduction to Spatial Econometrics", Publisher: Chapman & Hall/CRC.
+----------+ ----+ Examples +---------------------------------------------------------
* (m-STAR) Multiparametric Spatio Temporal AutoRegressive Regression
*** YOU MUST HAVE DIFFERENT Weighted Matrixes:
clear all
sysuse spmstar.dta, clear
spmstar y x1 x2 , wmfile(SPW1) wmat(W1) eigw(eW1) nwmat(1)
spmstar y x1 x2 , wmfile(SPW2) wmat(W2) eigw(eW2) nwmat(2)
spmstar y x1 x2 , wmfile(SPW3) wmat(W3) eigw(eW3) nwmat(3)
spmstar y x1 x2 , wmfile(SPW4) wmat(W4) eigw(eW4) nwmat(4)
+--------+ ----+ Author +-----------------------------------------------------------
Emad Abd Elmessih Shehata Assistant Professor Agricultural Research Center - Agricultural Economics Research Institute - Eg > ypt Email: emadstat@hotmail.com WebPage: http://emadstat.110mb.com/stata.htm WebPage at IDEAS: http://ideas.repec.org/f/psh494.html WebPage at EconPapers: http://econpapers.repec.org/RAS/psh494.htm
+------------------+ ----+ spmstar Citation +-------------------------------------------------
Shehata, Emad Abd Elmessih (2011) SPMSTAR: "Stata Module to Estimate (m-STAR) Spatial Multiparametric Spatio Temporal AutoRegressive Regression"
Online Help:
spregcs, spregxt, spautoreg, spweight, gs3sls, gs2slsxt, spmstar, spweightcs, spweightxt, spcs2xt (if installed).