{smcl} {hline} {cmd:help: {helpb sptobitmstar}}{space 50} {cmd:dialog:} {bf:{dialog sptobitmstar}} {hline} {bf:{err:{dlgtab:Title}}} {bf:sptobitmstar: Tobit (m-STAR) Multiparametric Spatio Temporal AutoRegressive Regression} {p 9 1 1}{bf:(Spatial Lag Cross Sections Models)}{p_end} {marker 00}{bf:{err:{dlgtab:Table of Contents}}} {p 4 8 2} {p 5}{helpb sptobitmstar##01:Syntax}{p_end} {p 5}{helpb sptobitmstar##02:Description}{p_end} {p 5}{helpb sptobitmstar##03:Options}{p_end} {p 5}{helpb sptobitmstar##04:Saved Results}{p_end} {p 5}{helpb sptobitmstar##05:References}{p_end} {p 1}*** {helpb sptobitmstar##06:Examples}{p_end} {p 5}{helpb sptobitmstar##07:Authors}{p_end} {p2colreset}{...} {marker 01}{bf:{err:{dlgtab:Syntax}}} {p 5 5 6} {opt sptobitmstar} {depvar} {indepvars} {weight} , {opt wmf:ile(weight_file)} {opt ll(#)}{p_end} {p 3 5 6} {err: [}{opt nw:mat(#)} {opt dist(norm|weib)} {opt mfx(lin, log)} {opt stand inv inv2 tolog nolog robust coll zero}{p_end} {p 5 5 6} {opt nocons:tant} {opt pred:ict(new_var)} {opt res:id(new_var)} {opt iter(#)} {opt tech(name)} {p_end} {p 5 5 6} {opt l:evel(#)} {opth vce(vcetype)} {helpb maximize} {it:other maximization options} {err:]}{p_end} {p2colreset}{...} {marker 02}{bf:{err:{dlgtab:Description}}} {p 2 2 2} {cmd:sptobitmstar} estimates Tobit Spatial econometric regression (MSTAR) "Multiparametric Spatio Temporal AutoRegressive Regression" for Spatial Lag Cross Sections Models.{p_end} {p 2 4 2}{cmd:sptobitmstar} can generate:{p_end} {cmd:- Binary / Standardized Weight Matrix.} {cmd:- Inverse / Inverse Squared Standardized Weight Matrix.} {cmd:- Binary / Standardized / Inverse Eigenvalues Variable.} {p 2 4 2} {cmd:sptobitmstar} predicted values are obtained from conditional expectation expression.{p_end} {pmore2}{bf:Yh = E(y|x) = inv(I-Rho*W) * X*Beta} {p 3 4 2} R2, R2 Adjusted, and F-Test, are obtained from 4 ways:{p_end} {p 5 4 2} 1- (Buse 1973) R2.{p_end} {p 5 4 2} 2- Raw Moments R2.{p_end} {p 5 4 2} 3- squared correlation between predicted (Yh) and observed dependent variable (Y).{p_end} {p 5 4 2} 4- Ratio of variance between predicted (Yh) and observed dependent variable (Y).{p_end} {p 5 4 2} - Adjusted R2: R2_a=1-(1-R2)*(N-1)/(N-K-1).{p_end} {p 5 4 2} - F-Test=R2/(1-R2)*(N-K-1)/(K).{p_end} {bf:{err:*** Important Notes:}} {cmd:sptobitmstar} generates some variables names with prefix: {cmd:w1x_ , w2x_ , w3x_ , w4x_ , w1y_ , w2y_ , mstar_ , spat_} {cmd:So, you must avoid to include variables names with thes prefixes} {p2colreset}{...} {marker 03}{bf:{err:{dlgtab:Options}}} {col 3}{opt wmf:ile(weight_file)}{col 20} Open CROSS SECTION weight matrix file. Spatial Cross Sections Weight Matrix file must be: 1- Square Matrix [NxN] 2- Symmetric Matrix (Optional) {col 3}Spatial Weight Matrix has two types: Standardized and binary weight matrix. {col 3}{opt stand}{col 20}Use Standardized Weight Matrix, (each row sum equals 1) {col 20}Default is Binary spatial Weight Matrix which each element is 0 or 1 {col 3}{opt inv}{col 20}Use Inverse Standardized Weight Matrix (1/W) {col 3}{opt inv2}{col 20}Use Inverse Squared Standardized Weight Matrix (1/W^2) {p 2 2 2}{bf:sptobitmstar} is used with more than Weight Matrix: (Border, Language, Currency, Trade...){p_end} {col 3}{opt zero}{col 20}convert missing values observations to Zero {col 3}{opt nw:mat(1,2,3,4)}{col 20}number of Rho's matrixes to be used {col 3}{opt coll}{col 20}keep collinear variables; default is removing collinear variables. {col 3}{opt nocons:tant}{col 20}Exclude Constant Term from Equation {col 3}{opt nolog}{col 20}suppress iteration of the log likelihood {col 3}{opt ll(#)}{col 20}value of minimum left-censoring dependent variable with {bf:({err:{it:tobit}})}. {col 3}{opt mfx(lin, log)}{col 20}functional form: Linear model {cmd:(lin)}, or Log-Log model {cmd:(log)}, {col 20}to compute Total, Direct, and InDirect Marginal Effects and Elasticities - In Linear model: marginal effects are the coefficients (Bm), and elasticities are (Es = Bm X/Y). - In Log-Log model: elasticities are the coefficients (Es), and the marginal effects are (Bm = Es Y/X). - {opt mfx(log)} and {opt tolog} options must be combined, to transform linear variables to log form. {p 8 4 2}{opt mfx(lin, log)} can calculate:{p_end} {cmd:- Total Marginal Effects and Elasticities.} {cmd:- Direct Marginal Effects and Elasticities.} {cmd:- InDirect Marginal Effects and Elasticities.} {col 3}{opt tolog}{col 20}Convert dependent and independent variables {col 20}to LOG Form in the memory for Log-Log regression. {col 20}{opt tolog} Transforms {depvar} and {indepvars} {col 20}to Log Form without lost the original data variables {synopt :{bf:dist({err:{it:norm, weib}})} Distribution of error term:}{p_end} {p 12 2 2}1- {bf:dist({err:{it:norm}})} Normal distribution; default.{p_end} {p 12 2 2}3- {bf:dist({err:{it:weib}})} Weibull distribution.{p_end} {p 10 10 1}{cmd:dist} option is used to remedy non normality problem, when the error term has non normality distribution.{p_end} {p 10 10 1} {opt dist(weib)} can be used.{p_end} {p 10 10 1}{bf:dist({err:{it:norm}})} is the default distribution.{p_end} {col 3}{opt pred:ict(new_variable)}{col 30}Predicted values variable {col 3}{opt res:id(new_variable)}{col 30}Residuals values variable computed as Ue=Y-Yh {col 3}{opt rob:ust}{col 20}Huber-White standard errors {col 3}{opt tech(name)}{col 20}technique algorithm for maximization of the log likelihood function LLF {col 8}{cmdab:tech(nr)}{col 20}Newton-Raphson (NR) algorithm; default {col 8}{cmdab:tech(bhhh)}{col 20}Berndt-Hall-Hall-Hausman (BHHH) algorithm {col 8}{cmdab:tech(dfp)}{col 20}Davidon-Fletcher-Powell (DFP) algorithm {col 8}{cmdab:tech(bfgs)}{col 20}Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm {col 3}{opt iter(#)}{col 20}maximum iterations; default is 100 {col 20} if {opt iter(#)} is reached (100), this means convergence not achieved yet, {col 20} so you can use another technique algorithm to converge LLF function {col 20} or exceed number of maximum iterations more than 100. {synopt :{opth vce(vcetype)} {opt ols}, {opt r:obust}, {opt cl:uster}, {opt boot:strap}, {opt jack:knife}, {opt hc2}, {opt hc3}}{p_end} {col 3}{opt level(#)}{col 20}confidence intervals level; default is level(95) {p 2 4 2}{help maximize:Other maximization_options} allows the user to specify other maximization options (e.g., difficult, trace, iterate(#), etc.). However, you should rarely have to specify them, though they may be helpful if parameters approach boundary values. {p2colreset}{...} {marker 04}{bf:{err:{dlgtab:Saved Results}}} {p 2 4 2 }{cmd:sptobitmstar} saves the following results in {cmd:e()}: Scalars {col 4}{cmd:e(N)}{col 20}number of observations {col 4}{cmd:e(r2bu)}{col 20}R-squared (Buse 1973) {col 4}{cmd:e(r2bu_a)}{col 20}R-squared Adj (Buse 1973) {col 4}{cmd:e(r2raw)}{col 20}Raw Moments R2 {col 4}{cmd:e(r2raw_a)}{col 20}Raw Moments R2 Adj {col 4}{cmd:e(f)}{col 20}F-test {col 4}{cmd:e(fp)}{col 20}F-test P-Value {col 4}{cmd:e(wald)}{col 20}Wald-test {col 4}{cmd:e(waldp)}{col 20}Wald-test P-Value {col 4}{cmd:e(r2h)}{col 20}R2 Between Predicted (Yh) and Observed DepVar (Y) {col 4}{cmd:e(r2h_a)}{col 20}Adjusted r2h {col 4}{cmd:e(fh)}{col 20}F-test due to r2h {col 4}{cmd:e(fhp)}{col 20}F-test due to r2h P-Value {col 4}{cmd:e(r2v)}{col 20}R2 Variance Ratio Between Predicted (Yh) and Observed DepVar (Y) {col 4}{cmd:e(r2v_a)}{col 20}Adjusted r2v {col 4}{cmd:e(fv)}{col 20}F-test due to r2v {col 4}{cmd:e(fvp)}{col 20}F-test due to r2v P-Value {col 4}{cmd:e(sig)}{col 20}Root MSE (Sigma) {col 4}{cmd:e(llf)}{col 20}Log Likelihood Function Matrixes {col 4}{cmd:e(b)}{col 20}coefficient vector {col 4}{cmd:e(V)}{col 20}variance-covariance matrix of the estimators {col 4}{cmd:e(mfxlinb)}{col 20}Beta, Total, Direct, and InDirect Marginal Effect{col 70}(in Lin Form) {col 4}{cmd:e(mfxline)}{col 20}Beta, Total, Direct, and InDirect Elasticity{col 70}(in Lin Form) {col 4}{cmd:e(mfxlogb)}{col 20}Beta, Total, Direct, and InDirect Marginal Effect{col 70}(in Log Form) {col 4}{cmd:e(mfxloge)}{col 20}Beta, Total, Direct, and InDirect Elasticity{col 70}(in Log Form) Functions {col 4}{cmd:e(sample)}{col 20}marks estimation sample {marker 05}{bf:{err:{dlgtab:References}}} {p 4 8 2}Anselin, L. (2001) {cmd: "Spatial Econometrics",} {it:In Baltagi, B. (Ed).: A Companion to Theoretical Econometrics Basil Blackwell: Oxford, UK}. {p 4 8 2}Anselin, L. (2007) {cmd: "Spatial Econometrics",} {it:In T. C. Mills and K. Patterson (Eds).: Palgrave Handbook of Econometrics. Vol 1, Econometric Theory. New York: Palgrave MacMillan}. {p 4 8 2}Anselin, L. & Florax RJ. (1995) {cmd: "New Directions in Spatial Econometrics: Introduction. In New Directions in Spatial Econometrics",} {it:Anselin L, Florax RJ (eds). Berlin, Germany: Springer-Verlag}. {p 4 8 2}Hays, Jude C., Aya Kachi & Robert J. Franzese, Jr (2010) {cmd: "A Spatial Model Incorporating Dynamic, Endogenous Network Interdependence: A Political Science Application",} {it:Statistical Methodology 7(3)}; 406-428. {p 4 8 2}James LeSage and R. Kelly Pace (2009) {cmd: "Introduction to Spatial Econometrics",} {it:Publisher: Chapman & Hall/CRC}. {p2colreset}{...} {marker 06}{bf:{err:{dlgtab:Examples}}} {bf:Note 1:} you can use: {helpb spweight}, {helpb spweightcs}, {helpb spweightxt} to create Spatial Weight Matrix. {bf:Note 2:} Remember, your spatial weight matrix must be: *** {bf:{err:1-Cross Section Dimention 2- Square Matrix 3- Symmetric Matrix}} {bf:Note 3:} You can use the dialog box for {dialog sptobitmstar}. {hline} {stata clear all} {stata sysuse sptobitmstar.dta, clear} {bf:{err:* Tobit (m-STAR) Multiparametric Spatio Temporal AutoRegressive Regression}} {bf:{bf:* (m-STAR) Lag Model}} *** {bf:YOU MUST HAVE DIFFERENT Weighted Matrixes Files:} {bf:* (1) *** Normal Distribution} {stata sptobitmstar ys x1 x2, wmfile(SPWmcs1) nwmat(1) mfx(lin) dist(norm) ll(3)} {stata sptobitmstar ys x1 x2, wmfile(SPWmcs1) nwmat(1) mfx(lin) dist(norm) ll(0)} {stata sptobitmstar ys x1 x2, wmfile(SPWmcs2) nwmat(2) mfx(lin) dist(norm) ll(0)} {stata sptobitmstar ys x1 x2, wmfile(SPWmcs3) nwmat(3) mfx(lin) dist(norm) ll(0)} {stata sptobitmstar ys x1 x2, wmfile(SPWmcs4) nwmat(4) mfx(lin) dist(norm) ll(0)} {hline} {bf:* (2) *** Weibull Distribution} {stata sptobitmstar ys x1 x2, wmfile(SPWmcs1) nwmat(1) mfx(lin) dist(weib) ll(3)} {stata sptobitmstar ys x1 x2, wmfile(SPWmcs1) nwmat(1) mfx(lin) dist(weib) ll(0)} {stata sptobitmstar ys x1 x2, wmfile(SPWmcs2) nwmat(2) mfx(lin) dist(weib) ll(0)} {stata sptobitmstar ys x1 x2, wmfile(SPWmcs3) nwmat(3) mfx(lin) dist(weib) ll(0)} {stata sptobitmstar ys x1 x2, wmfile(SPWmcs4) nwmat(4) mfx(lin) dist(weib) ll(0)} {hline} . clear all . sysuse sptobitmstar.dta, clear . sptobitmstar ys x1 x2 , wmfile(SPWmcs1) nwmat(1) dist(norm) mfx(lin) ll(0) ============================================================================== *** Binary (0/1) Weight Matrix: 49x49 (Non Normalized) ============================================================================== *** ys Lower Limit = 0 *** ys Left- Censored Observations = 9 *** ys Left-UnCensored Observations = 40 ------------------------------------------------------------ ============================================================================== * Tobit MLE Multiparametric Spatio Temporal AutoRegressive Regression * (m-STAR) Spatial Lag Normal Model (1 Weight Matrix) ============================================================================== ys = x1 + x2 ------------------------------------------------------------------------------ Sample Size = 49 Wald Test = 20.1639 | P-Value > Chi2(2) = 0.0000 F-Test = 10.0820 | P-Value > F(2 , 47) = 0.0002 (Buse 1973) R2 = 0.3048 | Raw Moments R2 = 0.7631 (Buse 1973) R2 Adj = 0.2900 | Raw Moments R2 Adj = 0.7581 Root MSE (Sigma) = 17.7544 | Log Likelihood Function = -152.3001 ------------------------------------------------------------------------------ - R2h= 0.3988 R2h Adj= 0.3860 F-Test = 15.26 P-Value > F(2 , 47) 0.0000 - R2v= 0.4104 R2v Adj= 0.3979 F-Test = 16.01 P-Value > F(2 , 47) 0.0000 ------------------------------------------------------------------------------ - Sum of Rho's = 0.0176958 Sum must be < 1 for Stability Condition ------------------------------------------------------------------------------ ys | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- ys | x1 | -.4184948 .119077 -3.51 0.000 -.6518815 -.1851081 x2 | -1.312812 .3373343 -3.89 0.000 -1.973975 -.6516494 _cons | 67.26161 6.227674 10.80 0.000 55.05559 79.46763 -------------+---------------------------------------------------------------- Rho1 | .0176958 .0268608 0.66 0.510 -.0349503 .070342 Sigma | 10.70508 1.184352 9.04 0.000 8.383789 13.02636 ------------------------------------------------------------------------------ Wald Test [Rho1=0]: 0.4340 P-Value > Chi2(1) 0.5100 Acceptable Range for Rho1: -0.3199 < Rho1 < 0.1633 ------------------------------------------------------------------------------ * Beta, Total, Direct, and InDirect (Model= ): Linear: Marginal Effect * +-------------------------------------------------------------------------------+ | Variable | Beta(B) | Total | Direct | InDirect | Mean | |--------------+------------+------------+------------+------------+------------| |ys | | | | | | | x1 | -0.4185 | -0.4178 | -0.3823 | -0.0355 | 38.4362 | | x2 | -1.3128 | -1.3108 | -1.1993 | -0.1115 | 14.3749 | +-------------------------------------------------------------------------------+ * Beta, Total, Direct, and InDirect (Model= ): Linear: Elasticity * +-------------------------------------------------------------------------------+ | Variable | Beta(Es) | Total | Direct | InDirect | Mean | |--------------+------------+------------+------------+------------+------------| | x1 | -0.5545 | -0.5537 | -0.5066 | -0.0471 | 38.4362 | | x2 | -0.6506 | -0.6496 | -0.5943 | -0.0552 | 14.3749 | +-------------------------------------------------------------------------------+ Mean of Dependent Variable = 29.0070 {p2colreset}{...} {marker 07}{bf:{err:{dlgtab:Authors}}} - {hi:Emad Abd Elmessih Shehata} {hi:Professor (PhD Economics)} {hi:Agricultural Research Center - Agricultural Economics Research Institute - Egypt} {hi:Email: {browse "mailto:emadstat@hotmail.com":emadstat@hotmail.com}} {hi:WebPage:{col 27}{browse "http://emadstat.110mb.com/stata.htm"}} {hi:WebPage at IDEAS:{col 27}{browse "http://ideas.repec.org/f/psh494.html"}} {hi:WebPage at EconPapers:{col 27}{browse "http://econpapers.repec.org/RAS/psh494.htm"}} - {hi:Sahra Khaleel A. Mickaiel} {hi:Professor (PhD Economics)} {hi:Cairo University - Faculty of Agriculture - Department of Economics - Egypt} {hi:Email: {browse "mailto:sahra_atta@hotmail.com":sahra_atta@hotmail.com}} {hi:WebPage:{col 27}{browse "http://sahraecon.110mb.com/stata.htm"}} {hi:WebPage at IDEAS:{col 27}{browse "http://ideas.repec.org/f/pmi520.html"}} {hi:WebPage at EconPapers:{col 27}{browse "http://econpapers.repec.org/RAS/pmi520.htm"}} {bf:{err:{dlgtab:SPTOBITMSTAR Citation}}} {p 1}{cmd:Shehata, Emad Abd Elmessih & Sahra Khaleel A. Mickaiel (2013)}{p_end} {p 1 10 1}{cmd:SPTOBITMSTAR: "Tobit (m-STAR) Spatial Multiparametric Spatio Temporal AutoRegressive Regression: Spatial Lag Cross Sections Models"}{p_end} {title:Online Help:} {bf:{err:*** Spatial Econometrics Regression Models:}} -------------------------------------------------------------------------------- {bf:{err:*** (1) Spatial Panel Data Regression Models:}} {helpb spregxt}{col 14}Spatial Panel Regression Econometric Models: {cmd:Stata Module Toolkit} {helpb gs2slsxt}{col 14}Generalized Spatial Panel 2SLS Regression {helpb gs2slsarxt}{col 14}Generalized Spatial Panel Autoregressive 2SLS Regression {helpb spglsxt}{col 14}Spatial Panel Autoregressive Generalized Least Squares Regression {helpb spgmmxt}{col 14}Spatial Panel Autoregressive Generalized Method of Moments Regression {helpb spmstarxt}{col 14}(m-STAR) Spatial Lag Panel Models {helpb spmstardxt}{col 14}(m-STAR) Spatial Durbin Panel Models {helpb spmstardhxt}{col 14}(m-STAR) Spatial Durbin Multiplicative Heteroscedasticity Panel Models {helpb spmstarhxt}{col 14}(m-STAR) Spatial Lag Multiplicative Heteroscedasticity Panel Models {helpb spregdhp}{col 14}Spatial Panel Han-Philips Linear Dynamic Regression: Lag & Durbin Models {helpb spregdpd}{col 14}Spatial Panel Arellano-Bond Linear Dynamic Regression: Lag & Durbin Models {helpb spregfext}{col 14}Spatial Panel Fixed Effects Regression: Lag & Durbin Models {helpb spregrext}{col 14}Spatial Panel Random Effects Regression: Lag & Durbin Models {helpb spregsacxt}{col 14}MLE Spatial AutoCorrelation Panel Regression (SAC) {helpb spregsarxt}{col 14}MLE Spatial Lag Panel Regression (SAR) {helpb spregsdmxt}{col 14}MLE Spatial Durbin Panel Regression (SDM) {helpb spregsemxt}{col 14}MLE Spatial Error Panel Regression (SEM) -------------------------------------------------------------------------------- {bf:{err:*** (2) Spatial Cross Section Regression Models:}} {helpb spregcs}{col 14}Spatial Cross Section Regression Econometric Models: {cmd:Stata Module Toolkit} {helpb gs2sls}{col 14}Generalized Spatial 2SLS Cross Sections Regression {helpb gs2slsar}{col 14}Generalized Spatial Autoregressive 2SLS Cross Sections Regression {helpb gs3sls}{col 14}Generalized Spatial Autoregressive 3SLS Regression {helpb gs3slsar}{col 14}Generalized Spatial Autoregressive 3SLS Cross Sections Regression {helpb gsp3sls}{col 14}Generalized Spatial 3SLS Cross Sections Regression {helpb spautoreg}{col 14}Spatial Cross Section Regression Models {helpb spgmm}{col 14}Spatial Autoregressive GMM Cross Sections Regression {helpb spmstar}{col 14}(m-STAR) Spatial Lag Cross Sections Models {helpb spmstard}{col 14}(m-STAR) Spatial Durbin Cross Sections Models {helpb spmstardh}{col 14}(m-STAR) Spatial Durbin Multiplicative Heteroscedasticity Cross Sections Models {helpb spmstarh}{col 14}(m-STAR) Spatial Lag Multiplicative Heteroscedasticity Cross Sections Models {helpb spregsac}{col 14}MLE Spatial AutoCorrelation Cross Sections Regression (SAC) {helpb spregsar}{col 14}MLE Spatial Lag Cross Sections Regression (SAR) {helpb spregsdm}{col 14}MLE Spatial Durbin Cross Sections Regression (SDM) {helpb spregsem}{col 14}MLE Spatial Error Cross Sections Regression (SEM) -------------------------------------------------------------------------------- {bf:{err:*** (3) Tobit Spatial Regression Models:}} {bf:*** (3-1) Tobit Spatial Panel Data Regression Models:} {helpb sptobitgmmxt}{col 14}Tobit Spatial GMM Panel Regression {helpb sptobitmstarxt}{col 14}Tobit (m-STAR) Spatial Lag Panel Models {helpb sptobitmstardxt}{col 14}Tobit (m-STAR) Spatial Durbin Panel Models {helpb sptobitmstardhxt}{col 14}Tobit (m-STAR) Spatial Durbin Multiplicative Heteroscedasticity Panel Models {helpb sptobitmstarhxt}{col 14}Tobit (m-STAR) Spatial Lag Multiplicative Heteroscedasticity Panel Models {helpb sptobitsacxt}{col 14}Tobit MLE Spatial AutoCorrelation (SAC) Panel Regression {helpb sptobitsarxt}{col 14}Tobit MLE Spatial Lag Panel Regression {helpb sptobitsdmxt}{col 14}Tobit MLE Spatial Panel Durbin Regression {helpb sptobitsemxt}{col 14}Tobit MLE Spatial Error Panel Regression {helpb spxttobit}{col 14}Tobit Spatial Panel Autoregressive GLS Regression -------------------------------------------------------------- {bf:*** (3-2) Tobit Spatial Cross Section Regression Models:} {helpb sptobitgmm}{col 14}Tobit Spatial GMM Cross Sections Regression {helpb sptobitmstar}{col 14}Tobit (m-STAR) Spatial Lag Cross Sections Models {helpb sptobitmstard}{col 14}Tobit (m-STAR) Spatial Durbin Cross Sections Models {helpb sptobitmstardh}{col 14}Tobit (m-STAR) Spatial Durbin Multiplicative Heteroscedasticity Cross Sections {helpb sptobitmstarh}{col 14}Tobit (m-STAR) Spatial Lag Multiplicative Heteroscedasticity Cross Sections {helpb sptobitsac}{col 14}Tobit MLE AutoCorrelation (SAC) Cross Sections Regression {helpb sptobitsar}{col 14}Tobit MLE Spatial Lag Cross Sections Regression {helpb sptobitsdm}{col 14}Tobit MLE Spatial Durbin Cross Sections Regression {helpb sptobitsem}{col 14}Tobit MLE Spatial Error Cross Sections Regression -------------------------------------------------------------------------------- {bf:{err:*** (4) Spatial Weight Matrix:}} {helpb spcs2xt}{col 14}Convert Cross Section to Panel Spatial Weight Matrix {helpb spweight}{col 14}Cross Section and Panel Spatial Weight Matrix {helpb spweightcs}{col 14}Cross Section Spatial Weight Matrix {helpb spweightxt}{col 14}Panel Spatial Weight Matrix -------------------------------------------------------------------------------- {psee} {p_end}