help: sptobitmstar dialog:

Title

sptobitmstar: Tobit (m-STAR) Multiparametric Spatio Temporal AutoRegressive Regression

(Spatial Lag Cross Sections Models)

Table of Contents

Syntax

Description

Options

Saved Results

References

*** Examples

Authors

Syntax

sptobitmstar depvar indepvars [weight] , wmfile(weight_file) ll(#)

[nwmat(#) dist(norm|weib) mfx(lin, log) stand inv inv2 tolog nolog robust coll zero

noconstant predict(new_var) resid(new_var) iter(#) tech(name)

level(#) vce(vcetype) maximize other maximization options ]

Description

sptobitmstar estimates Tobit Spatial econometric regression (MSTAR) "Multiparametric Spatio Temporal AutoRegressive Regression" for Spatial Lag Cross Sections Models.

sptobitmstar can generate:

- Binary / Standardized Weight Matrix.
- Inverse / Inverse Squared Standardized Weight Matrix.
- Binary / Standardized / Inverse Eigenvalues Variable.

sptobitmstar 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 4 ways:

1- (Buse 1973) R2.

2- Raw Moments R2.

3- squared correlation between predicted (Yh) and observed dependent variable (Y).

4- Ratio of variance between predicted (Yh) and observed dependent variable (Y).

- Adjusted R2: R2_a=1-(1-R2)*(N-1)/(N-K-1).

- F-Test=R2/(1-R2)*(N-K-1)/(K).

*** Important Notes:
sptobitmstar generates some variables names with prefix:
w1x_ , w2x_ , w3x_ , w4x_ , w1y_ , w2y_ , mstar_ , spat_
So, you must avoid to include variables names with thes prefixes

Options

wmfile(weight_file) Open CROSS SECTION weight matrix file.

Spatial Cross Sections Weight Matrix file must be:
1- Square Matrix [NxN]
2- Symmetric Matrix (Optional)

Spatial Weight Matrix has two types: Standardized and binary weight matrix.

stand Use Standardized Weight Matrix, (each row sum equals 1)
Default is Binary spatial Weight Matrix which each element is 0 or 1

inv Use Inverse Standardized Weight Matrix (1/W)

inv2 Use Inverse Squared Standardized Weight Matrix (1/W^2)

sptobitmstar is used with more than Weight Matrix: (Border, Language, Currency, Trade...)

zero convert missing values observations to Zero

nwmat(1,2,3,4) number of Rho's matrixes to be used

coll keep collinear variables; default is removing collinear variables.

noconstant Exclude Constant Term from Equation

nolog suppress iteration of the log likelihood

ll(#) value of minimum left-censoring dependent variable with (tobit).

mfx(lin, log) functional form: Linear model (lin), or Log-Log model (log),
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).
- mfx(log) and tolog options must be combined, to transform linear variables to log form.

mfx(lin, log) can calculate:

- Total Marginal Effects and Elasticities.
- Direct Marginal Effects and Elasticities.
- InDirect Marginal Effects and Elasticities.

tolog Convert dependent and independent variables
to LOG Form in the memory for Log-Log regression.
tolog Transforms depvar and indepvars
to Log Form without lost the original data variables

dist(norm, weib) Distribution of error term:

1- dist(norm) Normal distribution; default.

3- dist(weib) Weibull distribution.

dist option is used to remedy non normality problem, when the error term has non normality distribution.

dist(weib) can be used.

dist(norm) is the default distribution.

predict(new_variable) Predicted values variable

resid(new_variable) Residuals values variable computed as Ue=Y-Yh

robust Huber-White standard errors

tech(name) technique algorithm for maximization of the log likelihood function LLF
tech(nr) Newton-Raphson (NR) algorithm; default
tech(bhhh) Berndt-Hall-Hall-Hausman (BHHH) algorithm
tech(dfp) Davidon-Fletcher-Powell (DFP) algorithm
tech(bfgs) Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm

iter(#) maximum iterations; default is 100
if iter(#) is reached (100), this means convergence not achieved yet,
so you can use another technique algorithm to converge LLF function
or exceed number of maximum iterations more than 100.

vce(vcetype) ols, robust, cluster, bootstrap, jackknife, hc2, hc3

level(#) confidence intervals level; default is level(95)

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.

Saved Results

sptobitmstar saves the following results in e():

Scalars

e(N) number of observations
e(r2bu) R-squared (Buse 1973)
e(r2bu_a) R-squared Adj (Buse 1973)
e(r2raw) Raw Moments R2
e(r2raw_a) Raw Moments R2 Adj
e(f) F-test
e(fp) F-test P-Value
e(wald) Wald-test
e(waldp) Wald-test P-Value
e(r2h) R2 Between Predicted (Yh) and Observed DepVar (Y)
e(r2h_a) Adjusted r2h
e(fh) F-test due to r2h
e(fhp) F-test due to r2h P-Value
e(r2v) R2 Variance Ratio Between Predicted (Yh) and Observed DepVar (Y)
e(r2v_a) Adjusted r2v
e(fv) F-test due to r2v
e(fvp) F-test due to r2v P-Value
e(sig) Root MSE (Sigma)
e(llf) Log Likelihood Function

Matrixes
e(b) coefficient vector
e(V) variance-covariance matrix of the estimators

e(mfxlinb) Beta, Total, Direct, and InDirect Marginal Effect (in Lin Form)
e(mfxline) Beta, Total, Direct, and InDirect Elasticity (in Lin Form)

e(mfxlogb) Beta, Total, Direct, and InDirect Marginal Effect (in Log Form)
e(mfxloge) Beta, Total, Direct, and InDirect Elasticity (in Log Form)

Functions
e(sample) marks estimation sample

References

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.

Anselin, L. & Florax RJ. (1995)
"New Directions in Spatial Econometrics: Introduction. In New Directions in Spatial Econometrics",
Anselin L, Florax RJ (eds). Berlin, Germany: Springer-Verlag.

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

Note 1: you can use: spweight, spweightcs, spweightxt to create Spatial Weight Matrix.
Note 2: Remember, your spatial weight matrix must be:
*** 1-Cross Section Dimention 2- Square Matrix 3- Symmetric Matrix
Note 3: You can use the dialog box for .

clear all

sysuse sptobitmstar.dta, clear

* Tobit (m-STAR) Multiparametric Spatio Temporal AutoRegressive Regression
* (m-STAR) Lag Model

*** YOU MUST HAVE DIFFERENT Weighted Matrixes Files:

* (1) *** Normal Distribution
sptobitmstar ys x1 x2, wmfile(SPWmcs1) nwmat(1) mfx(lin) dist(norm) ll(3)
sptobitmstar ys x1 x2, wmfile(SPWmcs1) nwmat(1) mfx(lin) dist(norm) ll(0)
sptobitmstar ys x1 x2, wmfile(SPWmcs2) nwmat(2) mfx(lin) dist(norm) ll(0)
sptobitmstar ys x1 x2, wmfile(SPWmcs3) nwmat(3) mfx(lin) dist(norm) ll(0)
sptobitmstar ys x1 x2, wmfile(SPWmcs4) nwmat(4) mfx(lin) dist(norm) ll(0)

* (2) *** Weibull Distribution
sptobitmstar ys x1 x2, wmfile(SPWmcs1) nwmat(1) mfx(lin) dist(weib) ll(3)
sptobitmstar ys x1 x2, wmfile(SPWmcs1) nwmat(1) mfx(lin) dist(weib) ll(0)
sptobitmstar ys x1 x2, wmfile(SPWmcs2) nwmat(2) mfx(lin) dist(weib) ll(0)
sptobitmstar ys x1 x2, wmfile(SPWmcs3) nwmat(3) mfx(lin) dist(weib) ll(0)
sptobitmstar ys x1 x2, wmfile(SPWmcs4) nwmat(4) mfx(lin) dist(weib) ll(0)

. 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

Authors

- Emad Abd Elmessih Shehata
Professor (PhD Economics)
Agricultural Research Center - Agricultural Economics Research Institute - Egypt
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

- Sahra Khaleel A. Mickaiel
Professor (PhD Economics)
Cairo University - Faculty of Agriculture - Department of Economics - Egypt
Email: sahra_atta@hotmail.com
WebPage: http://sahraecon.110mb.com/stata.htm
WebPage at IDEAS: http://ideas.repec.org/f/pmi520.html
WebPage at EconPapers: http://econpapers.repec.org/RAS/pmi520.htm

SPTOBITMSTAR Citation

Shehata, Emad Abd Elmessih & Sahra Khaleel A. Mickaiel (2013)

SPTOBITMSTAR: "Tobit (m-STAR) Spatial Multiparametric Spatio Temporal AutoRegressive Regression: Spatial Lag Cross Sections Models"

Online Help:

*** Spatial Econometrics Regression Models:

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*** (1) Spatial Panel Data Regression Models:
spregxt Spatial Panel Regression Econometric Models: Stata Module Toolkit
gs2slsxt Generalized Spatial Panel 2SLS Regression
gs2slsarxt Generalized Spatial Panel Autoregressive 2SLS Regression
spglsxt Spatial Panel Autoregressive Generalized Least Squares Regression
spgmmxt Spatial Panel Autoregressive Generalized Method of Moments Regression
spmstarxt (m-STAR) Spatial Lag Panel Models
spmstardxt (m-STAR) Spatial Durbin Panel Models
spmstardhxt (m-STAR) Spatial Durbin Multiplicative Heteroscedasticity Panel Models
spmstarhxt (m-STAR) Spatial Lag Multiplicative Heteroscedasticity Panel Models
spregdhp Spatial Panel Han-Philips Linear Dynamic Regression: Lag & Durbin Models
spregdpd Spatial Panel Arellano-Bond Linear Dynamic Regression: Lag & Durbin Models
spregfext Spatial Panel Fixed Effects Regression: Lag & Durbin Models
spregrext Spatial Panel Random Effects Regression: Lag & Durbin Models
spregsacxt MLE Spatial AutoCorrelation Panel Regression (SAC)
spregsarxt MLE Spatial Lag Panel Regression (SAR)
spregsdmxt MLE Spatial Durbin Panel Regression (SDM)
spregsemxt MLE Spatial Error Panel Regression (SEM)
--------------------------------------------------------------------------------
*** (2) Spatial Cross Section Regression Models:
spregcs Spatial Cross Section Regression Econometric Models: Stata Module Toolkit
gs2sls Generalized Spatial 2SLS Cross Sections Regression
gs2slsar Generalized Spatial Autoregressive 2SLS Cross Sections Regression
gs3sls Generalized Spatial Autoregressive 3SLS Regression
gs3slsar Generalized Spatial Autoregressive 3SLS Cross Sections Regression
gsp3sls Generalized Spatial 3SLS Cross Sections Regression
spautoreg Spatial Cross Section Regression Models
spgmm Spatial Autoregressive GMM Cross Sections Regression
spmstar (m-STAR) Spatial Lag Cross Sections Models
spmstard (m-STAR) Spatial Durbin Cross Sections Models
spmstardh (m-STAR) Spatial Durbin Multiplicative Heteroscedasticity Cross Sections Models
spmstarh (m-STAR) Spatial Lag Multiplicative Heteroscedasticity Cross Sections Models
spregsac MLE Spatial AutoCorrelation Cross Sections Regression (SAC)
spregsar MLE Spatial Lag Cross Sections Regression (SAR)
spregsdm MLE Spatial Durbin Cross Sections Regression (SDM)
spregsem MLE Spatial Error Cross Sections Regression (SEM)
--------------------------------------------------------------------------------
*** (3) Tobit Spatial Regression Models:

*** (3-1) Tobit Spatial Panel Data Regression Models:
sptobitgmmxt Tobit Spatial GMM Panel Regression
sptobitmstarxt Tobit (m-STAR) Spatial Lag Panel Models
sptobitmstardxt Tobit (m-STAR) Spatial Durbin Panel Models
sptobitmstardhxt Tobit (m-STAR) Spatial Durbin Multiplicative Heteroscedasticity Panel Models
sptobitmstarhxt Tobit (m-STAR) Spatial Lag Multiplicative Heteroscedasticity Panel Models
sptobitsacxt Tobit MLE Spatial AutoCorrelation (SAC) Panel Regression
sptobitsarxt Tobit MLE Spatial Lag Panel Regression
sptobitsdmxt Tobit MLE Spatial Panel Durbin Regression
sptobitsemxt Tobit MLE Spatial Error Panel Regression
spxttobit Tobit Spatial Panel Autoregressive GLS Regression
--------------------------------------------------------------
*** (3-2) Tobit Spatial Cross Section Regression Models:
sptobitgmm Tobit Spatial GMM Cross Sections Regression
sptobitmstar Tobit (m-STAR) Spatial Lag Cross Sections Models
sptobitmstard Tobit (m-STAR) Spatial Durbin Cross Sections Models
sptobitmstardh Tobit (m-STAR) Spatial Durbin Multiplicative Heteroscedasticity Cross Sections
sptobitmstarh Tobit (m-STAR) Spatial Lag Multiplicative Heteroscedasticity Cross Sections
sptobitsac Tobit MLE AutoCorrelation (SAC) Cross Sections Regression
sptobitsar Tobit MLE Spatial Lag Cross Sections Regression
sptobitsdm Tobit MLE Spatial Durbin Cross Sections Regression
sptobitsem Tobit MLE Spatial Error Cross Sections Regression
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*** (4) Spatial Weight Matrix:
spcs2xt Convert Cross Section to Panel Spatial Weight Matrix
spweight Cross Section and Panel Spatial Weight Matrix
spweightcs Cross Section Spatial Weight Matrix
spweightxt Panel Spatial Weight Matrix
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