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help: spglsxt                                                   dialog: spglsxt
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+-------+ ----+ Title +------------------------------------------------------------

spglsxt: Spatial Panel Autoregressive Generalized Least Squares Regression

Syntax Description Options Spatial Panel Aautocorrelation Tests Model Selection Diagnostic Criteria Heteroscedasticity Tests Non Normality Tests Saved Results References

*** Examples

Acknowledgments Author

+--------+ ----+ Syntax +-----------------------------------------------------------

spglsxt depvar indepvars , nc(#) [ wmfile(weight_file) lmspac lmhet lmnorm diag tests dn coll zero tolog robust stand inv inv2 mfx(lin, log) gmm(#) noconstant predict(new_var) resid(new_var) level(#) vce(vcetype) ]

+-------------+ ----+ Description +------------------------------------------------------

spglsxt estimates Spatial Panel Autoregressive Generalized Least Squares Regre > ssion When panel data model with error components are both spatially and time-wise correlated. Generalized Method of Moments (GMM) that suggested in Kelejian-Prucha (1999), and Kapoor-Kelejian-Prucha (2007) is used in the estimation of (spglsxt)

spglsxt can generate: - Binary / Standardized Weight Matrix. - Inverse / Inverse Squared Standardized Weight Matrix. - Binary / Standardized / Inverse Eigenvalues Variable.

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: spglsxt 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 +----------------------------------------------------------

* nc(#) Number of Cross Sections Units Time series observations must be Balanced in each Cross Section

wmfile(weight_file) Open CROSS SECTION weight matrix file. spglsxt will convert automatically spatial cross section wei > ght matrix to spatial PANEL weight matrix.

Spatial Weight Matrix file must be: 1- [SxS] Cross Sections units Dimentions, and not Panel dimentions 2- Square Matrix 3- Symmetric Matrix (Optional)

Spatial Panel Weight Matrix has two types: Standardized and binary weight mat > rix.

stand Use Standardized Panel Weight Matrix, (each row sum equals 1 > ) Default is Binary spatial panel weight matrix which each ele > ment is 0 or 1

inv Use Inverse Standardized Weight Matrix (1/W)

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

zero convert missing values observations to Zero

dn Use (N) divisor instead of (N-K) for Degrees of Freedom (DF)

gmm(1, 2, 3) GMM Estimators for (spglsxt) 1- Initial GMM Model 2- Partial Weighted GMM Model 3- Full Weighted GMM Model

coll keep collinear variables; default is removing collinear vari > ables.

noconstant Exclude Constant Term from Equation

tests display ALL lmh, lmn, lmsp, diag tests

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.

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

predict(new_variable) Predicted values variable

resid(new_variable) Residuals values variable computed as Ue=Y-Yh ; that is known as combined residual: [Ue = > U_i + E_it] in xtreg models overall error component is computed as: [E_it] see: xtreg postestimation##predict

robust Huber-White standard errors

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

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

+--------------------------------------+ ----+ Spatial Panel Aautocorrelation Tests +-----------------------------

lmspac Spatial Panel Aautocorrelation Tests: * Ho: Error has No Spatial AutoCorrelation Ha: Error has Spatial AutoCorrelation - GLOBAL Moran MI Test - GLOBAL Geary GC Test - GLOBAL Getis-Ords GO Test - Moran MI Error Test - LM Error [SEM] (Burridge) Test - LM Error [SEM] (Robust) Test * Ho: Spatial Lagged Dependent Variable has No Spatial AutoCorrelation Ha: Spatial Lagged Dependent Variable has Spatial AutoCorrelation - LM Lag [SAR] (Anselin) Test - LM Lag [SAR] (Robust) Test * Ho: No General Spatial AutoCorrelation Ha: General Spatial AutoCorrelation - LM SAC (LMErr+LMLag_R) Test - LM SAC (LMLag+LMErr_R) Test

Definitions:

- Spatial autocorrelation: chock in one country affects neighboring countrie > s

- Spatial autocorrelation: is correlation of a variable with itself in space > .

- Spatial Lag Model: Y = BX + rWy + e ; e = lWe+u - Spatial Error Model: Y = BX + e ; e = lWe+u - Spatial Durbin Model: Y = BX + aWX* + rWy + e ; e = lWe+u - General Spatial Model: Y = BX + rWy + LW1y + e ; e = lW1e+u - General Spatial Model: Y = BX + rWy + LW1y + e ; e = lW1e+u

- General Spatial Model is used to deal with both types of spatial dependenc > e, namely Spatial Lag Dependence and Spatial Error Dependence

- Spatial Error Model is used to handle the spatial dependence due to the omitted variables or errors in measurement through the error term

- Spatial Autoregressive Model (SAR) is also known as Spatial Lag Model

- Positive spatial autocorrelation exists when high values correlate with high neighboring values or when low values correlate with low neighboring values

- Negative spatial autocorrelation exists when high values correlate with low neighboring values and vice versa.

- presence of positive spatial autocorrelation results in a loss of informat > ion, which is related to greater uncertainty, less precision, and larger standa > rd errors.

- Spatial autocorrelation coefficients (in contrast to their counterparts in > time) are not constrained by -1/+1. Their range depends on the choice of weights > matrix.

- Spatial dependence exists when the value associated with one location is dependent on those of other locations.

- Spatial heterogeneity exists when structural changes related to location exist in a dataset, it can result in non-constant error variance (heteroscedasticity) across areas, especially when scale-related measurement errors are present.

- Spatial regression models are statistical models that account for the presence of spatial effects, i.e., spatial autocorrelation (or more generally spatial dependence) and/or spatial heterogeneity.

- if LM test for spatial lag is more significant than LM test for spatial er > ror, and robust LM test for spatial lag is significant but robust LM test for spatial error is not, then the appropriate model is spatial lag model. Conversely, if LM test for spatial error is more significant than LM test for spatial lag and robust LM test for spatial error is significant but robust LM test for spatial lag is not, then the appropriate specificat > ion is spatial error model, [Anselin-Florax (1995)]. - robust versions of Spatial LM tests are considered only when standard versions (LM-Lag or LM-Error) are significant - General Spatial Model is used to deal with both types of spatial dependenc > e, namely spatial lag dependence and spatial error dependence - Spatial Error Model is used to handle spatial dependence due to omitted variables or errors in measurement through the error term - Spatial Autoregressive Model (SAR) is also known as Spatial Lag Model

+-------------------------------------------+ ----+ Panel Model Selection Diagnostic Criteria +------------------------

diag Spatial Panel Model Selection Diagnostic Criteria: - Log Likelihood Function LLF - Akaike Information Criterion (1974) AIC - Akaike Information Criterion (1973) Log AIC - Schwarz Criterion (1978) SC - Schwarz Criterion (1978) Log SC - Amemiya Prediction Criterion (1969) FPE - Hannan-Quinn Criterion (1979) HQ - Rice Criterion (1984) Rice - Shibata Criterion (1981) Shibata - Craven-Wahba Generalized Cross Validation (1979) GCV

+--------------------------------+ ----+ Panel Heteroscedasticity Tests +-----------------------------------

lmhet Spatial Panel Heteroscedasticity Tests: * Ho: Panel Homoscedasticity - Ha: Panel Heteroscedasticity - Engle LM ARCH Test AR(1) E2 =E2_1 - Hall-Pagan LM Test: E2 = Yh - Hall-Pagan LM Test: E2 = Yh2 - Hall-Pagan LM Test: E2 = LYh2 - Harvey LM Test: LogE2 = X - Wald Test: LogE2 = X - Glejser LM Test: |E| = X - Machado-Santos-Silva LM Test: Ev= Yh Yh2 - Machado-Santos-Silva LM Test: Ev= X - Breusch-Godfrey Test: E = E_1 X - White Test - Koenker(R2): E2 = X - White Test - B-P-G (SSR): E2 = X - White Test - Koenker(R2): E2 = X X2 - White Test - B-P-G (SSR): E2 = X X2 - White Test - Koenker(R2): E2 = X X2 XX - White Test - B-P-G (SSR): E2 = X X2 XX - Cook-Weisberg LM Test E = Yh - Cook-Weisberg LM Test E = X *** Single Variable Tests - Cook-Weisberg LM Test: E = xi - King LM Test: E = xi

*** Groupwise Panel Heteroscedasticity Tests * Ho: Panel Homoscedasticity - Ha: Panel Groupwise Heteroscedasticity - Lagrange Multiplier LM Test - Likelihood Ratio LR Test - Wald Test

+---------------------------+ ----+ Panel Non Normality Tests +----------------------------------------

lmnorm Spatial Panel Non Normality Tests: * Ho: Panel Normality - Ha: Panel Non Normality *** Non Normality Tests: - Jarque-Bera LM Test - White IM Test - Doornik-Hansen LM Test - Geary LM Test - Anderson-Darling Z Test - D'Agostino-Pearson LM Test *** Skewness Tests: - Srivastava LM Skewness Test - Small LM Skewness Test - Skewness Z Test - Skewness Coefficient - Standard Deviation *** Kurtosis Tests: - Srivastava Z Kurtosis Test - Small LM Kurtosis Test - Kurtosis Z Test - Kurtosis Coefficient - Standard Deviation *** Runs Tests: - Runs Test: - Standard Deviation Runs Sig(k) - Mean Runs E(k) - 95% Conf. Interval [E(k)+/- 1.96* Sig(k)]

+---------------+ ----+ Saved Results +----------------------------------------------------

Depending on the model estimated, spglsxt saves the following results in e():

Scalars

*** Spatial Panel Aautocorrelation Tests: e(mig) GLOBAL Moran MI Test e(migp) GLOBAL Moran MI Test P-Value e(gcg) GLOBAL Geary GC Test e(gcgp) GLOBAL Geary GC Test P-Value e(gog) GLOBAL Getis-Ords Test GO e(gogp) GLOBAL Getis-Ords GO Test P-Value e(mi1) Moran MI Error Test e(mi1p) Moran MI Error Test P-Value e(lmerr) LM Error (Burridge) Test e(lmerrp) LM Error (Burridge) Test P-Value e(lmerrr) LM Error (Robust) Test e(lmerrrp) LM Error (Robust) Test P-Value e(lmlag) LM Lag (Anselin) Test e(lmlagp) LM Lag (Anselin) Test P-Value e(lmlagr) LM Lag (Robust) Test e(lmlagrp) LM Lag (Robust) Test P-Value e(lmsac1) LM SAC (LMLag+LMErr_R) Test e(lmsac1p) LM SAC (LMLag+LMErr_R) Test P-Value e(lmsac2) LM SAC (LMErr+LMLag_R) Test e(lmsac2p) LM SAC (LMErr+LMLag_R) Test P-Value

*** Spatial Panel Model Selection Diagnostic Criteria:

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 LLF e(aic) Akaike Information Criterion (1974) AIC e(laic) Akaike Information Criterion (1973) Log AIC e(sc) Schwarz Criterion (1978) SC e(lsc) Schwarz Criterion (1978) Log SC e(fpe) Amemiya Prediction Criterion (1969) FPE e(hq) Hannan-Quinn Criterion (1979) HQ e(rice) Rice Criterion (1984) Rice e(shibata) Shibata Criterion (1981) Shibata e(gcv) Craven-Wahba Generalized Cross Validation (1979) GCV

*** Spatial Panel Heteroscedasticity Tests: e(lmharch) Engle LM ARCH Test AR(1) e(lmharchp) Engle LM ARCH Test AR(1) P-Value e(lmhhp1) Hall-Pagan LM Test E2 = Yh e(lmhhp1p) Hall-Pagan LM Test E2 = Yh P-Value e(lmhhp2) Hall-Pagan LM Test E2 = Yh2 e(lmhhp2p) Hall-Pagan LM Test E2 = Yh2 P-Value e(lmhhp3) Hall-Pagan LM Test E2 = Yh3 e(lmhhp3p) Hall-Pagan LM Test E2 = Yh3 P-Value e(lmhw01) White Test - Koenker(R2) E2 = X e(lmhw01p) White Test - Koenker(R2) E2 = X P-Value e(lmhw02) White Test - B-P-G (SSR) E2 = X e(lmhw02p) White Test - B-P-G (SSR) E2 = X P-Value e(lmhw11) White Test - Koenker(R2) E2 = X X2 e(lmhw11p) White Test - Koenker(R2) E2 = X X2 P-Value e(lmhw12) White Test - B-P-G (SSR) E2 = X X2 e(lmhw12p) White Test - B-P-G (SSR) E2 = X X2 P-Value e(lmhw21) White Test - Koenker(R2) E2 = X X2 XX e(lmhw21p) White Test - Koenker(R2) E2 = X X2 XX P-Value e(lmhw22) White Test - B-P-G (SSR) E2 = X X2 XX e(lmhw22p) White Test - B-P-G (SSR) E2 = X X2 XX P-Value e(lmhharv) Harvey LM Test e(lmhharvp) Harvey LM Test P-Value e(lmhwald) Wald Test e(lmhwaldp) Wald Test P-Value e(lmhgl) Glejser LM Test e(lmhglp) Glejser LM Test P-Value e(lmhmss1) Machado-Santos-Silva LM Test: Ev=Yh Yh2 e(lmhmss1p) Machado-Santos-Silva LM Test: Ev=Yh Yh2 P-Value e(lmhmss2) Machado-Santos-Silva LM Test: Ev=X e(lmhmss2p) Machado-Santos-Silva LM Test: Ev=X P-Value e(lmhbg) Breusch-Godfrey Test e(lmhbgp) Breusch-Godfrey Test P-Value e(lmhcw1) Cook-Weisberg LM Test E = Yh e(lmhcw1p) Cook-Weisberg LM Test E = Y P-Valueh e(lmhcw2) Cook-Weisberg LM Test E = X e(lmhcw2p) Cook-Weisberg LM Test E = X P-Value

*** Spatial Panel Groupwise Heteroscedasticity Tests: e(lmhglm) Lagrange Multiplier LM Test e(lmhglmp) Lagrange Multiplier LM Test P-Value e(lmhglr) Likelihood Ratio LR Test e(lmhglrp) Likelihood Ratio LR Test P-Value e(lmhgw) Wald Test e(lmhgwp) Wald Test P-Value

*** Spatial Panel Non Normality Tests: e(lmnjb) Jarque-Bera LM Test e(lmnjbp) Jarque-Bera LM Test P-Value e(lmnw) White IM Test e(lmnwp) White IM Test P-Value e(lmndh) Doornik-Hansen LM Test e(lmndhp) Doornik-Hansen LM Test P-Value e(lmng) Geary LM Test e(lmngp) Geary LM Test P-Value e(lmnad) Anderson-Darling Z Test e(lmnadp) Anderson-Darling Z Test P-Value e(lmndp) D'Agostino-Pearson LM Test e(lmndpp) D'Agostino-Pearson LM Test P-Value e(lmnsvs) Srivastava LM Skewness Test e(lmnsvsp) Srivastava LM Skewness Test P-Value e(lmnsms1) Small LM Skewness Test e(lmnsms1p) Small LM Skewness Test P-Value e(lmnsms2) Skewness Z Test e(lmnsms2p) Skewness Z Test P-Value e(lmnsvk) Srivastava Z Kurtosis Test e(lmnsvkp) Srivastava Z Kurtosis Test P-Value e(lmnsmk1) Small LM Kurtosis Test e(lmnsmk1p) Small LM Kurtosis Test P-Value e(lmnsmk2) Kurtosis Z Test e(lmnsmk2p) Kurtosis Z Test P-Value e(sk) Skewness Coefficient e(sksd) Skewness Standard Deviation e(ku) Kurtosis Coefficient e(kusd) Kurtosis Standard Deviation e(sn) Standard Deviation Runs Sig(k) e(en) Mean Runs E(k) e(lower) Lower 95% Conf. Interval [E(k)- 1.96* Sig(k)] e(upper) Upper 95% Conf. Interval [E(k)+ 1.96* Sig(k)]

Matrixes e(b) coefficient vector e(V) variance-covariance matrix of the estimators e(mfxlin) Marginal Effect and Elasticity in Lin Form e(mfxlog) Marginal Effect and Elasticity in Log Form

+------------+ ----+ References +-------------------------------------------------------

Anderson, T.W. & C. Hsiao (1982) "Formulation and Estimation of Dynamic Models Using Panel Data", Journal of Econometrics, 18; 47–82.

Anderson T.W. & Darling D.A. (1954) "A Test of Goodness of Fit", Journal of the American Statisical Association, 49; 765–69.

Anderson, T. W. & H. Rubin (1950) "The Asymptotic Properties of Estimates of the Parameters of a Single Equation in a Complete System of Stochastic Equations", Annals of Mathematical Statistics, Vol. 21; 570-82.

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. & Kelejian, H. H. (1997) "Testing for Spatial Error Autocorrelation in the Presence of Endogenous Regressors", International Regional Science Review, (20); 153-182.

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.

Anselin L., Le Gallo J. & Jayet H (2006) "Spatial Panel Econometrics" In: Matyas L, Sevestre P. (eds) The Econometrics of Panel Data, Fundamentals and Recent Developments in Theory and Practice, 3rd edn. Kluwer, Dordrecht; 901-969.

Arellano, M. and S. Bond (1991) "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations" The Review of Economic Studies 58; 277-297.

Arellano, M. and S. Bond (1998) "Dynamic Panel Data Estimation Using DPD98 for Gauss" : A Guide for Users.

Arellano, M. and O. Bover (1995) "Another Look at the Instrumental Variable Estimation of Error-Components Models" Journal of Econometrics 68; 29-51.

Breusch, Trevor & Adrian Pagan (1980) "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics", Review of Economic Studies 47; 239-253.

C.M. Jarque & A.K. Bera (1987) "A Test for Normality of Observations and Regression Residuals" International Statistical Review , Vol. 55; 163-172.

Cook, R.D., & S. Weisberg (1983) "Diagnostics for Heteroscedasticity in Regression", Biometrica 70; 1-10.

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Damodar Gujarati (1995) "Basic Econometrics" 3rd Edition, McGraw Hill, New York, USA.

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Geary R.C. (1947) "Testing for Normality" Biometrika, Vol. 34; 209-242.

Geary R.C. (1970) "Relative Efficiency of Count of Sign Changes for Assessing Residuals Autoregression in Least Squares Regression" Biometrika, Vol. 57; 123-127.

Greene, William (2007) "Econometric Analysis", 6th ed., Macmillan Publishing Company Inc., New York, USA..

Griffiths, W., R. Carter Hill & George Judge (1993) "Learning and Practicing Econometrics", John Wiley & Sons, Inc., New York, USA.

Harvey, Andrew (1990) "The Econometric Analysis of Time Series", 2nd edition, MIT Press, Cambridge, Massachusetts.

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+----------+ ----+ 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 spglsxt. Note 4: xtset is included automatically in spglsxt models.

-------------------------------------------------------------------------------

clear all

sysuse spglsxt.dta, clear

db spglsxt

* (1) Spatial Panel Autoregressive Generalized Least Squares Regression spglsxt y x1 x2 , nc(7) wmfile(SPWxt) gmm(1) mfx(lin) test spglsxt y x1 x2 , nc(7) wmfile(SPWxt) gmm(1) mfx(log) test tolog spglsxt y x1 x2 , nc(7) wmfile(SPWxt) gmm(2) mfx(lin) test spglsxt y x1 x2 , nc(7) wmfile(SPWxt) gmm(3) mfx(lin) test -------------------------------------------------------------------------------

* (2) Spatial Panel Autoregressive Generalized Least Squares Regression (Cont.)

This example is taken from Prucha data about Spatial Panel Regression. More details can be found in: http://econweb.umd.edu/~prucha/Research_Prog3.htm Results of (spglsxt) with gmm(3) option is identical to: http://econweb.umd.edu/~prucha/STATPROG/PANOLS/PROGRAM3(L3).log

clear all sysuse spglsxt1.dta, clear spglsxt y x1 , wmfile(SPWxt1) nc(100) gmm(1) stand spglsxt y x1 , wmfile(SPWxt1) nc(100) gmm(2) stand spglsxt y x1 , wmfile(SPWxt1) nc(100) gmm(3) stand -------------------------------------------------------------------------------

. clear all . sysuse spglsxt.dta, clear . spglsxt y x1 x2 , nc(7) wmfile(SPWxt) mfx(lin) test

============================================================================== *** Binary (0/1) Weight Matrix: 49x49 - NC=7 NT=7 (Non Normalized) ============================================================================== ============================================================================== * Spatial Panel Autoregressive Generalized Least Squares Regression (SPGLS) ============================================================================== *** Initial GMM Model: 1 y = x1 + x2 ------------------------------------------------------------------------------ Sample Size = 49 | Cross Sections Number = 7 Wald Test = 45.3388 | P-Value > Chi2(2) = 0.0000 F-Test = 22.6694 | P-Value > F(2 , 40) = 0.0000 (Buse 1973) R2 = 0.7748 | Raw Moments R2 = 0.9590 (Buse 1973) R2 Adj = 0.7297 | Raw Moments R2 Adj = 0.9509 Root MSE (Sigma) = 8.6984 | Log Likelihood Function = -170.5500 ------------------------------------------------------------------------------ - R2h= 0.5500 R2h Adj= 0.4600 F-Test = 28.11 P-Value > F(2 , 40) 0.0000 - R2v= 0.4508 R2v Adj= 0.3410 F-Test = 18.88 P-Value > F(2 , 40) 0.0000 ------------------------------------------------------------------------------ y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.2904423 .0941184 -3.09 0.004 -.4806627 -.1002219 x2 | -1.323453 .3309552 -4.00 0.000 -1.992338 -.6545674 _cons | 64.47419 5.091475 12.66 0.000 54.18394 74.76445 ------------------------------------------------------------------------------

============================================================================== * Panel Model Selection Diagnostic Criteria ============================================================================== - Log Likelihood Function LLF = -170.5500 - Akaike Final Prediction Error AIC = 347.0999 - Schwarz Criterion SC = 352.7754 - Akaike Information Criterion ln AIC = 4.2458 - Schwarz Criterion ln SC = 4.3616 - Amemiya Prediction Criterion FPE = 80.2952 - Hannan-Quinn Criterion HQ = 72.9474 - Rice Criterion Rice = 70.3840 - Shibata Criterion Shibata = 69.3287 - Craven-Wahba Generalized Cross Validation-GCV = 70.0846 ------------------------------------------------------------------------------

============================================================================== *** Spatial Panel Aautocorrelation Tests ============================================================================== Ho: Error has No Spatial AutoCorrelation Ha: Error has Spatial AutoCorrelation

- GLOBAL Moran MI = 0.0594 P-Value > Z( 0.705) 0.4805 - GLOBAL Geary GC = 0.8831 P-Value > Z(-0.828) 0.4076 - GLOBAL Getis-Ords GO = -0.1698 P-Value > Z(-0.705) 0.4805 ------------------------------------------------------------------------------ - Moran MI Error Test = 0.3779 P-Value > Z(3.505) 0.7055 ------------------------------------------------------------------------------ - LM Error (Burridge) = 0.1647 P-Value > Chi2(1) 0.6849 - LM Error (Robust) = 0.6335 P-Value > Chi2(1) 0.4261 ------------------------------------------------------------------------------ Ho: Spatial Lagged Dependent Variable has No Spatial AutoCorrelation Ha: Spatial Lagged Dependent Variable has Spatial AutoCorrelation

- LM Lag (Anselin) = 0.3204 P-Value > Chi2(1) 0.5714 - LM Lag (Robust) = 0.7891 P-Value > Chi2(1) 0.3744 ------------------------------------------------------------------------------ Ho: No General Spatial AutoCorrelation Ha: General Spatial AutoCorrelation

- LM SAC (LMErr+LMLag_R) = 0.9539 P-Value > Chi2(2) 0.6207 - LM SAC (LMLag+LMErr_R) = 0.9539 P-Value > Chi2(2) 0.6207 ------------------------------------------------------------------------------

============================================================================== *** Panel Heteroscedasticity Tests ============================================================================== Ho: Panel Homoscedasticity - Ha: Panel Heteroscedasticity

- Engle LM ARCH Test AR(1): E2 = E2_1 = 0.5025 P-Value > Chi2(1) 0.4784 ------------------------------------------------------------------------------ - Hall-Pagan LM Test: E2 = Yh = 0.0254 P-Value > Chi2(1) 0.8734 - Hall-Pagan LM Test: E2 = Yh2 = 0.0000 P-Value > Chi2(1) 0.9968 - Hall-Pagan LM Test: E2 = LYh2 = 0.2283 P-Value > Chi2(1) 0.6328 ------------------------------------------------------------------------------ - Harvey LM Test: LogE2 = X = 2.4753 P-Value > Chi2(2) 0.2901 - Wald Test: LogE2 = X = 6.1075 P-Value > Chi2(1) 0.0135 - Glejser LM Test: |E| = X = 8.3688 P-Value > Chi2(2) 0.0152 - Breusch-Godfrey Test: E = E_1 X = 10.9753 P-Value > Chi2(1) 0.0009 ------------------------------------------------------------------------------ - Machado-Santos-Silva Test: Ev=Yh Yh2 = 0.2531 P-Value > Chi2(2) 0.8811 - Machado-Santos-Silva Test: Ev=X = 7.2616 P-Value > Chi2(2) 0.0265 ------------------------------------------------------------------------------ - White Test - Koenker(R2): E2 = X = 9.8517 P-Value > Chi2(2) 0.0073 - White Test - B-P-G (SSR): E2 = X = 14.2397 P-Value > Chi2(2) 0.0008 ------------------------------------------------------------------------------ - White Test - Koenker(R2): E2 = X X2 = 11.7997 P-Value > Chi2(4) 0.0189 - White Test - B-P-G (SSR): E2 = X X2 = 17.0553 P-Value > Chi2(4) 0.0019 ------------------------------------------------------------------------------ - White Test - Koenker(R2): E2 = X X2 XX= 24.9319 P-Value > Chi2(5) 0.0001 - White Test - B-P-G (SSR): E2 = X X2 XX= 36.0364 P-Value > Chi2(5) 0.0000 ------------------------------------------------------------------------------ - Cook-Weisberg LM Test: E2/S2n = Yh = 0.0367 P-Value > Chi2(1) 0.8481 - Cook-Weisberg LM Test: E2/S2n = X = 14.2397 P-Value > Chi2(2) 0.0008 ------------------------------------------------------------------------------ *** Single Variable Tests (E2/Sig2): - Cook-Weisberg LM Test: x1 = 4.1504 P-Value > Chi2(1) 0.0416 - Cook-Weisberg LM Test: x2 = 3.0020 P-Value > Chi2(1) 0.0832 ------------------------------------------------------------------------------ *** Single Variable Tests: - King LM Test: x1 = 0.3196 P-Value > Chi2(1) 0.5718 - King LM Test: x2 = 2.9761 P-Value > Chi2(1) 0.0845 ------------------------------------------------------------------------------

============================================================================== * Panel Groupwise Heteroscedasticity Tests ============================================================================== Ho: Panel Homoscedasticity - Ha: Panel Groupwise Heteroscedasticity

- Lagrange Multiplier LM Test = 7.3373 P-Value > Chi2(6) 0.2908 - Likelihood Ratio LR Test = 7.1253 P-Value > Chi2(6) 0.3094 - Wald Test = 12.4812 P-Value > Chi2(7) 0.0858 ------------------------------------------------------------------------------

============================================================================== * Panel Non Normality Tests ============================================================================== Ho: Normality - Ha: Non Normality ------------------------------------------------------------------------------ *** Non Normality Tests: - Jarque-Bera LM Test = 1.8192 P-Value > Chi2(2) 0.4027 - White IM Test = 11.9460 P-Value > Chi2(2) 0.0025 - Doornik-Hansen LM Test = 4.7489 P-Value > Chi2(2) 0.0931 - Geary LM Test = -0.7192 P-Value > Chi2(2) 0.6980 - Anderson-Darling Z Test = 0.3559 P > Z( 0.087) 0.5346 - D'Agostino-Pearson LM Test = 2.6169 P-Value > Chi2(2) 0.2702 ------------------------------------------------------------------------------ *** Skewness Tests: - Srivastava LM Skewness Test = 0.1992 P-Value > Chi2(1) 0.6554 - Small LM Skewness Test = 0.2464 P-Value > Chi2(1) 0.6196 - Skewness Z Test = -0.4964 P-Value > Chi2(1) 0.6196 ------------------------------------------------------------------------------ *** Kurtosis Tests: - Srivastava Z Kurtosis Test = 1.2728 P-Value > Z(0,1) 0.2031 - Small LM Kurtosis Test = 2.3705 P-Value > Chi2(1) 0.1236 - Kurtosis Z Test = 1.5396 P-Value > Chi2(1) 0.1236 ------------------------------------------------------------------------------ Skewness Coefficient = -0.1562 - Standard Deviation = 0.3398 Kurtosis Coefficient = 3.8908 - Standard Deviation = 0.6681 ------------------------------------------------------------------------------ Runs Test: (23) Runs - (24) Positives - (25) Negatives Standard Deviation Runs Sig(k) = 3.4619 , Mean Runs E(k) = 25.4898 95% Conf. Interval [E(k)+/- 1.96* Sig(k)] = (18.7045 , 32.2751 ) ------------------------------------------------------------------------------

* Linear: Marginal Effect - Elasticity *

+-----------------------------------------------------------------------------+ | Variable | Marginal_Effect(B) | Elasticity(Es) | Mean | |--------------+--------------------+--------------------+--------------------| | x1 | -0.2904 | -0.3178 | 38.4362 | | x2 | -1.3235 | -0.5416 | 14.3749 | +-----------------------------------------------------------------------------+ Mean of Dependent Variable = 35.1288

+-----------------+ ----+ Acknowledgments +--------------------------------------------------

I would like to thank Mudit Kapoor, Harry H. Kelejian and Ingmar R. Prucha.

+--------+ ----+ Author +-----------------------------------------------------------

Emad Abd Elmessih Shehata Professor (PhD Economics) 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

+------------------+ ----+ SPGLSXT Citation +-------------------------------------------------

Shehata, Emad Abd Elmessih (2012) SPGLSXT: "Spatial Panel Autoregressive Generalized Least Squares Regression"

http://ideas.repec.org/c/boc/bocode/s457421.html

http://econpapers.repec.org/software/bocbocode/s457421.htm