{smcl} {hline} {cmd:help: {helpb lmnsem}}{space 55} {cmd:dialog:} {bf:{dialog lmnsem}} {hline} {bf:{err:{dlgtab:Title}}} {bf: lmnsem: Overall System Non Normality Tests after (SEM) Regressions} {bf:{err:{dlgtab:Syntax}}} {cmd: lmnsem} {bf:{err:{dlgtab:Description}}} {p 2 2 2}lmnsem computes overall system Non Normality Tests, after:{p_end} {p 2 2 2}- (SEM) Structural Equation Modeling Regressions {helpb sem} for system of simultaneous equations.{p_end} {bf:{err:{dlgtab:Saved Results}}} {pstd} {cmd:lmnsem} saves the following in {cmd:r()}: {synoptset 12 tabbed}{...} {p2col 5 12 12 2: Scalars}{p_end} {col 4}{cmd:r(lmnjb_#)}{col 20}Jarque-Bera LM Test for eq.# {col 4}{cmd:r(lmnjbp_#)}{col 20}Jarque-Bera LM Test for eq.# P-Value {col 4}{cmd:r(lmnjb)}{col 20}Jarque-Bera LM Test {col 4}{cmd:r(lmnjbp)}{col 20}Jarque-Bera LM Test P-Value {col 4}{cmd:r(lmndh)}{col 20}Doornik-Hansen LM Test {col 4}{cmd:r(lmndhp)}{col 20}Doornik-Hansen LM Test P-Value {col 4}{cmd:r(lmng)}{col 20}Geary LM Test {col 4}{cmd:r(lmngp)}{col 20}Geary LM Test P-Value {col 4}{cmd:r(lmnad)}{col 20}Anderson-Darling Z Test {col 4}{cmd:r(lmnadp)}{col 20}Anderson-Darling Z Test P-Value {col 4}{cmd:r(lmndp)}{col 20}D'Agostino-Pearson LM Test {col 4}{cmd:r(lmndpp)}{col 20}D'Agostino-Pearson LM Test P-Value {col 4}{cmd:r(lmnsvs)}{col 20}Srivastava LM Skewness Test {col 4}{cmd:r(lmnsvsp)}{col 20}Srivastava LM Skewness Test P-Value {col 4}{cmd:r(lmnsms1)}{col 20}Small LM Skewness Test {col 4}{cmd:r(lmnsms1p)}{col 20}Small LM Skewness Test P-Value {col 4}{cmd:r(lmnsms2)}{col 20}Skewness Z Test {col 4}{cmd:r(lmnsms2p)}{col 20}Skewness Z Test P-Value {col 4}{cmd:r(lmnsvk)}{col 20}Srivastava Z Kurtosis Test {col 4}{cmd:r(lmnsvkp)}{col 20}Srivastava Z Kurtosis Test P-Value {col 4}{cmd:r(lmnsmk1)}{col 20}Small LM Kurtosis Test {col 4}{cmd:r(lmnsmk1p)}{col 20}Small LM Kurtosis Test P-Value {col 4}{cmd:r(lmnsmk2)}{col 20}Kurtosis Z Test {col 4}{cmd:r(lmnsmk2p)}{col 20}Kurtosis Z Test P-Value {col 4}{cmd:r(sk)}{col 20}Skewness Coefficient {col 4}{cmd:r(sksd)}{col 20}Skewness Standard Deviation {col 4}{cmd:r(ku)}{col 20}Kurtosis Coefficient {col 4}{cmd:r(kusd)}{col 20}Kurtosis Standard Deviation {col 4}{cmd:r(sn)}{col 20}Standard Deviation Runs Sig(k) {col 4}{cmd:r(en)}{col 20}Mean Runs E(k) {col 4}{cmd:r(lower)}{col 20}Lower 95% Conf. Interval [E(k)- 1.96* Sig(k)] {col 4}{cmd:r(upper)}{col 20}Upper 95% Conf. Interval [E(k)+ 1.96* Sig(k)] {bf:{err:{dlgtab:References}}} {p 4 8 2}Anderson T.W., Darling D.A. (1954) {cmd: "A Test of Goodness of Fit",} {it:Journal of the American Statisical Association, 49}; 765–69. {p 4 8 2}C.M. Jarque & A.K. Bera (1987) {cmd: "A Test for Normality of Observations and Regression Residuals"} {it:International Statistical Review} , Vol. 55; 163-172. {p 4 8 2}D'Agostino, R. B., & Rosman, B. (1974) {cmd: "The Power of Geary’s Test of Normality",} {it:Biometrika, 61(1)}; 181-184. {p 4 8 2}Damodar Gujarati (1995) {cmd: "Basic Econometrics"} {it:3rd Edition, McGraw Hill, New York, USA}. {p 4 8 2}Geary R.C. (1947) {cmd: "Testing for Normality"} {it:Biometrika, Vol. 34}; 209-242. {p 4 8 2}Geary R.C. (1970) {cmd: "Relative Efficiency of Count of Sign Changes for Assessing Residuals Autoregression in Least Squares Regression"} {it:Biometrika, Vol. 57}; 123-127. {p 4 8 2}Pearson, E. S., D'Agostino, R. B., & Bowman, K. O. (1977) {cmd: "Tests for Departure from Normality: Comparison of Powers",} {it:Biometrika, 64(2)}; 231-246. {bf:{err:{dlgtab:Examples}}} in this example FIML will be used as follows: {stata clear all} {stata sysuse lmnsem.dta , clear} {stata sem (y1 <- y2 x1 x2) (y2 <- y1 x3 x4), cov(e.y1*e.y2)} {stata lmnsem} {stata return list} * If you want to use dialog box: Press OK to compute lmnsem {stata db lmnsem} . clear all . sysuse lmnsem.dta , clear . sem (y1 <- y2 x1 x2) (y2 <- y1 x3 x4), cov(e.y1*e.y2) Structural equation model Number of obs = 17 Estimation method = ml Log likelihood = -363.34588 ------------------------------------------------------------------------------ | OIM | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- Structural | y1 <- | y2 | .2425937 .2106232 1.15 0.249 -.1702201 .6554075 x1 | .2568409 .462485 0.56 0.579 -.649613 1.163295 x2 | -1.037016 .3154059 -3.29 0.001 -1.6552 -.4188317 _cons | 147.0826 54.4491 2.70 0.007 40.36431 253.8009 -----------+---------------------------------------------------------------- y2 <- | y1 | -.6282929 .6148239 -1.02 0.307 -1.833326 .5767398 x3 | -.5226661 .3235637 -1.62 0.106 -1.156839 .1115071 x4 | 3.4208 1.440664 2.37 0.018 .5971513 6.244449 _cons | 62.44495 42.36071 1.47 0.140 -20.58052 145.4704 -------------+---------------------------------------------------------------- Variance | e.y1 | 80.17577 28.99122 39.46865 162.8673 e.y2 | 142.4478 80.80501 46.86006 433.0208 -------------+---------------------------------------------------------------- Covariance | e.y1 | e.y2 | 25.62619 53.75243 0.48 0.634 -79.72665 130.979 ------------------------------------------------------------------------------ LR test of model vs. saturated: chi2(2) = 0.12, Prob > chi2 = 0.9408 . lmnsem ================================================= * System Non Normality Tests (ml) ================================================= *** Single Equation Non Normality Tests: Ho: Normality - Ha: Non Normality Eq. 1 : Jarque-Bera LM Test = 2.6232 P-Value > Chi2(2) 0.2694 Eq. 2 : Jarque-Bera LM Test = 2.2936 P-Value > Chi2(2) 0.3177 ------------------------------------------------------------------------------ *** Overall System Non Normality Tests: Ho: No Overall System Non Normality *** Non Normality Tests: - Jarque-Bera LM Test = 5.4681 P-Value > Chi2(2) 0.0650 - Doornik-Hansen LM Test = 4.9476 P-Value > Chi2(2) 0.0843 - Geary LM Test = 0.4365 P-Value > Chi2(2) 0.8039 - Anderson-Darling Z Test = -0.4223 P-Value>Z( 1.361) 0.9133 - D'Agostino-Pearson LM Test = 7.0244 P-Value > Chi2(2) 0.0298 ------------------------------------------------------------------------------ *** Skewness Tests: - Srivastava LM Skewness Test = 4.4358 P-Value > Chi2(1) 0.0352 - Small LM Skewness Test = 4.9358 P-Value > Chi2(1) 0.0263 - Skewness Z Test = -2.2217 P-Value > Chi2(1) 0.0263 ------------------------------------------------------------------------------ *** Kurtosis Tests: - Srivastava Z Kurtosis Test = 1.0160 P-Value > Z(0,1) 0.3096 - Small LM Kurtosis Test = 2.0887 P-Value > Chi2(1) 0.1484 - Kurtosis Z Test = 1.4452 P-Value > Chi2(1) 0.0742 ------------------------------------------------------------------------------ Skewness Coefficient = -0.8848 - Standard Deviation = 0.4031 Kurtosis Coefficient = 3.8536 - Standard Deviation = 0.7879 ------------------------------------------------------------------------------ Runs Test: (19) Runs - (19) Positives - (15) Negatives Standard Deviation Runs Sig(k) = 2.8300 , Mean Runs E(k) = 17.7647 95% Conf. Interval [E(k)+/- 1.96* Sig(k)] = (12.2179 , 23.3115 ) ------------------------------------------------------------------------------ {bf:{err:{dlgtab:Author}}} {hi:Emad Abd Elmessih Shehata} {hi:Assistant Professor} {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"}} {bf:{err:{dlgtab:lmnsem Citation}}} {phang}{cmd:Shehata, Emad Abd Elmessih (2012)}{p_end} {phang}{cmd:LMNSEM: "Stata Module to Compute Overall System Non Normality Tests after Structural Equation Modeling (SEM) Regressions"}{p_end} {title:Online Help:} {p 2 10 2} {helpb lmasem}, {helpb lmhsem}, {helpb lmnsem}, {helpb lmcovsem}, {helpb r2sem},{p_end} {p 2 10 2} {helpb lmareg3}, {helpb lmhreg3}, {helpb lmnreg3}, {helpb lmcovreg3}, {helpb r2reg3}. {opt (if installed)}.{p_end} {psee} {p_end}