{smcl} {hline} {cmd:help: {helpb lmcovsem}}{space 50} {cmd:dialog:} {bf:{dialog lmcovsem}} {hline} {bf:{err:{dlgtab:Title}}} {p 4 8 2} {bf:lmcovsem: (SEM-FIML) Breusch-Pagan Diagonal Covariance Matrix LM Test} {marker 00}{bf:{err:{dlgtab:Table of Contents}}} {p 4 8 2} {p 5}{helpb lmcovsem##01:Syntax}{p_end} {p 5}{helpb lmcovsem##02:Description}{p_end} {p 5}{helpb lmcovsem##03:Saved Results}{p_end} {p 5}{helpb lmcovsem##04:References}{p_end} {p 1}*** {helpb lmcovsem##05:Examples}{p_end} {p 5}{helpb lmcovsem##06:Author}{p_end} {p2colreset}{...} {marker 01}{bf:{err:{dlgtab:Syntax}}} {p 10 4 6} {opt lmcovsem}{p_end} {p2colreset}{...} {marker 02}{bf:{err:{dlgtab:Description}}} {p 2 2 2}- (SEM) Structural Equation Modeling Regressions {helpb sem} for system of simultaneous equations.{p_end} {p 3 2 2}- SEM Estimations assume:{p_end} {p 4 7 7}1- Independence of the errors in each eqution or no correlations between different periods in the same equation.{p_end} {p 4 7 7}2- no correlations between the errors for any of two equtions between two different periods, this is called {cmd:"Intertemporal Correlation"}.{p_end} {p 4 7 7}3- correlations may be exist between different two equations, but at the same period, and this is called {cmd:"Contemporaneous Correlation"}.{p_end} {p 4 7 7}4- SEM can be applied when there is correlations between different two equations at the same period, or if the independent variables are differnt from equation to equation.{p_end} {p 4 7 7}5- If {cmd:"Contemporaneous Correlation"} does not exist, ordinary least squares (OLS) can be applied separately to each equation, the results are fully efficient and there is no need to estimate SEM.{p_end} {p 4 4 4} Breusch-Pagan LM can test whether contemporaneous diagonal covariance matrix is 0. (Independence of the Errors), or correlated if at least one covariance is nonzero.{p_end} {p 4 4 4} Ho: {cmd:no Contemporaneous Correlation}: Sig12 = Sig13 = Sig23 = ... = 0.{p_end} {p 4 4 4} Ha: {cmd: Contemporaneous Correlation}: at least one Covariance is nonzero.{p_end} {p2colreset}{...} {marker 03}{bf:{err:{dlgtab:Saved Results}}} {p 2 4 2 }{cmd:lmcovsem} saves the following in {cmd:r()}: {col 4}{cmd:r(lmcov)}{col 20}LM Diagonal Covariance Matrix Test {col 4}{cmd:r(lmcovp)}{col 20}LM Diagonal Covariance Matrix Test P-Value {col 4}{cmd:r(lmcovdf)}{col 20}Chi2 Degrees of Freedom {marker 04}{bf:{err:{dlgtab:References}}} {p 4 8 2}Judge, Georege, R. Carter Hill, William . E. Griffiths, Helmut Lutkepohl, & Tsoung-Chao Lee (1988) {cmd: "Introduction To The Theory And Practice Of Econometrics",} {it:2nd ed., John Wiley & Sons, Inc., New York, USA}; 456-461. {p 4 8 2}Judge, Georege, W. E. Griffiths, R. Carter Hill, Helmut Lutkepohl, & Tsoung-Chao Lee(1985) {cmd: "The Theory and Practice of Econometrics",} {it:2nd ed., John Wiley & Sons, Inc., New York, USA}. {p2colreset}{...} {marker 05}{bf:{err:{dlgtab:Examples}}} in this example FIML will be used as follows: {stata clear all} {stata sysuse lmcovsem.dta , clear} {stata sem (y1 <- y2 x1 x2) (y2 <- y1 x3 x4), cov(e.y1*e.y2)} {stata lmcovsem} {stata return list} * If you want to use dialog box: Press OK to compute lmcovsem {stata db lmcovsem} {hline} . clear all . sysuse lmcovsem.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 . lmcovsem ============================================================================== * (SEM-FIML) Breusch-Pagan Diagonal Covariance Matrix LM Test - Method(ml) ============================================================================== Ho: Diagonal Disturbance Covariance Matrix (Independent Equations) Ho: Run OLS - Ha: Run SEM Lagrange Multiplier Test = 0.97750 Degrees of Freedom = 1.0 P-Value > Chi2(1) = 0.32282 ============================================================================== {marker 06}{bf:{err:{dlgtab:Author}}} {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 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:LMCOVSEM Citation}}} {p 1}{cmd:Shehata, Emad Abd Elmessih (2012)}{p_end} {p 1 10 1}{cmd:LMCOVSEM: "Stata Module to Compute (SEM-FIML) Breusch-Pagan Diagonal Covariance Matrix LM Test"}{p_end} {browse "http://ideas.repec.org/c/boc/bocode/s457435.html"} {browse "http://econpapers.repec.org/software/bocbocode/s457435.htm"} {title:Online Help:} {bf:{err:* Breusch-Pagan Diagonal Covariance Matrix Test:}} {helpb lmcovnlsur}{col 12}(NL-SUR) Breusch-Pagan Diagonal Covariance Matrix Test {helpb lmcovreg3}{col 12}(3SLS-SUR) Breusch-Pagan Diagonal Covariance Matrix Test {helpb lmcovsem}{col 12}(SEM-FIML) Breusch-Pagan Diagonal Covariance Matrix Test {helpb lmcovvar}{col 12}(VAR) Breusch-Pagan Diagonal Covariance Matrix Test {helpb lmcovxt}{col 12}Panel Data Breusch-Pagan Diagonal Covariance Matrix Test --------------------------------------------------------------------------- {bf:{err:* (1) (3SLS-SUR) * Simultaneous Equations:}} {helpb lmareg3}{col 12}(3SLS-SUR) Overall System Autocorrelation Tests {helpb lmhreg3}{col 12}(3SLS-SUR) Overall System Heteroscedasticity Tests {helpb lmnreg3}{col 12}(3SLS-SUR) Overall System Non Normality Tests {helpb lmcovreg3}{col 12}(3SLS-SUR) Breusch-Pagan Diagonal Covariance Matrix {helpb r2reg3}{col 12}(3SLS-SUR) Overall System R2, F-Test, and Chi2-Test {helpb diagreg3}{col 12}(3SLS-SUR) Overall System ModeL Selection Diagnostic Criteria --------------------------------------------------------------------------- {bf:{err:* (2) (SEM-FIML) * Structural Equation Modeling:}} {helpb lmasem}{col 12}(SEM-FIML) Overall System Autocorrelation Tests {helpb lmhsem}{col 12}(SEM-FIML) Overall System Heteroscedasticity Tests {helpb lmnsem}{col 12}(SEM-FIML) Overall System Non Normality Tests {helpb lmcovsem}{col 12}(SEM-FIML) Breusch-Pagan Diagonal Covariance Matrix Test {helpb r2sem}{col 12}(SEM-FIML) Overall System R2, F-Test, and Chi2-Test {helpb diagsem}{col 12}(SEM-FIML) Overall System ModeL Selection Diagnostic Criteria --------------------------------------------------------------------------- {bf:{err:* (3) (NL-SUR) * Non Linear Seemingly Unrelated Regression:}} {helpb lmanlsur}{col 12}(NL-SUR) Overall System Autocorrelation Tests {helpb lmhnlsur}{col 12}(NL-SUR) Overall System Heteroscedasticity Tests {helpb lmnnlsur}{col 12}(NL-SUR) Overall System Non Normality Tests {helpb lmcovnlsur}{col 12}(NL-SUR) Breusch-Pagan Diagonal Covariance Matrix Test {helpb r2nlsur}{col 12}(NL-SUR) Overall System R2, F-Test, and Chi2-Test {helpb diagnlsur}{col 12}(NL-SUR) Overall System ModeL Selection Diagnostic Criteria --------------------------------------------------------------------------- {bf:{err:* (4) (VAR) * Vector Autoregressive Model:}} {helpb lmavar}{col 12}(VAR) Overall System Autocorrelation Tests {helpb lmhvar}{col 12}(VAR) Overall System Heteroscedasticity Tests {helpb lmnvar}{col 12}(VAR) Overall System Non Normality Tests {helpb lmcovvar}{col 12}(VAR) Breusch-Pagan Diagonal Covariance Matrix Test {helpb r2var}{col 12}(VAR) Overall System R2, F-Test, and Chi2-Test {helpb diagvar}{col 12}(VAR) Overall System ModeL Selection Diagnostic Criteria --------------------------------------------------------------------------- {psee} {p_end}