+-------+ ----+ Title +------------------------------------------------------------
lmcovsem: Breusch-Pagan LM Diagonal Covariance Matrix Test after (SEM)
+-------------------+ ----+ Table of Contents +------------------------------------------------
Syntax Description Saved Results References
*** Examples
Author
+--------+ ----+ Syntax +-----------------------------------------------------------
lmcovsem
+-------------+ ----+ Description +------------------------------------------------------ - (SEM) Structural Equation Modeling Regressions sem for system of simultaneous equations. - SEM Estimations assume: 1- Independence of the errors in each eqution or no correlations between different periods in the same equation. 2- no correlations between the errors for any of two equtions between two different periods, this is called "Intertemporal Correlation". 3- correlations may be exist between different two equations, but at the same period, and this is called "Contemporaneous Correlation". 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. 4- If "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. 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. Ho: no Contemporaneous Correlation: Sig12 = Sig13 = Sig23 = ... = 0. Ha: Contemporaneous Correlation: at least one Covariance is nonzero.
+---------------+ ----+ Saved Results +----------------------------------------------------
lmcovsem saves the following in r():
r(lmcov) Lagrange Multiplier LM Test r(lmcovp) Lagrange Multiplier LM Test P-Value r(lmcovdf) Chi2 Degrees of Freedom
+------------+ ----+ References +-------------------------------------------------------
Judge, Georege, R. Carter Hill, William . E. Griffiths, Helmut Lutkepohl, & Tsoung-Chao Lee (1988) "Introduction To The Theory And Practice Of Econometrics", 2nd ed., John Wiley & Sons, Inc., New York, USA; 456-461.
Judge, Georege, W. E. Griffiths, R. Carter Hill, Helmut Lutkepohl, & Tsoung-Chao Lee(1985) "The Theory and Practice of Econometrics", 2nd ed., John Wiley & Sons, Inc., New York, USA.
+----------+ ----+ Examples +---------------------------------------------------------
in this example FIML will be used as follows:
clear all
sysuse lmcovsem.dta , clear
sem (y1 <- y2 x1 x2) (y2 <- y1 x3 x4), cov(e.y1*e.y2)
lmcovsem
return list
* If you want to use dialog box: Press OK to compute lmcovsem
db lmcovsem
. 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 ============================================================================== * Breusch-Pagan LM Diagonal Covariance Matrix Test: SEM - 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 ==============================================================================
+--------+ ----+ Author +-----------------------------------------------------------
Emad Abd Elmessih Shehata Assistant Professor 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
+-------------------+ ----+ lmcovsem Citation +------------------------------------------------
Shehata, Emad Abd Elmessih (2012) LMCOVSEM: "Stata Module to Compute Breusch-Pagan Lagrange Multiplier Diagonal Covariance Matrix Test after Structural Equation Modeling Regressions (SEM) Regressions"
Online Help:
lmasem, lmhsem, lmnsem, lmcovsem, r2sem, lmareg3, lmhreg3, lmnreg3, lmcovreg3, r2reg3. (if installed).