-------------------------------------------------------------------------------
help: lmcovvar                                                   dialog: lmcovv
> ar
-------------------------------------------------------------------------------

+-------+ ----+ Title +------------------------------------------------------------

lmcovvar: (VAR) Breusch-Pagan Diagonal Covariance Matrix Test

+-------------------+ ----+ Table of Contents +------------------------------------------------

Syntax Options Description Saved Results References

*** Examples

Authors

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

lmcovvar depvarlist [if] [in] , [ noconstant lags(numlist) exog( varlist) nolog constraints(numlist) iterate(#) dfk nobigf lutstats small tolerance(#) noisure nocnsreport level(#) ]

+---------+ ----+ Options +----------------------------------------------------------

options Description ------------------------------------------------------------------------- Model

noconstant suppress constant term lags(#/#) Dependent Variables Lag length; default is (1/1) exog(varlist) use exogenous variables varlist

Model 2 constraints(numlist) apply specified linear constraints nolog suppress SURE iteration log tolerance(#) set convergence tolerance of SURE noisure use one-step SURE dfk make small-sample degrees-of-freedom adjustment small report small-sample t and F statistics nobigf do not compute parameter vector for coefficients implicitly set to zero iterate(#) set maximum number of iterations for SURE; default is iterate(1600) Reporting level(#) set confidence level; default is level(95) lutstats report Lütkepohl lag-order selection statistics nocnsreport do not display constraints

*** tsset must be used before using lmcovvar

depvarlist and varlist may contain time-series operators; see tsvarlist

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

lmcovvar computes Breusch-Pagan Diagonal Covariance Matrix Test after: - (VAR) Vector Autoregressive Model (var). - lmcovvar options are identically to official (var) command.

- (var) model with SURE estimations assumes: 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- SURE 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. 5- 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 SURE. Breusch-Pagan Diagonal Covariance Matrix LM Test 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 +----------------------------------------------------

lmcovvar saves the following in r():

Scalars

r(lmcov) LM Diagonal Covariance Matrix Test r(lmcovp) LM Diagonal Covariance Matrix Test P-Value r(lmcovdf) Chi2 Degrees of Freedom

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

Greene, William (1993) "Econometric Analysis", 2nd ed., Macmillan Publishing Company Inc., New York, USA.; 490-491.

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; 758-763.

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; 477-478.

Kmenta, Jan (1986) "Elements of Econometrics", 2nd ed., Macmillan Publishing Company, Inc., New York, USA; 645.

+----------+ ----+ Examples +---------------------------------------------------------

clear all sysuse lmcovvar.dta , clear tsset t lmcovvar y1 y2, lags(1/1) exog(x1 x2) return list

* If you want to use dialog box: Press OK to compute lmcovvar

db lmcovvar

* This example is taken from: 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; 758-763.

clear all sysuse lmcovvar.dta , clear tsset t

lmcovvar y1 y2 in 5/71 , lags(1/1) lmcovvar y1 y2 in 5/71 , lags(1/2) lmcovvar y1 y2 in 5/71 , lags(1/3)

return list -------------------------------------------------------------------------------

. clear all . sysuse lmcovvar.dta , clear . tsset t . lmcovvar y1 y2 in 5/71 , lags(1/1)

Vector autoregression

Sample: 5 - 71 No. of obs = 67 Log likelihood = -566.0542 AIC = 17.07625 FPE = 89376.34 HQIC = 17.15437 Det(Sigma_ml) = 74711.32 SBIC = 17.27368

Equation Parms RMSE R-sq chi2 P>chi2 ---------------------------------------------------------------- y1 3 16.6829 0.2878 27.07799 0.0000 y2 3 19.9034 0.1863 15.33891 0.0005 ----------------------------------------------------------------

------------------------------------------------------------------------------ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- y1 | y1 | L1. | -.0903073 .1279538 -0.71 0.480 -.3410921 .1604774 | y2 | L1. | .5189002 .114004 4.55 0.000 .2954564 .742344 | _cons | 7.949964 2.585932 3.07 0.002 2.88163 13.0183 -------------+---------------------------------------------------------------- y2 | y1 | L1. | .1970232 .1526549 1.29 0.197 -.1021748 .4962212 | y2 | L1. | .297665 .1360122 2.19 0.029 .0310861 .564244 | _cons | 8.537288 3.085138 2.77 0.006 2.490528 14.58405 ------------------------------------------------------------------------------

============================================================================== * (VAR) Breusch-Pagan LM Diagonal Covariance Matrix Test ============================================================================== Ho: Diagonal Disturbance Covariance Matrix (Independent Equations) Ho: Run OLS - Ha: Run SUR

Lagrange Multiplier Test = 17.24311 Degrees of Freedom = 1.0 P-Value > Chi2(1) = 0.00003 ==============================================================================

+---------+ ----+ Authors +----------------------------------------------------------

- 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

- 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

+-------------------+ ----+ LMCOVVAR Citation +------------------------------------------------

Shehata, Emad Abd Elmessih & Sahra Khaleel A. Mickaiel (2012) LMCOVVAR: "(VAR) Breusch-Pagan Diagonal Covariance Matrix Test"

Online Help:

* Breusch-Pagan Diagonal Covariance Matrix Test: lmcovnlsur (NL-SUR) Breusch-Pagan Diagonal Covariance Matrix Test lmcovreg3 (3SLS-SUR) Breusch-Pagan Diagonal Covariance Matrix Test lmcovsem (SEM-FIML) Breusch-Pagan Diagonal Covariance Matrix Test lmcovvar (VAR) Breusch-Pagan Diagonal Covariance Matrix Test lmcovxt Panel Data Breusch-Pagan Diagonal Covariance Matrix Test ---------------------------------------------------------------------------

* (1) (3SLS-SUR) * Simultaneous Equations: lmareg3 (3SLS-SUR) Overall System Autocorrelation Tests lmhreg3 (3SLS-SUR) Overall System Heteroscedasticity Tests lmnreg3 (3SLS-SUR) Overall System Non Normality Tests lmcovreg3 (3SLS-SUR) Breusch-Pagan Diagonal Covariance Matrix r2reg3 (3SLS-SUR) Overall System R2, F-Test, and Chi2-Test diagreg3 (3SLS-SUR) Overall System ModeL Selection Diagnostic Criteria --------------------------------------------------------------------------- * (2) (SEM-FIML) * Structural Equation Modeling: lmasem (SEM-FIML) Overall System Autocorrelation Tests lmhsem (SEM-FIML) Overall System Heteroscedasticity Tests lmnsem (SEM-FIML) Overall System Non Normality Tests lmcovsem (SEM-FIML) Breusch-Pagan Diagonal Covariance Matrix Test r2sem (SEM-FIML) Overall System R2, F-Test, and Chi2-Test diagsem (SEM-FIML) Overall System ModeL Selection Diagnostic Criteria --------------------------------------------------------------------------- * (3) (NL-SUR) * Non Linear Seemingly Unrelated Regression: lmanlsur (NL-SUR) Overall System Autocorrelation Tests lmhnlsur (NL-SUR) Overall System Heteroscedasticity Tests lmnnlsur (NL-SUR) Overall System Non Normality Tests lmcovnlsur (NL-SUR) Breusch-Pagan Diagonal Covariance Matrix Test r2nlsur (NL-SUR) Overall System R2, F-Test, and Chi2-Test diagnlsur (NL-SUR) Overall System ModeL Selection Diagnostic Criteria --------------------------------------------------------------------------- * (4) (VAR) * Vector Autoregressive Model: lmavar (VAR) Overall System Autocorrelation Tests lmhvar (VAR) Overall System Heteroscedasticity Tests lmnvar (VAR) Overall System Non Normality Tests lmcovvar (VAR) Breusch-Pagan Diagonal Covariance Matrix Test r2var (VAR) Overall System R2, F-Test, and Chi2-Test diagvar (VAR) Overall System ModeL Selection Diagnostic Criteria ---------------------------------------------------------------------------