help bayerhanck
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Title

bayerhanck -- Test for Non-Cointegration

Syntax

bayerhanck LHSvar [if] [in] , rhs(RHSvarlist) [options]

    options                     Description
    -------------------------------------------------------------------------
    Model
      lags(#)                   use # for the maximum number of augmentation
                                  lags (>=0), default is 1
      trend(constant)           include an unrestricted constant in model;
                                  the default
      trend(trend)              include a linear trend in the cointegrating
                                  equations and a quadratic trend in the
                                  undifferenced data
      trend(none)               do not include a trend or a constant

    Reporting
      crit(1 | 5 | 10)           Level-alpha in % for the critical value of
                                  the test to be reported

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Description

    bayerhanck produces a joint test-statistic for the null of
    no-cointegration based on Engle-Granger, Johansen maximum eigenvalue,
    Boswijk, and Banerjee tests (see Bayer and Hanck, 2009, for details).


Saved results


bayerhanck saves the following in e():

    Scalars     
      e(EJ)             test statistics based on Engle-Granger and Johansen
                         tests
      e(BECREJ)         test statistics based on all four tests
      e(CRIT_EJ)        critical value for the test statistics based on
                         Engle-Granger and Johansen tests
      e(CRIT_BECREJ)    critical value for the test statistics based on all
                         four tests

    Matrices    
      e(STAT)           test statistics of all four underlying tests
      e(PV)             corresponding p-values

Examples

    Example 1: (underlying tests produce conflicting results)

    Setup
        . webuse lutkepohl

    Test for non-cointegration
        . bayerhanck linvestment, rhs(lincome lconsumption)


    Same as above, but use 4 lags in the underlying VECM model
        . bayerhanck linvestment, rhs(lincome lconsumption) lags(4)

    Same as above, but report 10% critical values instead of 5%
        . bayerhanck y, rhs(i c) lags(4) crit(10)


    Example 2: Monte Carlo Study

        clear all
        set more off
        set matsize 100
        local rep=100
        mat def testPower=J(`rep',2,999)
        mat def testSize=J(`rep',2,999)
        forv z=1/`rep' {
            clear
            qui {
            set obs 400
            gen dx=rnormal()
            gen x=sum(dx)+50
            forv j=1/5 {
            gen dz`j'=rnormal()
            gen z`j'=sum(dz`j')
            }
            gen T=_n
            tsset T
            gen u=rnormal()
            replace u=u+0.95*l.u if T>1
            gen y=x+u
            drop if T<200
            replace x=x+T
        
            di "Power Example"
            bayerhanck x, rhs(y) trend(trend) lags(1)
            mat testPower[`z',1]=`e(EJ)'
            mat testPower[`z',2]=`e(BECREJ)'
            di "Size Example"
            bayerhanck x, rhs(z*) trend(trend) lags(1) crit(10)
            mat testSize[`z',1]=`e(EJ)'
            mat testSize[`z',2]=`e(BECREJ)'
            }
        }
        ereturn list
        svmat testPower
        svmat testSize
        keep test*
        drop if testPower1==.
        count if testSize1>e(CRIT_EJ)
        count if testSize2>e(CRIT_BECREJ)
        count if testPower1>e(CRIT_EJ)
        count if testPower2>e(CRIT_BECREJ)


Also see

    Bayer, Christian and Christoph Hanck: "Combining Non-Cointegration
             tests", METEOR RM/09/012, University of Maastricht.

Installation Files
        bayerhanck.ado
        NullDistr.dta
        bayerhanck.sthlp