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From |
DE SOUZA Eric <eric.de_souza@coleurope.eu> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: F test on VECM |

Date |
Fri, 8 Apr 2011 10:45:52 +0200 |

I forgot to add that in the second case with three variables one can test the restrictions in question: constraint 1 [_ce1]ln_se = -1 constraint 2 [_ce1]ln_ne = 1 It is only in the first case with two variables that the eror message occurs Eric -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of DE SOUZA Eric Sent: 08 April 2011 10:15 To: statalist@hsphsun2.harvard.edu Subject: RE: st: F test on VECM Normally the following should work (but it does not in this case, see below why): webuse rdinc vec ln_ne ln_se constraint 1 [_ce1]ln_se = -1 constraint 2 [_ce1]ln_ne = 1 vec ln_ne ln_se, bconstraints(1/2) vec ln_ne ln_se ln_sw vec ln_ne ln_se ln_sw, bconstraints(1 2) The errror message one gets is the following: there are at least as many constraints as parameters This is a weakness of the program: it should go ahead and estimate and produce the likelihood ratio test. Remember that without constraints, the beta coefficients are not identified. -vecrank- automatically imposes identification restrictions in order to able to estimate the model, what it calls the Johansen restrictions. If the restrictions you wish to test are not constraining, then the maximum value of the likelihood function will be the same for both models. In this case, you cannot test the restrictions. If the restrictions are constraining, you should always get a likelhood ratio test of the restrictions. The following is the output (edited for length) from PcGive (OxMetrics) The last line gives you the likelihood ratio test. The null is not rejected SYS( 2) Cointegrated VAR (using rdinc.xls) The estimation sample is: 1950 - 2002 Cointegrated VAR (2) in: [0] = ln_ne [1] = ln_se [2] = ln_sw Unrestricted variables: [0] = Constant Number of lags used in the analysis: 2 beta ln_ne 1.0000 ln_se -0.98233 ln_sw 0.037982 alpha ln_ne -0.44735 ln_se -0.36762 ln_sw -0.35322 . . . . log-likelihood 465.501631 - beta is not identified No restrictions imposed SYS( 3) Cointegrated VAR (using rdinc.xls) The estimation sample is: 1950 - 2002 Cointegrated VAR (2) in: [0] = ln_ne [1] = ln_se [2] = ln_sw Unrestricted variables: [0] = Constant Number of lags used in the analysis: 2 General cointegration restrictions: &3=1; &4=-1; beta ln_ne 1.0000 ln_se -1.0000 ln_sw 0.056902 . . . log-likelihood 465.500467 no. long-run restrictions 1 beta is identified LR test of restrictions: Chi^2(1) =0.0023272 [0.9615] In fact, this is a bad example because there is no cointegration, but it suffices for the purpose here Eric de Souza College of Europe Brugge (Bruges), Belgium http://www.coleurope.eu -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nat Tharnpanich Sent: 07 April 2011 23:08 To: statalist@hsphsun2.harvard.edu Subject: Re: st: F test on VECM Thanks so much Charles. However, I am afraid that this is not what I wanted. I want to do the F test on the cointegrating vector itself. For example, based on your online data, I want to test whether the estimated coefficient of ln_se which takes a value of -0.94 when ln_ne is constrained to be 1 is statistically different from, say, -1. Do you happen to know how to do that? Nat On Apr 7 2011, Charles Koss wrote: >you may try this: > >clear >webuse rdinc >vec ln_ne ln_se >test [D_ln_se]L._ce1 == 0 > >test [reference to the equation name ].{reference to the parameter} == >0 > >did it work? > >Charles > > -- Nat Tharnpanich Downing College and Department of Land Economy University of Cambridge CB2 1DQ nt289@cam.ac.uk * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: F test on VECM***From:*Charles Koss <hqtiger@gmail.com>

**References**:**st: rolling regression and hypothesis testing***From:*Nat Tharnpanich <nt289@cam.ac.uk>

**st: RE: rolling regression and hypothesis testing***From:*Nick Cox <n.j.cox@durham.ac.uk>

**Re: st: RE: rolling regression and hypothesis testing***From:*Nat Tharnpanich <nt289@cam.ac.uk>

**st: F test on VECM***From:*Nat Tharnpanich <nt289@cam.ac.uk>

**Re: st: F test on VECM***From:*Charles Koss <hqtiger@gmail.com>

**Re: st: F test on VECM***From:*Nat Tharnpanich <nt289@cam.ac.uk>

**RE: st: F test on VECM***From:*DE SOUZA Eric <eric.de_souza@coleurope.eu>

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