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Re: st: F test on VECM


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: F test on VECM
Date   Fri, 8 Apr 2011 12:34:32 +0100

Typo time: you have one "s" too many.

-bconstraints()- not -bconstrainsts()-.

Nick

On Fri, Apr 8, 2011 at 12:21 PM, Charles Koss <hqtiger@gmail.com> wrote:

> I have run this code:
>
> clear
> webuse rdinc
> constraint 1 [_ce1]ln_ne = -1
> constraint 2 [_ce1]ln_se =  1
> vec ln_ne ln_se, bconstrainsts(1/2)
>
> and got error message too: option bconstrainsts() not allowed
>
> Is this the error message that you are refering to?

On Fri, Apr 8, 2011 at 3:45 AM, DE SOUZA Eric

>> 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

DE SOUZA Eric

>> 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

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