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Re: st: unconventional lag length in VAR model?


From   Robert A Yaffee <bob.yaffee@nyu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: unconventional lag length in VAR model?
Date   Tue, 5 Mar 2013 15:48:44 -0500

Jason,
  You'll be hard pressed to conduct many of the multivariate tests
with uneven lag structures.
     -   Bob


On Tue, Mar 5, 2013 at 3:37 PM, Jason R Franken <JR-Franken@wiu.edu> wrote:
> Bob,
>
> Thanks for the suggestions.  It doesn't seem that varsoc, varlmar, and varwle are able to investigate as complex of lag structures as the procedures described in Hsiao (1979) [http://www.jstor.org/stable/pdfplus/2286972.pdf?acceptTC=true] or Kaylen (1988) [http://www.jstor.org/stable/pdfplus/1241509.pdf].
>
> The varwle procedure is a little less restrictive in that it allows the lag structure to differ across each equation of the VAR, but it implies the same lag structure for each variable within an equation.  I want to allow for the possibility that different lags should be excluded for each variable within an equation.
>
> If anyone can offer further insights/suggestions it would be appreciated.
>
> Jason
>
> ----- Original Message -----
> From: "Robert A Yaffee" <bob.yaffee@nyu.edu>
> To: statalist@hsphsun2.harvard.edu
> Sent: Monday, March 4, 2013 12:30:10 PM
> Subject: Re: st: unconventional lag length in VAR model?
>
> Jason,
>    The varsoc, varlmar, and varwle  are generally used for this purpose.
>      Bob  Yaffee
>
>
> On Fri, Mar 1, 2013 at 1:48 PM, Jason R Franken <JR-Franken@wiu.edu> wrote:
>> I want to determine the appropriate lag structure for a VAR of 3 price series - C, F1, and E. A prior study used these variables and determined the structure using Akaiki's Final Prediction Error (FPE), which can be obtained with the  below commands.
>>
>> My problem is that the prior study was able to ascertain how the lag length differed for each variable and in each equation (that is 4 lags of each variable in each equation might not be appropriate), and I'm not sure how to investigate this with the below commands. Specifically, the prior study finds (for a shorter time period) that the F1 equation has lag 1 of F1 and lags 1 and 2 of C; the E equation has only lags 1 through 4 of C; and the C equation has only lag1 of F1.
>>
>> Can I examine this by estimating a VAR with commands for seemingly unrelated regression (reg3, sur; suest; sureg)?
>>
>> Thanks in advance,
>> Jason Franken
>>
>> RESULTS:
>> .  var C F1 E, lags(1/4)
>>
>> Vector autoregression
>>
>> Sample:  1976q1   2010q3                           No. of obs      =       139
>> Log likelihood =  -1096.43                         AIC             =  16.33712
>> FPE            =  2502.717                         HQIC            =   16.6717
>> Det(Sigma_ml)  =  1425.583                         SBIC            =  17.16046
>>
>> Equation           Parms      RMSE     R-sq      chi2     P>chi2
>> ----------------------------------------------------------------
>> C                    13     5.20584   0.5664   181.5755   0.0000
>> F1                   13     4.50839   0.6138   220.9116   0.0000
>> E                    13     3.64389   0.7331   381.8178   0.0000
>> ----------------------------------------------------------------
>> ------------------------------------------------------------------------------
>>              |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
>> -------------+----------------------------------------------------------------
>> C            |
>>            C |
>>          L1. |   .8522103   .1119234     7.61   0.000     .6328444    1.071576
>>          L2. |  -.2105517   .1371686    -1.53   0.125    -.4793972    .0582938
>>          L3. |   .4614619   .1400664     3.29   0.001     .1869368     .735987
>>          L4. |  -.0850031   .1404678    -0.61   0.545     -.360315    .1903088
>>           F1 |
>>          L1. |   .1405764    .136883     1.03   0.304    -.1277093    .4088621
>>          L2. |  -.3039644   .1454131    -2.09   0.037    -.5889687     -.01896
>>          L3. |   .0981597   .1456564     0.67   0.500    -.1873216     .383641
>>          L4. |   .0081719   .1410511     0.06   0.954    -.2682833     .284627
>>            E |
>>          L1. |  -.1111197   .1855016    -0.60   0.549    -.4746961    .2524566
>>          L2. |   .0254513   .1867506     0.14   0.892    -.3405732    .3914758
>>          L3. |  -.2986691   .1875468    -1.59   0.111    -.6662541     .068916
>>          L4. |   .0016383   .1617178     0.01   0.992    -.3153227    .3185994
>>        _cons |    18.8637   4.441675     4.25   0.000     10.15818    27.56923
>> -------------+----------------------------------------------------------------
>> F1           |
>>            C |
>>          L1. |    .728714   .0969286     7.52   0.000     .5387376    .9186905
>>          L2. |   -.221222   .1187916    -1.86   0.063    -.4540491    .0116052
>>          L3. |   .5090438   .1213011     4.20   0.000      .271298    .7467896
>>          L4. |  -.3071865   .1216488    -2.53   0.012    -.5456138   -.0687593
>>           F1 |
>>          L1. |   .3239146   .1185442     2.73   0.006     .0915722    .5562569
>>          L2. |   .0113203   .1259315     0.09   0.928    -.2355008    .2581414
>>          L3. |   .0733694   .1261422     0.58   0.561    -.1738648    .3206036
>>          L4. |   .3028863   .1221539     2.48   0.013      .063469    .5423036
>>            E |
>>          L1. |  -.3247369   .1606491    -2.02   0.043    -.6396034   -.0098704
>>          L2. |   -.225406   .1617309    -1.39   0.163    -.5423927    .0915807
>>          L3. |  -.0965215   .1624204    -0.59   0.552    -.4148596    .2218166
>>          L4. |  -.2100509   .1400518    -1.50   0.134    -.4845473    .0644455
>>        _cons |   19.94553   3.846605     5.19   0.000     12.40632    27.48473
>> -------------+----------------------------------------------------------------
>> E            |
>>            C |
>>          L1. |   .5893907   .0783421     7.52   0.000      .435843    .7429384
>>          L2. |  -.3047364   .0960128    -3.17   0.002     -.492918   -.1165548
>>          L3. |   .3976193   .0980411     4.06   0.000     .2054622    .5897763
>>          L4. |   .0127413   .0983221     0.13   0.897    -.1799665    .2054491
>>           F1 |
>>          L1. |   .4016804   .0958129     4.19   0.000     .2138907    .5894702
>>          L2. |  -.3147535   .1017836    -3.09   0.002    -.5142457   -.1152614
>>          L3. |  -.1558412   .1019539    -1.53   0.126    -.3556671    .0439848
>>          L4. |   .0926355   .0987304     0.94   0.348    -.1008726    .2861435
>>            E |
>>          L1. |   -.023602    .129844    -0.18   0.856    -.2780915    .2308876
>>          L2. |   .2505328   .1307183     1.92   0.055    -.0056704    .5067359
>>          L3. |  -.0338416   .1312756    -0.26   0.797    -.2911371    .2234539
>>          L4. |  -.1952189   .1131963    -1.72   0.085    -.4170795    .0266417
>>        _cons |   12.52217   3.109002     4.03   0.000     6.428634     18.6157
>> ------------------------------------------------------------------------------
>>
>> .   varsoc
>>
>>    Selection order criteria
>>    Sample:  1976q1   2010q3                     Number of obs      =       139
>>   +---------------------------------------------------------------------------+
>>   |lag |    LL      LR      df    p      FPE       AIC      HQIC      SBIC    |
>>   |----+----------------------------------------------------------------------|
>>   |  0 |  -1236.7                      11201.5   17.8374   17.8632   17.9008  |
>>   |  1 | -1146.61  180.18    9  0.000  3488.08   16.6707   16.7736    16.924* |
>>   |  2 | -1125.79  41.648    9  0.000  2942.99   16.5006   16.6807   16.9439  |
>>   |  3 | -1111.73  28.119    9  0.001   2737.7   16.4278   16.6851   17.0611  |
>>   |  4 | -1096.43  30.599*   9  0.000  2502.72*  16.3371*  16.6717*  17.1605  |
>>   +---------------------------------------------------------------------------+
>>    Endogenous:  C F1 E
>>     Exogenous:  _cons
>>
>> *
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>
>
>
> --
> Robert A. Yaffee, Ph.D.
> Research Professor
> Silver School of Social Work
> New York University
>
> Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2009.pdf
>
> CV:  http://homepages.nyu.edu/~ray1/vita.pdf
>
> *
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> *
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-- 
Robert A. Yaffee, Ph.D.
Research Professor
Silver School of Social Work
New York University

Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2009.pdf

CV:  http://homepages.nyu.edu/~ray1/vita.pdf

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/faqs/resources/statalist-faq/
*   http://www.ats.ucla.edu/stat/stata/


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