Dear professor Sebastian Kripfganz
I hope you are well today
Always thank you for your clear answer
This time I have three question as followings
1.First Q
In your London presentation(2019)p.31 you tald that "In combination with the moment conditions for the differenced model, further lags for the level model are redundant" with roodman(2009), and Arellano and Bover(1995)
Here what is the exact meaning of redundant? does it mean that we do not need to include higher order lags or must not use higer lags additionally ?
2.Second Q
another question is if higher order lags are redundant and it is better to use only one moment contion then instead of using the first zero lag can we use higher lag one. in case of predetermined variable
3. Third Q
Last, if higher lags are redundant, then why including the higer lags affect the estimation results
The folliwings are three case
1.using only first differenced instrument in level equation :gmm(n, lag(1 1) diff model(level))
xtdpdgmm L(0/1).n w k, model(diff) collapse gmm(n, lag(2 4)) gmm(w k, lag(1 3)) gmm(n, lag(1 1) diff model(level)) gmm(w k, lag(0 0) diff model(level)) two vce(r) // p.36
------------------------------------------------------------------------------
| WC-Robust
n | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
n |
L1. | .5117523 .1208484 4.23 0.000 .2748937 .7486109
|
w | -1.323125 .2383451 -5.55 0.000 -1.790273 -.855977
k | .1931365 .0941343 2.05 0.040 .0086367 .3776363
_cons | 4.698425 .7943584 5.91 0.000 3.141511 6.255339
------------------------------------------------------------------------------
2.including higher lagged differenced instruments in level equation :gmm(n, lag(1 3) diff model(level))
xtdpdgmm L(0/1).n w k, model(diff) collapse gmm(n, lag(2 4)) gmm(w k, lag(1 3)) gmm(n, lag(1 3) diff model(level)) gmm(w k, lag(0 0) diff model(level)) two vce(r) // p.36
------------------------------------------------------------------------------
| WC-Robust
n | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
n |
L1. | .4951825 .0955246 5.18 0.000 .3079577 .6824073
|
w | -1.2974 .192342 -6.75 0.000 -1.674383 -.9204167
k | .2078625 .0951428 2.18 0.029 .0213861 .394339
_cons | 4.649591 .5885837 7.90 0.000 3.495989 5.803194
------------------------------------------------------------------------------
3.Replacing with higher lagged differenced instruments in level equation :gmm(n, lag(3 3) diff model(level))
xtdpdgmm L(0/1).n w k, model(diff) collapse gmm(n, lag(2 4)) gmm(w k, lag(1 3)) gmm(n, lag(3 3) diff model(level)) gmm(w k, lag(0 0) diff model(level)) two vce(r) // p.36
------------------------------------------------------------------------------
| WC-Robust
n | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
n |
L1. | .4765705 .0994517 4.79 0.000 .2816488 .6714922
|
w | -1.412922 .2631131 -5.37 0.000 -1.928614 -.8972297
k | .1916385 .1015619 1.89 0.059 -.0074192 .3906962
_cons | 5.016399 .8148121 6.16 0.000 3.419396 6.613401
------------------------------------------------------------------------------
Thank you so much again!!!
I hope you are well today
Always thank you for your clear answer
This time I have three question as followings
1.First Q
In your London presentation(2019)p.31 you tald that "In combination with the moment conditions for the differenced model, further lags for the level model are redundant" with roodman(2009), and Arellano and Bover(1995)
Here what is the exact meaning of redundant? does it mean that we do not need to include higher order lags or must not use higer lags additionally ?
2.Second Q
another question is if higher order lags are redundant and it is better to use only one moment contion then instead of using the first zero lag can we use higher lag one. in case of predetermined variable
3. Third Q
Last, if higher lags are redundant, then why including the higer lags affect the estimation results
The folliwings are three case
1.using only first differenced instrument in level equation :gmm(n, lag(1 1) diff model(level))
xtdpdgmm L(0/1).n w k, model(diff) collapse gmm(n, lag(2 4)) gmm(w k, lag(1 3)) gmm(n, lag(1 1) diff model(level)) gmm(w k, lag(0 0) diff model(level)) two vce(r) // p.36
------------------------------------------------------------------------------
| WC-Robust
n | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
n |
L1. | .5117523 .1208484 4.23 0.000 .2748937 .7486109
|
w | -1.323125 .2383451 -5.55 0.000 -1.790273 -.855977
k | .1931365 .0941343 2.05 0.040 .0086367 .3776363
_cons | 4.698425 .7943584 5.91 0.000 3.141511 6.255339
------------------------------------------------------------------------------
2.including higher lagged differenced instruments in level equation :gmm(n, lag(1 3) diff model(level))
xtdpdgmm L(0/1).n w k, model(diff) collapse gmm(n, lag(2 4)) gmm(w k, lag(1 3)) gmm(n, lag(1 3) diff model(level)) gmm(w k, lag(0 0) diff model(level)) two vce(r) // p.36
------------------------------------------------------------------------------
| WC-Robust
n | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
n |
L1. | .4951825 .0955246 5.18 0.000 .3079577 .6824073
|
w | -1.2974 .192342 -6.75 0.000 -1.674383 -.9204167
k | .2078625 .0951428 2.18 0.029 .0213861 .394339
_cons | 4.649591 .5885837 7.90 0.000 3.495989 5.803194
------------------------------------------------------------------------------
3.Replacing with higher lagged differenced instruments in level equation :gmm(n, lag(3 3) diff model(level))
xtdpdgmm L(0/1).n w k, model(diff) collapse gmm(n, lag(2 4)) gmm(w k, lag(1 3)) gmm(n, lag(3 3) diff model(level)) gmm(w k, lag(0 0) diff model(level)) two vce(r) // p.36
------------------------------------------------------------------------------
| WC-Robust
n | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
n |
L1. | .4765705 .0994517 4.79 0.000 .2816488 .6714922
|
w | -1.412922 .2631131 -5.37 0.000 -1.928614 -.8972297
k | .1916385 .1015619 1.89 0.059 -.0074192 .3906962
_cons | 5.016399 .8148121 6.16 0.000 3.419396 6.613401
------------------------------------------------------------------------------
Thank you so much again!!!
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