Hi,
I have tried modelling my regression into AR models with different lags, as far as I am concerned, the ADF tests if the lagged component of the dependant variable is equal to one (unit root), and in my first regression, I could see that L1=0.6435 and it is statistically significant, which clearly means that there is no unit root, is this equivalent to another form of 'ADF' test too?
Another interesting fact is that my results become insignificant when the fourth lag is included, why is that so? What is the implication behind it?
I have tried modelling my regression into AR models with different lags, as far as I am concerned, the ADF tests if the lagged component of the dependant variable is equal to one (unit root), and in my first regression, I could see that L1=0.6435 and it is statistically significant, which clearly means that there is no unit root, is this equivalent to another form of 'ADF' test too?
Another interesting fact is that my results become insignificant when the fourth lag is included, why is that so? What is the implication behind it?
Code:
. reg lkk L.lkk
Source | SS df MS Number of obs = 1,878
-------------+---------------------------------- F(1, 1876) = 1347.72
Model | 683.656533 1 683.656533 Prob > F = 0.0000
Residual | 951.635002 1,876 .507268125 R-squared = 0.4181
-------------+---------------------------------- Adj R-squared = 0.4178
Total | 1635.29153 1,877 .871226177 Root MSE = .71223
------------------------------------------------------------------------------
lkk | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lkk |
L1. | .6435107 .0175289 36.71 0.000 .6091325 .677889
|
_cons | .5626007 .0330171 17.04 0.000 .4978467 .6273547
------------------------------------------------------------------------------
. reg lkk L.lkk L2.lkk
Source | SS df MS Number of obs = 1,408
-------------+---------------------------------- F(2, 1405) = 584.82
Model | 540.138103 2 270.069051 Prob > F = 0.0000
Residual | 648.822563 1,405 .461795419 R-squared = 0.4543
-------------+---------------------------------- Adj R-squared = 0.4535
Total | 1188.96067 1,407 .845032456 Root MSE = .67956
------------------------------------------------------------------------------
lkk | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lkk |
L1. | .5376675 .025 21.51 0.000 .4886262 .5867089
L2. | .1836253 .0248365 7.39 0.000 .1349046 .232346
|
_cons | .4363539 .0395376 11.04 0.000 .3587947 .513913
------------------------------------------------------------------------------
. reg lkk L(1/3).lkk
Source | SS df MS Number of obs = 938
-------------+---------------------------------- F(3, 934) = 294.93
Model | 414.185358 3 138.061786 Prob > F = 0.0000
Residual | 437.217421 934 .468112871 R-squared = 0.4865
-------------+---------------------------------- Adj R-squared = 0.4848
Total | 851.402779 937 .908647577 Root MSE = .68419
------------------------------------------------------------------------------
lkk | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lkk |
L1. | .5596522 .0327283 17.10 0.000 .4954228 .6238816
L2. | .1607176 .034937 4.60 0.000 .0921535 .2292817
L3. | .0930933 .0301917 3.08 0.002 .0338419 .1523446
|
_cons | .3121269 .0510917 6.11 0.000 .211859 .4123947
------------------------------------------------------------------------------
. reg lkk L(1/4).lkk
Source | SS df MS Number of obs = 469
-------------+---------------------------------- F(4, 464) = 124.46
Model | 221.002183 4 55.2505458 Prob > F = 0.0000
Residual | 205.982645 464 .443928115 R-squared = 0.5176
-------------+---------------------------------- Adj R-squared = 0.5134
Total | 426.984829 468 .912360745 Root MSE = .66628
------------------------------------------------------------------------------
lkk | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lkk |
L1. | .6316919 .0442414 14.28 0.000 .5447535 .7186303
L2. | .0957735 .0523239 1.83 0.068 -.0070477 .1985946
L3. | .0360217 .0465455 0.77 0.439 -.0554444 .1274879
L4. | .0182292 .0413091 0.44 0.659 -.0629469 .0994053
|
_cons | .3351893 .0729343 4.60 0.000 .1918668 .4785118
------------------------------------------------------------------------------
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