Quantcast
Channel: Statalist
Viewing all articles
Browse latest Browse all 65570

ARDL long-run coefficient

$
0
0
Dear all,

I have a question regarding the calculation of long-run coefficient from an ARDL model.

I would like to estimated the following ARDL model and then test whether yt and x1t are cointegrated by means of bounds testing approach :

Δyt = β0 + Σ βiΔyt-i + ΣγjΔx1t-j + θ0yt-1 + θ1x1t-1 + et

Code:
regress d.y  c  l.d.y  l(0/1).d.x1  l.y  l.x1

From this equation, I know that the long-run coefficient for x1 is -(θ1/ θ0).
(the bounds test say that the two variables of interest are cointegrated)


I would like to know whether this long-run coefficient is equivalent to the long-run coefficient in the corresponding long-run relationship between yt and x1t (the levels model):

yt = β0 + a1x1t + vt (levels model)

Code:
regress y c  x1


When I perfomed the estimates, the two different calculations of the long-run coefficient do not give the same results. I am confused and wonder where I could have made a mistake. Or must a1 not necessarily be equal to -(θ1/ θ0)?

Thank you so much for your help

Here are the Stata results:
Source | SS df MS Number of obs = 36
-------------+------------------------------ F( 5, 30) = 16.94
Model | 63.2903203 5 12.6580641 Prob > F = 0.0000
Residual | 22.4119664 30 .747065547 R-squared = 0.7385
-------------+------------------------------ Adj R-squared = 0.6949
Total | 85.7022867 35 2.44863676 Root MSE = .86433
------------------------------------------------------------------------------
D. |
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
y |
LD. | .2882683 .1202836 2.40 0.023 .0426164 .5339202
x1 |
D1. | -.4332363 .065969 -6.57 0.000 -.567963 -.2985096
LD. | .1881575 .0844541 2.23 0.034 .0156793 .3606358
y |
L1. | -.9508915 .1854258 -5.13 0.000 -1.329582 -.5722014
x1 |
L1. | -.0168967 .0079469 -2.13 0.042 -.0331265 -.0006669
_cons | 2.646874 .6757397 3.92 0.000 1.266829 4.026918




Source | SS df MS Number of obs = 38
-------------+------------------------------ F( 1, 36) = 8.80
Model | 15.5955962 1 15.5955962 Prob > F = 0.0053
Residual | 63.8147884 36 1.77263301 R-squared = 0.1964
-------------+------------------------------ Adj R-squared = 0.1741
Total | 79.4103846 37 2.14622661 Root MSE = 1.3314
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | -.029125 .0098192 -2.97 0.005 -.0490393 -.0092108
_cons | 2.919511 .5483467 5.32 0.000 1.807412 4.03161
------------------------------------------------------------------------------

We can see that -(-.0168967/-.9508915 ) is not equal to -.029125

Viewing all articles
Browse latest Browse all 65570

Trending Articles



<script src="https://jsc.adskeeper.com/r/s/rssing.com.1596347.js" async> </script>