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st: ecm via xtrc
Dear all,
I am using a time series cross-section data set including 465 observations (31 annual observations for 15 countries). My dependent variable is social security transfer (SSTRAN), while covariates are left power (LEFTCUM), unemployment rate (UNEM), percentage of the inactive population (DEPRATIO), and trade openness (TRADE). All these variables seem non-stationary processes. Consequently, I mean to estimate an panel error correction model- So if I estimate this kind of model via a simple OLS procedure, the parameter for the lagged dependent level variable which represents a measure of equilibrium properties, is quite low (-0.014) (see belo the STATA output). nevertheless, if estimate a random coefficient error correction model via xtrc STATA command, the parameter for the lagged dependent level variable increases strongly. In this case it is -0.30 (see STATA output below). A part from the problem of the statistical significance of the coefficient, this implies that the adjus!
tment process among variables is considerably faster.
On the basis of this, I wonder if it is statistically reasonable estimate an error correction model using xtrc STATA command?
Why if one controls causal heterogeneity via a random coefficient model, the adjustment process should be faster than a basic specification?
Thanks a lot in advance for any your help
Best regards
Federico Podest�
. reg dsstran lsstran dleftcum lleftcum dunem lunem ddepratio ldepratio dtrade ltrade
Source | SS df MS Number of obs = 450
-------------+------------------------------ F( 9, 440) = 28.44
Model | 73.1523504 9 8.12803894 Prob > F = 0.0000
Residual | 125.736581 440 .285764957 R-squared = 0.3678
-------------+------------------------------ Adj R-squared = 0.3549
Total | 198.888931 449 .442959758 Root MSE = .53457
------------------------------------------------------------------------------
dsstran | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lsstran | -.0138686 .0075559 -1.84 0.067 -.0287187 .0009816
dleftcum | .0639292 .0739955 0.86 0.388 -.0814992 .2093577
lleftcum | .0013023 .0037215 0.35 0.727 -.0060118 .0086165
dunem | .4283545 .0308134 13.90 0.000 .3677947 .4889143
lunem | -.0295548 .0094424 -3.13 0.002 -.0481127 -.0109969
ddepratio | .135711 .0872411 1.56 0.121 -.0357501 .307172
ldepratio | .0020256 .0103268 0.20 0.845 -.0182705 .0223216
dtrade | -.0188634 .0072521 -2.60 0.010 -.0331164 -.0046104
ltrade | .0024225 .0012992 1.86 0.063 -.000131 .004976
_cons | .3227546 .3836955 0.84 0.401 -.4313491 1.076858
. xtrc dsstran lsstran dleftcum lleftcum dunem lunem ddepratio ldepratio dtrade ltrade
Random-coefficients regression Number of obs = 450
Group variable: cc Number of groups = 15
Obs per group: min = 30
avg = 30.0
max = 30
Wald chi2(9) = 113.31
Prob > chi2 = 0.0000
------------------------------------------------------------------------------
dsstran | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lsstran | -.292154 .084294 -3.47 0.001 -.4573672 -.1269408
dleftcum | .1335818 .290957 0.46 0.646 -.4366835 .703847
lleftcum | .1331348 .1545065 0.86 0.389 -.1696924 .435962
dunem | .5189076 .1106382 4.69 0.000 .3020606 .7357545
lunem | .0747792 .0588429 1.27 0.204 -.0405507 .1901092
ddepratio | .0108599 .3924658 0.03 0.978 -.758359 .7800788
ldepratio | -.0173162 .0737059 -0.23 0.814 -.1617772 .1271448
dtrade | -.0033899 .0184207 -0.18 0.854 -.0394938 .032714
ltrade | .0043399 .01749 0.25 0.804 -.0299398 .0386197
_cons | 2.725573 2.747165 0.99 0.321 -2.658771 8.109917
------------------------------------------------------------------------------
Test of parameter constancy: chi2(140) = 353.60 Prob > chi2 = 0.0000
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