One reason for differences in significance between your models has to
do with the number of observations. In your "simple OLS procedure" you
assume that you have 465 independent observations, which is way to
optimistic. For example you can ask me on 10 different days whether or
not I like chocolate, or you can ask 10 different people whether they
like chocolate. In the latter case you have 10 indepedent pieces of
information, but in the former case you don't, and you need to take
that into account. I don't know whether -xtrc- is the best way of doing
that, there are many other models in the -xt- family, see: -help xt-.
-- Maarten
--- "Podesta', Federico" <[email protected]> wrote:
> 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
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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