Dear all,
I could solve the problem somehow.
Till next time,
Joe
From: "joe jacob" <[email protected]>
Reply-To: [email protected]
To: [email protected]
Subject: st: Regressor with too many zeros
Date: Thu, 18 Sep 2003 05:36:44 +0000
Dear all,
I am estimating the effect of export orientation (exports/sales) by firms
on their learning efforts from imported inputs. I use an -ivtobit- model
due to the censored natured of the learning variable and the endogenous
nature of the export intensity variable. There are a few other explanatory
variables also, which capture the level of skill and technological
capability of the firm. The problem is that there are a large number of
non-exporting firms (hence a zero value for many observations of the export
intensity variable). My estimation did not produce a significant positive
relation between export intensity and learning. BUT when I added an export
dummy (1 for exporters and 0 for non-exporters) the variable became highly
significant. (both export intensity and export dummy were instrumented)
However, the export dummy became significant Negatively with a higher
coefficient value. This latter result seems very counter-intuitive.
Put simply, the question is thus, when the key regressor has too many zeros
(as my export intensity variable has), how do we tackle the problem of
non-variability? Simply excluding zero-valued observations don't seem
right?
Please comment.
Thanks,
Joe
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