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Exporting and productivity as part of the growth process: causal evidence from a data-driven structural VAR
This paper introduces a little known category of estimators - Linear Non-Gaussian vector autoregression models that are acyclic or cyclic - imported from the machine learning literature, to revisit a well-known debate. Does exporting increase firm productivity? Or is it only more productive firms that remain in the export market? We focus on a relatively well-studied country (Chile) and on already-exporting firms (i.e. the intensive margin of exporting). We explicitly look at the co-evolution of productivity and growth, and attempt to ascertain both contemporaneous and lagged causal relationships. Our findings suggest that exporting does not have any causal influence on the other variables. Instead, export seems to be determined by other dimensions of firm growth. With respect to learning by exporting (LBE), we find no evidence that export growth causes productivity growth within the period and very little evidence that exporting growth has a causal effect on subsequent TFP growth
A Testability Analysis Framework for Non-Functional Properties
This paper presents background, the basic steps and an example for a
testability analysis framework for non-functional properties
Controlling for Unobserved Confounds in Classification Using Correlational Constraints
As statistical classifiers become integrated into real-world applications, it
is important to consider not only their accuracy but also their robustness to
changes in the data distribution. In this paper, we consider the case where
there is an unobserved confounding variable that influences both the
features and the class variable . When the influence of
changes from training to testing data, we find that the classifier accuracy can
degrade rapidly. In our approach, we assume that we can predict the value of
at training time with some error. The prediction for is then fed to
Pearl's back-door adjustment to build our model. Because of the attenuation
bias caused by measurement error in , standard approaches to controlling for
are ineffective. In response, we propose a method to properly control for
the influence of by first estimating its relationship with the class
variable , then updating predictions for to match that estimated
relationship. By adjusting the influence of , we show that we can build a
model that exceeds competing baselines on accuracy as well as on robustness
over a range of confounding relationships.Comment: 9 page
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