Ruben Dewitte, Michel Dumont, Glenn Rayp, Peter Willemé
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引用次数: 0
Abstract
Finding a good parametric approximation to the productivity distribution is a problem of general interest. This paper argues that heterogeneity in productivity is best captured by finite mixture models (FMMs). FMMs build on the existence of unobserved subpopulations in the data. As such, they are generally consistent with models of firm dynamics differing between groups of firms and allow for a very flexible distribution fit. Relative to commonly used parametric alternatives, we find that FMMs are the only distributions able to provide a sufficiently good fit to the data. A gains from trade exercise with Portuguese data reveals that only FMMs approximate the “true” gains reasonably well.
期刊介绍:
The Canadian Journal of Economics (CJE) is the journal of the Canadian Economics Association (CEA) and is the primary academic economics journal based in Canada. The editors seek to maintain and enhance the position of the CJE as a major, internationally recognized journal and are very receptive to high-quality papers on any economics topic from any source. In addition, the editors recognize the Journal"s role as an important outlet for high-quality empirical papers about the Canadian economy and about Canadian policy issues.