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 This page is devoted to what I am calling Flexible Productivity
Analysis or FPA. Over the past 40 years, a large number of models have
been developed for productivity/efficiency measurements adding many features
such as panel data, time varying, heterogeneity, endogeniety, system estimation
and so on [see e.g. works by Battese, Coelli, Greene, Kumbhakar, Lovell,
Schmidt, Sickles, Simar, Tsionas, Wilson among others]. There have also been great
efforts to use flexible functional forms for technology or inefficiency
distributions using e.g. kernel smoothing techniques or DEA. FPA refers to a nonparametric
approach to modelling and estimation of efficiency/productivity models based on
lowranked penalized splines. I use the word flexible to describe
several features of the approach: First, using this approach one can estimate semiparametric
version of almost any of the models developed in the past 40 years under a
unified framework. Second, one has control over the degree of flexibility for
each part of the model [anything from parametric to semiparametric to fully
nonparametric]. Third, this combined with the trick of "transformation to normal" can handle even more complex models. Finally, there are some degrees of flexibility in using the
estimation method [Penalized Least Square, ML, Bayesian, Variational Bayes].
Here, I will gradually add papers and computer codes related to Flexible Productivity Analysis .
