2 resultados para large-sample

em Dalarna University College Electronic Archive


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High-growth firms have received considerable interest recently since they create most of the new jobs in the economy. The purpose of our paper is to investigate the characteristics of high-growth firms prior to their growth period, and whether these characteristics differ across industries. Using data on a large sample of limited liability firms in Sweden for the period 2007-2010, we find that high-growth firms do not have the characteristics that we typically associate with successful firms. On the contrary, our results indicate that high-growth firms have low profits and a weak financial position. This might explain why studies have found that high-growth firms are seldom capable of sustaining their high growth rates in subsequent periods, and thus question policies that are targeted towards these companies.

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The FE ('fixed effects') estimator of technical inefficiency performs poorly when N ('number of firms') is large and T ('number of time observations') is small. We propose estimators of both the firm effects and the inefficiencies, which have small sample gains compared to the traditional FE estimator. The estimators are based on nonparametric kernel regression of unordered variables, which includes the FE estimator as a special case. In terms of global conditional MSE ('mean square error') criterions, it is proved that there are kernel estimators which are efficient to the FE estimators of firm effects and inefficiencies, in finite samples. Monte Carlo simulations supports our theoretical findings and in an empirical example it is shown how the traditional FE estimator and the proposed kernel FE estimator lead to very different conclusions about inefficiency of Indonesian rice farmers.