An Exploratory Analysis of the Profitability of Small and Medium Firms Using Panel Data: The Case of the Greater Bucharest Metropolitan Area

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• 作者：Călin Vâ ; lsan^a ; Elena Druică^b ; ^{elena.druica@faa.unibuc.ro" class="auth_mail" title="E-mail the corresponding author} ; Radu-Daniel Pintilii^b
• 关键词：prediction model ; profits ; Romanian small and medium firms
• 刊名：Procedia Environmental Sciences
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：32
• 期：Complete
• 页码：337-343
• 全文大小：223 K

文摘

This study attempts to predict aggregate profits for small and medium Romanian firms using a relatively naïve model We use a dataset consisting of 4,519 observations spanning a period of eleven years, from 2001 to 2011 Each observation is obtained by aggregating the data associated with all small and medium firms that can be found for a given NACE and SIRUTA code in the greater Bucharest metropolitan area Our sample includes a number of more than 1,514 observations that correspond to firms with aggregate zero turnover and aggregate zero number of employees These are in fact shell companies, firms that are inactive, but somehow remained in the evidence of the Romanian Trade Register Office We split our sample into two distinct periods, using the 2008 financial crisis as the dividing point We fit a simple prediction model of aggregate total profits as a function of four variables, using the pre-financial crisis period We test the predictions of our model using the post-crisis period The results are imparting three important lessons First, by allowing shell companies in our sample, the prediction accuracy of our model appears to weaken Many surveys and economic policy studies conducted by the Romanian government take into account all companies in the evidence of the Trade Register Office, whether active or not We thus strongly recommend that policy initiatives be based solely on statistical surveys that include only firms in operation Second, we do not need very detailed information, a large number of explanatory variables, or a very sophisticated model in order to achieve a good prediction power Using only four variables, our naïve prediction model boasts an impressive out-of-sample R-square of almost 62% Third, the 2008 financial crisis that wreaked havoc in Western Europe and North America, represented a true tipping point for the economy of the greater Bucharest metropolitan area as well.