The Drivers of Revenue Productivity: a New Decomposition Analysis with Firm-level Data
Filippo di Mauro, Giordano Mion, Daniel Stöhlker
ECB Working Paper,
No. 2014,
2017
Abstract
This paper aims to derive a methodology to decompose aggregate revenue TFP changes over time into four different components – namely physical TFP, mark-ups, quality and production scale. The new methodology is applied to a panel of EU countries and manufacturing industries over the period 2006-2012. In summary, patterns of measured revenue productivity have been broadly similar across EU countries, most notably when we group them into stressed (Italy, Spain and Slovenia) and non-stressed countries (Belgium, Finland, France and Germany). In particular, measured revenue productivity drops for both groups by about 6 percent during the recent crisis. More specifically, for both stressed and non-stressed countries the drop in revenue productivity was accompanied by a substantial dip in the proxy we use for TFP in quantity terms, as well as by a strong reduction in mark-ups.
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Central Bank Transparency and Cross-border Banking
Stefan Eichler, Helge Littke, Lena Tonzer
Abstract
We analyze the effect of central bank transparency on cross-border bank activities. Based on a panel gravity model for cross-border bank claims for 21 home and 47 destination countries from 1998 to 2010, we find strong empirical evidence that a rise in central bank transparency in the destination country, on average, increases cross-border claims. Using interaction models, we find that the positive effect of central bank transparency on cross-border claims is only significant if the central bank is politically independent. Central bank transparency and credibility are thus considered complements by banks investing abroad.
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Three methods of forecasting currency crises: Which made the run in signaling the South African currency crisis of June 2006?
Tobias Knedlik, Rolf Scheufele
IWH Discussion Papers,
No. 17,
2007
Abstract
In this paper we test the ability of three of the most popular methods to forecast the South African currency crisis of June 2006. In particular we are interested in the out-ofsample performance of these methods. Thus, we choose the latest crisis to conduct an out-of-sample experiment. In sum, the signals approach was not able to forecast the outof- sample crisis of correctly; the probit approach was able to predict the crisis but just with models, that were based on raw data. Employing a Markov-regime-switching approach also allows to predict the out-of-sample crisis. The answer to the question of which method made the run in forecasting the June 2006 currency crisis is: the Markovswitching approach, since it called most of the pre-crisis periods correctly. However, the “victory” is not straightforward. In-sample, the probit models perform remarkably well and it is also able to detect, at least to some extent, out-of-sample currency crises before their occurrence. It can, therefore, not be recommended to focus on one approach only when evaluating the risk for currency crises.
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