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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu I n s t i t u t f ü r W e l t w i r t s c h a f t a n d e r U n i v e r s i t ä t K i e l K i e l I n s t i t u t e f o r t h e W o r l d E c o n o m y Terms of use: Documents in Kiel Institute for World Economics AbstractWe use the Factor-Augmented Vector Autoregression (FAVAR) approach of Bernanke, Boivin and Eliasz (2005) to estimate the effects of monetary policy shocks on wages and employment in the euro area. The use of a large data set comprising country, sectoral and euro area-wide data allows us to better identify common monetary policy shocks in the euro area and their effects on labour market outcomes. At the same time the FAVAR approach gives us estimates of how relative wages and employment in the various countries and sectors respond to these common shocks. The ultimate objective of our work is to relate the estimated cross-country differences in wage and employment responses to differences in labour market institutions and sectoral composition. JEL: E3, E4, J3, J6

We study the bunching identification strategy for an elasticity parameter that summarizes agents' response to changes in slope (kink) or intercept (notch) of a schedule of incentives. A notch identifies the elasticity but a kink does not, when the distribution of agents is fully flexible. We propose new non-parametric and semi-parametric identification assumptions on the distribution of agents that are weaker than assumptions currently made in the literature. We revisit the original empirical application of the bunching estimator and find that our weaker identification assumptions result in meaningfully different estimates. We provide the Stata package bunching to implement our procedures.