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fluctuations in R&D activities. Thus, we apply the DEA/window model to carry out panel data analysis. The DEA/window model was proposed by Klopp (as cited in Cooper et al. ( 2011 )) and is a representative model that is utilized in time-dependent situations

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endogeneity bias since the unobserved panel-level effects can be correlated with the lags of the dependent variable. One can use the dynamic panel data system estimator developed by Arellano-Bover/Blundell-Bond, using GMM estimation on first differenced

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Acta Oeconomica
Authors: GheorghiŢa Dincă, Marius Sorin Dincă, and Maria LetiŢia Andronic

). Varabyova – Schreyögg (2013) performed panel-data analysis and found that higher health expenditures influence the efficiency in the hospital sector. Cheng – Zervopoulos (2014) used a generalised directional distance function to measure health systems

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pushes the productive sector. Chaves and Moro ( 2007 ) investigated the pattern of S&T interplay for both the national system of innovation (NSI) and the health innovation system. The panel data and simultaneous equations models analyze the interaction

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innovation and market value: Evidence from Japanese firm level panel data ( IIR Working Paper, WP#05 - 06 ) . Tokyo : Institute of Innovation Research, Hitotsubashi University . Nanba , H. , Fujii , A

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Acta Oeconomica
Authors: Marija Petrović-Ranđelović, Tamara Rađenović, Bojan Krstić, and Vladimir Mićić

– Botrić (2006) applied the panel data estimation method to identify the main FDI determinants in the seven South Eastern European (SEE) countries in the period of 1996–2002. They found that the FDI inflows in these countries are determined by the

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“reforms” prominent in the EU – IMF agenda. Slow recovery throughout the EMU and a still less favourable economic outlook for countries like Greece or Italy, however, challenge this approach and point to the need of empirical insight. Relying on panel data

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Econometrics , 12 ( 4 ), 357 – 392 . doi: 10.1002/(sici)1099-1255(199707)12:4<357::aid-jae441>3.0.co;2-t. Levin , AT , Lin , C-F , Chu , C-S 2002 Unit root tests in panel data: Asymptotic and finite

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change . Cambridge, MA : Harvard University Press . Peters , B 2009 Persistence of innovation: Stylized facts and panel data evidence . Journal of Technology Transfer 34 2 226

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their average colleague outperforms them (so that they maximise learning)? This cannot be answered with a cross-section (as used in this article); panel data are required to separate cause and effect. Is the ranking of fragile schools (according to the

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