Identifying Cooperation for Innovation ― A Comparison of Data Sources
Michael Fritsch, Mirko Titze, Matthias Piontek
Industry and Innovation,
Nr. 6,
2020
Abstract
The value of social network analysis is critically dependent on the comprehensive and reliable identification of actors and their relationships. We compare regional knowledge networks based on different types of data sources, namely, co-patents, co-publications, and publicly subsidized collaborative R&D projects. Moreover, by combining these three data sources, we construct a multilayer network that provides a comprehensive picture of intraregional interactions. By comparing the networks based on the data sources, we address the problems of coverage and selection bias. We observe that using only one data source leads to a severe underestimation of regional knowledge interactions, especially those of private sector firms and independent researchers.
Artikel Lesen
Identifying Cooperation for Innovation – A Comparison of Data Sources
Michael Fritsch, Matthias Piontek, Mirko Titze
Abstract
The value of social network analysis is critically dependent on the comprehensive and reliable identification of actors and their relationships. We compare regional knowledge networks based on different types of data sources, namely, co-patents, co-publications, and publicly subsidised collaborative Research and Development projects. Moreover, by combining these three data sources, we construct a multilayer network that provides a comprehensive picture of intraregional interactions. By comparing the networks based on the data sources, we address the problems of coverage and selection bias. We observe that using only one data source leads to a severe underestimation of regional knowledge interactions, especially those of private sector firms and independent researchers. The key role of universities that connect many regional actors is identified in all three types of data.
Artikel Lesen
For How Long Do IMF Forecasts of World Economic Growth Stay Up-to-date?
Katja Heinisch, Axel Lindner
Applied Economics Letters,
Nr. 3,
2019
Abstract
This study analyses the performance of the International Monetary Fund (IMF) World Economic Outlook output forecasts for the world and for both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current year and the next year, we examine the durability of IMF forecasts, looking at how much time has to pass so that IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time data set for GDP and for indicators, we find that some simple single-indicator forecasts on the basis of data that are available at higher frequency can significantly outperform the IMF forecasts as soon as the publication of the IMF’s Outlook is only a few months old. In particular, there is an obvious gain using leading indicators from January to March for the forecast of the current year.
Artikel Lesen
Outperforming IMF Forecasts by the Use of Leading Indicators
Katja Drechsel, Sebastian Giesen, Axel Lindner
IWH Discussion Papers,
Nr. 4,
2014
Abstract
This study analyzes the performance of the IMF World Economic Outlook forecasts for world output and the aggregates of both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current and the next year, we examine whether IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time dataset for GDP and for the indicators we find that some simple single-indicator forecasts on the basis of data that are available at higher frequency can significantly outperform the IMF forecasts if the publication of the Outlook is only a few months old.
Artikel Lesen
Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Drechsel, Rolf Scheufele
Abstract
This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay. In a simulated out-of-sample experiment we evaluate the different modelling strategies conditional on the given state of information and depending on the model averaging technique. The proposed approach is computationally simple and can be easily implemented as a nowcasting tool. Finally, this method also allows retracing the driving forces of the forecast and hence enables the interpretability of the forecast outcome.
Artikel Lesen
Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Drechsel, Rolf Scheufele
Abstract
This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay.
Artikel Lesen
What Drives Innovation Output from Subsidized R&D Cooperation? — Project-level Evidence from Germany
Michael Schwartz, Michael Fritsch, Jutta Günther, François Peglow
Technovation,
Nr. 6,
2012
Abstract
Using a large dataset of 406 subsidized R&D cooperation projects, we provide detailed insights into the relationship between project characteristics and innovation output. Patent applications and publications are used as measures for the innovation output of an R&D project. We find that large-firm involvement is strongly positively related with the number of patent applications, but not with the number of publications. Conversely, university involvement has positive effects on projects’ innovation output in terms of the number of publications but not in terms of patent applications. In general, projects’ funding as measure of projects’ size is an important predictor of the innovation output of R&D cooperation projects. No significant effects are found for the number of partners as (an alternative) measure of projects’ size, for spatial proximity between cooperation partners, for the involvement of a public institute for applied research, and for prior cooperation experiences. We derive conclusions for the design of R&D cooperation support schemes.
Artikel Lesen
A Systemic View on Knowledge-based Development Metrics
Mirko Titze, Michael Schwartz, Matthias Brachert
International Journal of Knowledge-Based Development,
Nr. 1,
2012
Abstract
Drawing on the systems perspective of innovation processes, this article proposes a conceptual approach for a comprehensive analysis of regional knowledge generation and transfer. Instead of focusing on one single indicator, the approach emphasizes the importance to take multiple channels of knowledge transfer into account. This provides valuable insights into the spatial structure of innovation processes on different levels. We disentangle the innovation process and consider four different layers: i.) publications in peer-reviewed journals, ii.) patent applications, iii.) formal R&D collaboration projects, the iv.) localized input-output relations. Further, we demonstrate the relevance of the „multi-layer approach‟ by applying it empirically to a specific regional innovation system: The Free State of Saxony – a federal state in Germany. We argue that the approach could be a valuable tool to inform policy-makers about knowledge-based regional development strategies.
Artikel Lesen
Macroeconomic Challenges in the Euro Area and the Acceding Countries
Katja Drechsel
Dissertation, Fachbereich Wirtschaftswissenschaften der Universität Osnabrück,
2010
Abstract
deutscher Titel: Makroökonomische Herausforderungen für die Eurozone und die Beitrittskandidaten
Abstract: The conduct of effective economic policy faces a multiplicity of macroeconomic challenges, which requires a wide scope of theoretical and empirical analyses. With a focus on the European Union, this doctoral dissertation consists of two parts which make empirical and methodological contributions to the literature on forecasting real economic activity and on the analysis of business cycles in a boom-bust framework in the light of the EMU enlargement. In the first part, we tackle the problem of publication lags and analyse the role of the information flow in computing short-term forecasts up to one quarter ahead for the euro area GDP and its main components. A huge dataset of monthly indicators is used to estimate simple bridge equations. The individual forecasts are then pooled, using different weighting schemes. To take into consideration the release calendar of each indicator, six forecasts are compiled successively during the quarter. We find that the sequencing of information determines the weight allocated to each block of indicators, especially when the first month of hard data becomes available. This conclusion extends the findings of the recent literature. Moreover, when combining forecasts, two weighting schemes are found to outperform the equal weighting scheme in almost all cases. In the second part, we focus on the potential accession of the new EU Member States in Central and Eastern Europe to the euro area. In contrast to the discussion of Optimum Currency Areas, we follow a non-standard approach for the discussion on abandonment of national currencies the boom-bust theory. We analyse whether evidence for boom-bust cycles is given and draw conclusions whether these countries should join the EMU in the near future. Using a broad range of data sets and empirical methods we document credit market imperfections, comprising asymmetric financing opportunities across sectors, excess foreign currency liabilities and contract enforceability problems both at macro and micro level. Furthermore, we depart from the standard analysis of comovements of business cycles among countries and rather consider long-run and short-run comovements across sectors. While the results differ across countries, we find evidence for credit market imperfections in Central and Eastern Europe and different sectoral reactions to shocks. This gives favour for the assessment of the potential euro accession using this supplementary, non-standard approach.
Artikel Lesen
What Determines the Innovative Success of Subsidized Collaborative R&D Projects? – Project-Level Evidence from Germany –
Michael Schwartz, François Peglow, Michael Fritsch, Jutta Günther
IWH Discussion Papers,
Nr. 7,
2010
publiziert in: Technovation
Abstract
Systemic innovation theory emphasizes that innovations are the result of an interdependent exchange process between different organizations. This is reflected in the current paradigm in European innovation policy, which aims at the support of collaborative R&D and innovation projects bringing together science and industry. Building on a large data set using project-level evidence on 406 subsidized R&D cooperation projects, the present paper provides detailed insights on the relationship between the innovative success of R&D cooperation projects and project characteristics. Patent applications and publications are used as measures for direct outcomes of R&D projects. We also differentiate between academic-industry projects and pure inter-firm projects. Main results of negative binomial regressions are that large-firm involvement is positively related to pa-tent applications, but not to publications. Conversely, university involvement has positive effects on project outcomes in terms of publications but not in terms of patent applications. In general, projects’ funding is an important predictor of innovative success of R&D cooperation projects. No significant results are found for spatial proximity among cooperation partners and for the engagement of an applied research institute. Results are discussed with respect to the design of R&D cooperation support schemes.
Artikel Lesen