Time-varying Volatility, Financial Intermediation and Monetary Policy
S. Eickmeier, N. Metiu, Esteban Prieto
IWH Discussion Papers,
No. 19,
2016
Abstract
We document that expansionary monetary policy shocks are less effective at stimulating output and investment in periods of high volatility compared to periods of low volatility, using a regime-switching vector autoregression. Exogenous policy changes are identified by adapting an external instruments approach to the non-linear model. The lower effectiveness of monetary policy can be linked to weaker responses of credit costs, suggesting a financial accelerator mechanism that is weaker in high volatility periods. To rationalize our robust empirical results, we use a macroeconomic model in which financial intermediaries endogenously choose their capital structure. In the model, the leverage choice of banks depends on the volatility of aggregate shocks. In low volatility periods, financial intermediaries lever up, which makes their balance sheets more sensitive to aggregate shocks and the financial accelerator more effective. On the contrary, in high volatility periods, banks decrease leverage, which renders the financial accelerator less effective; this in turn decreases the ability of monetary policy to improve funding conditions and credit supply, and thereby to stimulate the economy. Hence, we provide a novel explanation for the non-linear effects of monetary stimuli observed in the data, linking the effectiveness of monetary policy to the procyclicality of leverage.
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Nested Models and Model Uncertainty
Alexander Kriwoluzky, Christian A. Stoltenberg
Scandinavian Journal of Economics,
No. 2,
2016
Abstract
Uncertainty about the appropriate choice among nested models is a concern for optimal policy when policy prescriptions from those models differ. The standard procedure is to specify a prior over the parameter space, ignoring the special status of submodels (e.g., those resulting from zero restrictions). Following Sims (2008, Journal of Economic Dynamics and Control 32, 2460–2475), we treat nested submodels as probability models, and we formalize a procedure that ensures that submodels are not discarded too easily and do matter for optimal policy. For the United States, we find that optimal policy based on our procedure leads to substantial welfare gains compared to the standard procedure.
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Young, Restless and Creative: Openness to Disruption and Creative Innovations
Daron Acemoglu, Ufuk Akcigit, Murat Alp Celik
NBER Working Paper,
No. 19894,
2015
Abstract
This paper argues that openness to new, unconventional and disruptive ideas has a first-order impact on creative innovations—innovations that break new ground in terms of knowledge creation. After presenting a motivating model focusing on the choice between incremental and radical innovation, and on how managers of different ages and human capital are sorted across different firms with different degrees of openness to disruption, we provide firm-level, patent level and cross-country evidence consistent with this pattern. Our measures of creative innovations proxy for innovation quality (average number of citations per patent) and creativity (fraction of superstar innovators, the likelihood of a very high number of citations, and generality of patents). Our main proxy for openness to disruption is the age of the manager - based on the idea that only companies or societies open to such disruption will allow the young to rise up within the hierarchy. Using this proxy at the firm, patent and country level, we present robust evidence that openness to disruption is associated with more creative innovations, but we also show that once the effect of the sorting of young managers to firms that are more open to disruption is factored in, the (causal) impact of manager age on creative innovations is small.
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Taking the First Step - What Determines German Laser Source Manufacturers' Entry into Innovation Networks?
Jutta Günther, Muhamed Kudic, Andreas Pyka
International Journal of Innovation Management,
No. 5,
2015
Abstract
Early access to technological knowledge embodied in the industry’s innovation network can provide an important competitive advantage to firms. While the literature provides much evidence on the positive effects of innovation networks on firms’ performance, not much is known about the determinants of firms’ initial entry into such networks. We analyze firms’ timing and propensity to enter the industry’s innovation network. More precisely, we seek to shed some light on the factors affecting the duration between firm founding and its first cooperation event. In doing so, we apply a unique longitudinal event history dataset based on the full population of German laser source manufacturers. Innovation network data stem from official databases providing detailed information on the organizations involved, subject of joint research and development (R&D) efforts as well as start and end times for all publically funded R&D projects between 1990 and 2010. Estimation results from a non-parametric event history model indicate that micro firms enter the network later than small-sized or large firms. An in-depth analysis of the size effects for medium-sized firms provides some unexpected findings. The choice of cooperation type makes no significant difference for the firms’ timing to enter the network. Finally, the analysis of geographical determinants shows that cluster membership can, but do not necessarily, affect a firm’s timing to cooperate.
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Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The ex-post threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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Does Proximity Matter in the Choice of Partners in Collaborative R&D Projects? – An Empirical Analysis of Granted Projects in Germany
Mirko Titze, Philipp Marek, , Clemens Fuhrmeister
IWH Discussion Papers,
No. 12,
2014
Abstract
This paper contributes to the discussion on the importance of physical distance in the emergence of cross-region collaborative Research and Development (R&D) interactions. The proximity theory, and its extensions, is used as a theoretical framework. A spatial interaction model for count data was implemented for the empirical analysis of German data from the period from 2005 to 2010. The results show that all tested proximity measurements (geographical, cognitive, social and institutional proximity) have a significant positive influence on collaboration intensity. The proximity paradox, however, cannot be confirmed for geographical, social and institutional proximity, but for cognitive proximity.
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Executive Compensation Structure and Credit Spreads
Stefano Colonnello, Giuliano Curatola, Ngoc Giang Hoang
Abstract
We develop a model of managerial compensation structure and asset risk choice. The model provides predictions about how inside debt features affect the relation between credit spreads and compensation components. First, inside debt reduces credit spreads only if it is unsecured. Second, inside debt exerts important indirect effects on the role of equity incentives: When inside debt is large and unsecured, equity incentives increase credit spreads; When inside debt is small or secured, this effect is weakened or reversed. We test our model on a sample of U.S. public firms with traded CDS contracts, finding evidence supportive of our predictions. To alleviate endogeneity concerns, we also show that our results are robust to using an instrumental variable approach.
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College Choice Allocation Mechanisms: Structural Estimates and Counterfactuals
J.-R. Carvalho, T. Magnac, Qizhou Xiong
Abstract
We evaluate a simple allocation mechanism of students to majors at college entry that was commonly used in universities in Brazil in the 1990s and 2000s. Students first chose a single major and then took exams that select them in or out of the chosen major. The literature analyzing student placement, points out that this decentralized mechanism is not stable and is not strategy-proof. This means that some pairs of major & students can be made better off and that students tend to disguise their preferences using such a mechanism. We build up a model of performance and school choices in which expectations are carefully specified and we estimate it using cross-section data reporting choices between two medical schools and grade performances at the entry exams. Given those estimates, we evaluate changes in selection and students’ expected utilities when other mechanisms are implemented. Results highlight the importance of strategic motives and redistributive effects of changes of the allocation mechanisms.
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A Community College Instructor Like Me: Race and Ethnicity Interactions in the Classroom
Robert W. Fairlie, Florian Hoffmann, Philip Oreopoulos
American Economic Review,
No. 8,
2014
Abstract
Administrative data from a large and diverse community college are used to examine if underrepresented minority students benefit from taking courses with underrepresented minority instructors. To identify racial interactions we estimate models that include both student and classroom fixed effects and focus on students with limited choice in courses. We find that the performance gap in terms of class dropout rates and grade performance between white and underrepresented minority students falls by 20 to 50 percent when taught by an underrepresented minority instructor. We also find these interactions affect longer term outcomes such as subsequent course selection, retention, and degree completion.
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Agglomeration and FDI in East German Knowledge-intensive Business Services
Philipp Marek
Economia Politica,
No. 3,
2012
Abstract
The focus of this article is the empirical identification of factors influencing Foreign Direct Investment (FDI) in the knowledge-intensive business service (KIBS) sector on the regional level of «Raumordnungsregionen» in East Germany. The analysis focuses on the impact of regional agglomeration and technological capability on the location decision of foreign investors and West German MNEs. It shows that localisation, patent activity and the share of employees with an R&D occupation affect significantly the location decision of FDI. This result provides an explanation for the strong concentration of KIBS in urban areas in a post-transition economy.
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