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Seminar of the “D2 Seminar Series” – Florence Center for Data Science
February 3 @ 14:30 - 16:00
Welcome back to the new edition of the D2 Seminar Series of the Florence Center for Data Science!
We are happy to host Nicola Del Sarto from the Department of Economics and Management, University of Florence and Andrea Mercatanti from Department of Statistical Sciences, Sapienza University of Rome.
Speaker: Nicola Del Sarto – Department of Economics and Management, University of Florence
Title:“One size does not fit all. Business models heterogeneity among Internet of Things architecture layers”
Abstract: “The new paradigm known as the Internet of Things (IoT) is expected to have a significant impact on business during the next years, as it leads to the connection of physical objects and the interaction between the digital and physical worlds. While prior literature addressing the business implications arising from this paradigm has largely considered IoT as an integrated technology, in this study we examine different components of IoT and assess whether firms concerned with the development of IoT solutions have adopted original business models to exploit the opportunities offered by the specific IoT architecture layer they operate in. In turn, based on primary survey data collected on a sample of IoT Italy association’s members, we explore different dimensions of the business model and offer a reinterpretation of the business model Canvas framework adapted to the IoT environment. We show that the specificities of each IoT layer require firms to adopt adhoc business models and focus on different dimensions of the business model Canvas. We believe our research provides some important contributions for both academics and practitioners. For the latter, we provide a tool useful for making decisions on how to design the business model for IoT applications.”
Speaker: Andrea Mercatanti – Department of Statistical Sciences, Sapienza University of Rome
Title: “A Regression Discontinuity Design for ordinal running variables: evaluating Central Bank purchases of corporate bonds.” (Joint work with F. Li, T. Makinen, A. Silvestrini)
Abstract: Regression discontinuity (RD) is a widely used quasi-experimental design for causal inference. In the standard RD, the assignment to treatment is determined by a continuous pretreatment variable (i.e., running variable) falling above or below a pre-fixed threshold. Recent applications increasingly feature ordered categorical or ordinal running variables, which pose challenges to RD estimation due to the lack of a meaningful measure of distance. We proposes an RD approach for ordinal running variables under the local randomization framework. The proposal first estimates an ordered probit model for the ordinal running variable. The estimated probability of being assigned to treatment is then adopted as a latent continuous running variable
and used to identify a covariate-balanced subsample around the threshold. Assuming local unconfoundedness of the treatment in the subsample, an estimate of the effect of the program is obtained by employing a weighted estimator of the average treatment effect. Two weighting estimators—overlap weights and ATT weights—as well as their augmented versions are considered. We apply the method to evaluate the causal effects of the corporate sector purchase programme (CSPP) of the European Central Bank, which involves large-scale purchases of securities issued by corporations in the euro area. We find a statistically significant and negative effect of the CSPP on corporate bond spreads at issuance.