Is venture capital a local business? : A test of the proximity and local network hypotheses

Venture capital (VC) investment has long been conceptualized as a local business , in which the VC’s ability to source, syndicate, fund, monitor, and add value to portfolio firms critically depends on their access to knowledge obtained through their ties to the local (i.e., geographically proximate) network. Consistent with the view that local networks matter, existing research confirms that local and geographically distant portfolio firms are sourced, syndicated, funded, and monitored differently. Curiously, emerging research on VC investment practice within the United States finds that distant investments, as measured by “exits” (either initial public offering or merger & acquisition) out-perform local investments. These findings raise important questions about the assumed benefits of local network membership and proximity. To more deeply probe these questions, we contrast the deal structure of cross-border VC investment with domestic VC investment, and contrast the deal structure of cross-border VC investments that include a local partner with those that do not. Evidence from 139,892 rounds of venture capital financing in the period 1980-2009 suggests that cross-border investment practice, in terms of deal sourcing, syndication, and performance indeed change with proximity, but that monitoring practices do not. Further, we find that the inclusion of a local partner in the investment syndicate yields surprisingly few benefits. This evidence, we argue, raises important questions about VC investment practice as well as the ability of firms to capture and lever the presumed benefits of network membership

Characteristics of exporting and non-exporting firms in Austria

In this study we provide detailed evidence on the importance and performance of exporters compared to non-exporters in Austrian manufacturing, based on firm level data. The results are in line with those found in other studies pointing towards the exceptional role of exporting firms with respect to various size and performance measures. We provide both descriptive as well as econometric evidence on these ‘export premia’ along these lines and further present a brief comparison with results found for other countries. Our findings however also suggest the existence of quite large differences across industries with respect to the export premia which deserves further attention.export premium, heterogenous firms, trade

Time Series of Count Data : Modelling and Estimation

This paper compares various models for time series of counts which can account for discreetness, overdispersion and serial correlation. Besides observation- and parameter-driven models based upon corresponding conditional Poisson distributions, we also consider a dynamic ordered probit model as a flexible specification to capture the salient features of time series of counts. For all models, we present appropriate efficient estimation procedures. For parameter-driven specifications this requires Monte Carlo procedures like simulated Maximum likelihood or Markov Chain Monte-Carlo. The methods including corresponding diagnostic tests are illustrated with data on daily admissions for asthma to a single hospital. --Efficient Importance Sampling,GLARMA,Markov Chain Monte-Carlo,Observation-driven model,Parameter-driven model,Ordered Probit

Austrian Exporters: A Firm-Level Analysis

exports, firm heterogeneity, export premium, Austrian manufacturing firms

Interactive Music Generation with Positional Constraints using Anticipation-RNNs

Recurrent Neural Networks (RNNS) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary length. However, their left-to-right generation procedure only allows a limited control from a potential user which makes them unsuitable for interactive and creative usages such as interactive music generation. This paper introduces a novel architecture called Anticipation-RNN which possesses the assets of the RNN-based generative models while allowing to enforce user-defined positional constraints. We demonstrate its efficiency on the task of generating melodies satisfying positional constraints in the style of the soprano parts of the J.S. Bach chorale harmonizations. Sampling using the Anticipation-RNN is of the same order of complexity than sampling from the traditional RNN model. This fast and interactive generation of musical sequences opens ways to devise real-time systems that could be used for creative purposes.Comment: 9 pages, 7 figure