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on Network Economics |
| By: | Bayer, Péter |
| Abstract: | This paper studies the evolution of behavior governing strategic network formation. I first propose a general framework of evolutionary selection in non-cooperative games played in heterogeneous groups under assortative matching. I show that evolution selects strate-gies that (i) execute altruistic actions towards others in the interaction group with rate of altruism equal to the rate of assortative matching and (ii) are stable against pairwise coali-tional deviations under two qualifications: pairs successfully coordinate their deviations with probability equaling the rate of assortative matching and externalities are taken into account with the same weight. I then restrict the domain of interaction games to strategic network formation and define a new stability concept for networks called ‘evolutionarily stable networks’. The concept fuses ideas of solution concepts used by evolutionary game theory and network formation games. In a game of communication, evolutionarily stable networks prescribe equal information access. In the classic co-authorship game only the least efficient network, the complete network, is evolutionarily stable. Finally, I present an evolutionary model of homophilistic network formation between identity groups and show that extreme high degrees of homophily may persist even in groups with virtually no preference for it; thus societies may struggle to eliminate segregation between identity groups despite becoming increasingly tolerant. |
| Keywords: | Networks; evolution; relatedness; stability, homophily |
| JEL: | C73 D85 |
| Date: | 2023–11–20 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:128722&r=net |
| By: | Haoge Chang |
| Abstract: | This paper considers the estimation of treatment effects in randomized experiments with complex experimental designs, including cases with interference between units. We develop a design-based estimation theory for arbitrary experimental designs. Our theory facilitates the analysis of many design-estimator pairs that researchers commonly employ in practice and provide procedures to consistently estimate asymptotic variance bounds. We propose new classes of estimators with favorable asymptotic properties from a design-based point of view. In addition, we propose a scalar measure of experimental complexity which can be linked to the design-based variance of the estimators. We demonstrate the performance of our estimators using simulated datasets based on an actual network experiment studying the effect of social networks on insurance adoptions. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06891&r=net |
| By: | Jonathan Chassot; Michael Creel |
| Abstract: | We propose a method to estimate model parameters using temporal convolutional networks (TCNs). By training the TCN on simulated data, we learn the mapping from sample data to the model parameters that were used to generate this data. This mapping can then be used to define exactly identifying moment conditions for the method of simulated moments (MSM) in a purely data-driven manner, alleviating a researcher from the need to specify and select moment conditions. Using several test models, we show by example that this proposal can outperform the maximum likelihood estimator, according to several metrics, for small and moderate sample sizes, and that this result is not simply due to bias correction. To illustrate our proposed method, we apply it to estimate a jump-diffusion model for a financial series. |
| Keywords: | temporal convolutional networks, method of simulated moments, jump-diffusion stochastic volatility |
| JEL: | C15 C45 C53 C58 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:bge:wpaper:1412&r=net |
| By: | Antonin Bergeaud; Arthur Guillouzouic |
| Abstract: | Following Bergeaud et al. (2022), we construct a new measure of proximity between industrial sectors and public research laboratories. Using this measure, we explore the underlying network of knowledge linkages between scientific fields and industrial sectors in France. We show empirically that there exists a significant negative correlation between the geographical distance between firms and laboratories and their scientific proximity, suggesting strongly localized spillovers. Moreover, we uncover some important differences by field, stronger than when using standard patent-based measures of proximity. |
| Keywords: | knowledge spillovers, technological distance, public laboratories |
| Date: | 2023–11–15 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1961&r=net |
| By: | Alexej Brauer |
| Abstract: | Currently, there is a lot of research in the field of neural networks for non-life insurance pricing. The usual goal is to improve the predictive power via neural networks while building upon the generalized linear model, which is the current industry standard. Our paper contributes to this current journey via novel methods to enhance actuarial non-life models with transformer models for tabular data. We build here upon the foundation laid out by the combined actuarial neural network as well as the localGLMnet and enhance those models via the feature tokenizer transformer. The manuscript demonstrates the performance of the proposed methods on a real-world claim frequency dataset and compares them with several benchmark models such as generalized linear models, feed-forward neural networks, combined actuarial neural networks, LocalGLMnet, and pure feature tokenizer transformer. The paper shows that the new methods can achieve better results than the benchmark models while preserving certain generalized linear model advantages. The paper also discusses the practical implications and challenges of applying transformer models in actuarial settings. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.07597&r=net |
| By: | Moritz Scherrmann; Ralf Elsas |
| Abstract: | Firm disclosures about future prospects are crucial for corporate valuation and compliance with global regulations, such as the EU's MAR and the US's SEC Rule 10b-5 and RegFD. To comply with disclosure obligations, issuers must identify nonpublic information with potential material impact on security prices as only new, relevant and unexpected information materially affects prices in efficient markets. Financial analysts, assumed to represent public knowledge on firms' earnings prospects, face limitations in offering comprehensive coverage and unbiased estimates. This study develops a neural network to forecast future firm earnings, using four decades of financial data, addressing analysts' coverage gaps and potentially revealing hidden insights. The model avoids selectivity and survivorship biases as it allows for missing data. Furthermore, the model is able to produce both fiscal-year-end and quarterly earnings predictions. Its performance surpasses benchmark models from the academic literature by a wide margin and outperforms analysts' forecasts for fiscal-year-end earnings predictions. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.10756&r=net |