The price formation in Non-Walrasian markets is notoriously an open problem. Here we focus on urban housing markets, where the mismatch between supply and demand has important consequences in terms of social welfare. We propose a simple Agent-Based Model (ABM) that explicitly reproduces the market mechanism and which is specifically suited to study issues related to spatial income segregation. We first find the analytical solution of the ABM in some specific cases, shedding light on the structure of the model and on the effect of the parameters. We then simulate the fully-fledged ABM and find that: (i) the market mechanism easily implies income segregation; (ii) an increase of the demand in one part of the city can potentially increase the prices all over the city (in qualitative agreement with the data); (iii) subsidies are more efficient than taxes in mitigating income segregation. These non-trivial results provide an example of the kind of insights that can be gained if one considers bounded rationality, heterogeneity and the potential lack of (Walrasian) equilibrium, as it would have been much less natural to address these issues under more standard assumptions.
Price formation on a housing market and spatial income segregation. (arXiv:1606.00424v1 [q-fin.EC])
Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso. (arXiv:1606.00142v1 [stat.ML])
Model selection is difficult to analyse yet theoretically and empirically important, especially for high-dimensional data analysis. Recently the least absolute shrinkage and selection operator (Lasso) has been applied in the statistical and econometric literature. Consis- tency of Lasso has been established under various conditions, some of which are difficult to verify in practice. In this paper, we study model selection from the perspective of generalization ability, under the framework of structural risk minimization (SRM) and Vapnik-Chervonenkis (VC) theory. The approach emphasizes the balance between the in-sample and out-of-sample fit, which can be achieved by using cross-validation to select a penalty on model complexity. We show that an exact relationship exists between the generalization ability of a model and model selection consistency. By implementing SRM and the VC inequality, we show that Lasso is L2-consistent for model selection under assumptions similar to those imposed on OLS. Furthermore, we derive a probabilistic bound for the distance between the penalized extremum estimator and the extremum estimator without penalty, which is dominated by overfitting. We also propose a new measurement of overfitting, GR2, based on generalization ability, that converges to zero if model selection is consistent. Using simulations, we demonstrate that the proposed CV-Lasso algorithm performs well in terms of model selection and overfitting control.
The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology
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read more...Remarks Before The SEC Historical Society â âThe Continuous Process Of Optimizing The Equity Marketsâ
Thank you, David [Lynn], for that kind introduction and for inviting me to your annual meeting. It is truly my privilege and honor to serve as Chair of the Commission, and it is wonderful to share the room and microphone today with my friend and former Chairman Richard Breeden along with others who, from firsthand experience, deeply appreciate the value of the SEC as a critical institution with a long heritage of protecting investors and our markets. At the Commission, our history informs who we are and the work we do, and it is our great, good fortune that we have the SEC Historical Society to preserve that rich history.
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