Residential relocation in response to light rail transit investment: case study of the Hudson-Bergen Light Rail system

© 2016, The Author(s).It is widely acknowledged that the improved accessibility enabled by investment in public transport services can, under favorable market conditions, impact the local real estate market within the zone of influence of the service’s stations. The motivation for this study is to establish the nature of two such impacts, specifically the spatial and socio-economic patterns of residential relocations that are driven by the new light rail transit (LRT) service. Using empirical data (n = 1,023) from the Hudson–Bergen Light Rail system in New Jersey (US), we report findings regarding the impacts of the introduction of the new LRT service. We investigate two linked dimensions; the first is the distinctive socio-economic profile of LRT passengers who self-report having relocated to the new transit corridor due, at least in part, to the new transit service. The second is their proximity (following their residential relocation) to the new LRT line’s stations. We present a novel analysis that accounts for endogeneity between these two dimensions of residential relocation. Of light rail passengers who engaged in a residential relocation in the 5 years prior to the survey, two-thirds (69 %) indicate that proximity to the light rail service was a ‘somewhat’ or ‘very’ important consideration. Via the multivariate analysis, we demonstrate that small household size, low income, youth (as opposed to older age), and low car ownership are each positively linked, ceteris paribus, with having engaged in a residential relocation motivated by the new transit service. Finally, higher household income is found to be associated with distance (after relocation) to the nearest transit station, which is consistent with bid-rent theory

Prices and volumes of options: A simple theory of risk sharing when markets are incomplete

We present a simple theory of business-cycle movements of option prices and volumes. This theory relies on time-varying heterogeneity between agents in their demand for insurance against aggregate risk. Formally, we build an infinite-horizon model where agents face an aggregate risk, but also different levels of idiosyncratic risk. We manage to characterize analytically a general equilibrium in which positive quantities of derivatives are traded. This allows us to explain the informational content of derivative volumes over the business cycle. We also carry out welfare analysis with respect to the introduction of options, which appears not to be Pareto-improving.Option Pricing, Open Interest, Incomplete Markets.

ASAP : towards accurate, stable and accelerative penetrating-rank estimation on large graphs

Pervasive web applications increasingly require a measure of similarity among objects. Penetrating-Rank (P-Rank) has been one of the promising link-based similarity metrics as it provides a comprehensive way of jointly encoding both incoming and outgoing links into computation for emerging applications. In this paper, we investigate P-Rank efficiency problem that encompasses its accuracy, stability and computational time. (1) We provide an accuracy estimate for iteratively computing P-Rank. A symmetric problem is to find the iteration number K needed for achieving a given accuracy ε. (2) We also analyze the stability of P-Rank, by showing that small choices of the damping factors would make P-Rank more stable and well-conditioned. (3) For undirected graphs, we also explicitly characterize the P-Rank solution in terms of matrices. This results in a novel non-iterative algorithm, termed ASAP , for efficiently computing P-Rank, which improves the CPU time from O(n 4) to O( n 3 ). Using real and synthetic data, we empirically verify the effectiveness and efficiency of our approaches

Incomplete markets, liquidation risk, and the term structure of interest rates

We analyze the term structure of real interest rates in a general equilibrium model with incomplete markets and borrowing constraints. Agents are subject to both aggregate and idiosyncratic income shocks, which latter may force them into early portfolio liquidation in a bad aggregate state. We derive a closed-form equilibrium with limited agent heterogeneity (despite market incompleteness), which allows us to produce analytical expressions for bond prices and returns at any maturity. The attractiveness of bonds as liquidity makes aggregate bond demand downward-sloping, so that greater bond supply raises both the level and the slope of the yield curve. Moreover, time-variations in liquidation risk are shown to help explain the rejection of the Expectations Hypothesis.Incomplete markets; yield curve; borrowing constraints.

Generalized Firefly Algorithm for optimal transmit beamforming

This paper proposes a generalized Firefly Algorithm (FA) to solve an optimization framework having objective function and constraints as multivariate functions of independent optimization variables. Four representative examples of how the proposed generalized FA can be adopted to solve downlink beamforming problems are shown for a classic transmit beamforming, cognitive beamforming, reconfigurable-intelligent-surfaces-aided (RIS-aided) transmit beamforming, and RIS-aided wireless power transfer (WPT). Complexity analyzes indicate that in large-antenna regimes the proposed FA approaches require less computational complexity than their corresponding interior point methods (IPMs) do, yet demand a higher complexity than the iterative and the successive convex approximation (SCA) approaches do. Simulation results reveal that the proposed FA attains the same global optimal solution as that of the IPM for an optimization problem in cognitive beamforming. On the other hand, the proposed FA approaches outperform the iterative, IPM and SCA in terms of obtaining better solution for optimization problems, respectively, for a classic transmit beamforming, RIS-aided transmit beamforming and RIS-aided WPT

Firefly algorithm for beamforming design in RIS-aided communication systems

This paper studies a non-convex power minimization problem for reconfigurable-intelligent-surfaces-aided communication systems whose constraints are multivariate functions of two independent optimization variables, i.e., active and passive beamforming vectors. A widely adopted alternative optimization (AO) approach approximates the originally non-convex problem by two convex sub-optimization problems where each sub-optimization problem deals with one variable considering the other variable as a constant. The solution for the original problem is obtained by iteratively solving these sub-optimization problems. Although the AO approach converts the original NP-hard optimization problem to two convex sub-problems, the solutions attained by this method may not be the global optimal solution due to the approximation process as well as the inherent non-convexity of the original problem. To overcome the issue, this paper adopts a nature-inspired optimization approach and introduces a novel Firefly algorithm (FA) to simultaneously solve for two independent optimization variables of the originally non- convex optimization problem. Computational complexity analyses are provided for the proposed FA and the AO approaches. Simulation results reveal that the proposed FA approach prevails its AO counterpart in obtaining a better solution for the under- studied optimization problem with the same order of computational complexity