3,298 research outputs found

    Estimating Estate-Specific Price-to-Rent Ratios in Shanghai and Shenzhen: A Bayesian Approach

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    The price-to-rent ratio, a common yardstick for the value of housing, is difficult to estimate when rental properties are poor substitutes of owner-occupied homes. In this study, we estimate price-to-rent ratios of residential properties in two major cities in China, where urban high-rises (estates) comprise both rental and owner-occupied units. We conduct Bayesian inference on estate-specific parameters by using information of rental units to elicit priors of the unobserved rents of units sold in the same estate. We find that the price-to-rent ratios tend to be higher for low-end properties. We discuss economic explanations for the phenomenon and the policy implications.

    Asymmetric Monetary Policy in Australia

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    We find evidence for asymmetric behaviour in Australian monetary policy. During 1984-1990, the Reserve Bank of Australia acted with considerable discretion yielding poor performance of an interest rate rule. However it behaved asymmetrically to inflation and the output gap in downturns and upturns. On embracing inflation targeting from 1991, it enhanced its credibility by anchoring inflation expectations. Not only did its actions become more predictable in 1991-2002, it responded asymmetrically only to output, switching to act more acutely in downturns. While its asymmetric behaviour could result from asymmetric preferences or non-linear aggregate supply, our results support the former explanation.non-linear Phillips curve; Interest rate rules; asymmetric preferences; generalized method of moments; inflation targeting; credibility

    Introducing Theranostics Journal - From the Editor-in-Chief

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    Theranostics is a multidisciplinary journal that publishes innovative and original research papers reflecting the field of molecular imaging, molecular therapeutics, multifunctional nanoparticle platforms, image-guided therapy, and translational nanomedicine. A broad spectrum of biomedical research that can be applied to future theranostic applications is encouraged

    GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs

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    Physics-Informed Neural Network (PINN) has proven itself a powerful tool to obtain the numerical solutions of nonlinear partial differential equations (PDEs) leveraging the expressivity of deep neural networks and the computing power of modern heterogeneous hardware. However, its training is still time-consuming, especially in the multi-query and real-time simulation settings, and its parameterization often overly excessive. In this paper, we propose the Generative Pre-Trained PINN (GPT-PINN) to mitigate both challenges in the setting of parametric PDEs. GPT-PINN represents a brand-new meta-learning paradigm for parametric systems. As a network of networks, its outer-/meta-network is hyper-reduced with only one hidden layer having significantly reduced number of neurons. Moreover, its activation function at each hidden neuron is a (full) PINN pre-trained at a judiciously selected system configuration. The meta-network adaptively ``learns'' the parametric dependence of the system and ``grows'' this hidden layer one neuron at a time. In the end, by encompassing a very small number of networks trained at this set of adaptively-selected parameter values, the meta-network is capable of generating surrogate solutions for the parametric system across the entire parameter domain accurately and efficiently

    Medical Research Data-Sharing:The 'Public Good' and Vulnerable Groups

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    Estimating Estate-Specific Price-to-Rent Ratios in Shanghai and Shenzhen: A Bayesian Approach

    Get PDF
    The price-to-rent ratio, a common yardstick for the value of housing, is difficult to estimate when rental properties are poor substitutes of owner-occupied homes. In this study we estimate price-to-rent ratios of residential properties in two major cities in China, where urban high-rises (estates) comprise both rental and owner-occupied units. We conduct Bayesian inference on estate-specific parameters, using information of rental units to elicit priors of the unobserved rents of units sold in the same estate. We find that the price-to-rent ratios tend to be higher for low-end properties. We discuss economic explanations for the phenomenon and the policy implications
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