6,861 research outputs found

    A century of the evolution of the urban system in Brazil

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    In this paper, we study the hitherto unexplored evolution of the size distribution of 185 urban areas in Brazil between 1907 and 2008. We find that the power law parameter of the size distribution of the 100 largest urban areas increases from 0.63 in 1907 to 0.89 in 2008, which confirms an agglomeration process in which the size distribution has become more unequal. A panel fixed effects model pooling the same range of urban size distributions provides a power law parameter equal to 0.53, smaller than those from cross-sectional estimation. Clearly, Zipf’s Law is rejected. The lognormal distribution fits the city size distribution quite well until the 1940s, but since then applies to small and medium size cities only. These results are consistent with our understanding of historical-political and socio-economic processes that have shaped the development of Brazilian cities

    Heavy-Tailed Features and Empirical Analysis of the Limit Order Book Volume Profiles in Futures Markets

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    This paper poses a few fundamental questions regarding the attributes of the volume profile of a Limit Order Books stochastic structure by taking into consideration aspects of intraday and interday statistical features, the impact of different exchange features and the impact of market participants in different asset sectors. This paper aims to address the following questions: 1. Is there statistical evidence that heavy-tailed sub-exponential volume profiles occur at different levels of the Limit Order Book on the bid and ask and if so does this happen on intra or interday time scales ? 2.In futures exchanges, are heavy tail features exchange (CBOT, CME, EUREX, SGX and COMEX) or asset class (government bonds, equities and precious metals) dependent and do they happen on ultra-high (<1sec) or mid-range (1sec -10min) high frequency data? 3.Does the presence of stochastic heavy-tailed volume profile features evolve in a manner that would inform or be indicative of market participant behaviors, such as high frequency algorithmic trading, quote stuffing and price discovery intra-daily? 4. Is there statistical evidence for a need to consider dynamic behavior of the parameters of models for Limit Order Book volume profiles on an intra-daily time scale ? Progress on aspects of each question is obtained via statistically rigorous results to verify the empirical findings for an unprecedentedly large set of futures market LOB data. The data comprises several exchanges, several futures asset classes and all trading days of 2010, using market depth (Type II) order book data to 5 levels on the bid and ask

    Stochastic Ordering under Conditional Modelling of Extreme Values: Drug-Induced Liver Injury

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    Drug-induced liver injury (DILI) is a major public health issue and of serious concern for the pharmaceutical industry. Early detection of signs of a drug's potential for DILI is vital for pharmaceutical companies' evaluation of new drugs. A combination of extreme values of liver specific variables indicate potential DILI (Hy's Law). We estimate the probability of severe DILI using the Heffernan and Tawn (2004) conditional dependence model which arises naturally in applications where a multidimensional random variable is extreme in at least one component. We extend the current model by including the assumption of stochastically ordered survival curves for different doses in a Phase 3 study.Comment: 24 pages, 5 figure

    Consumption Inequality and Intra-Household Allocations

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    The consumption literature uses adult equivalence scales to measure individual level inequality. This practice imposes the assumption that there is no within household inequality. In this paper, we show that ignoring consumption inequality within households produces misleading estimates of inequality along two dimensions. First, the use of adult equivalence scales underestimates the level of cross sectional consumption inequality by 30%. This result is driven by the fact that large differences in the earnings of husbands and wives translate into large differences in consumption allocations within households. Second, the rise in inequality since the 1970s is overstated by two-thirds: within house-hold inequality declined over time as the share of income provided by wives increased. Our findings also indicate that increases in marital sorting on wages and hours worked can simultaneously explain virtually all of the decline in within household inequality and a substantial fraction of the rise in between household inequality for one and two adult households in the UK since the 1970s.Collective Model, Consumption Inequality, Marital Sorting, Adult Equivalence Scales

    Consumption Inequality and Intra-Household Allocations

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    Collective Model, Consumption Inequality, Marital Sorting, Adult Equivalence Scales

    Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

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    In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.Comment: 27 pages, 17 figure

    The Impact of Competition Policy: What are the Known Unknowns?

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    Evaluations of competition policy are increasingly common and typically establish that consumer bene.ts from detected cases easily outweigh the costs of competition authorities (CA). However, such assessments are often driven by data availability and only capture a small part of the total impact because they sidestep the difficult issue of how to evaluate deterrence. Similarly, they ignore the fact that policy does not root out all anti-competitive cases. This paper suggests a broader framework for evaluation which encompasses these unobserved impacts. Calibration is difficult precisely because we cannot rely on empirical observations on cases which have been observed to make deductions about cases which have not (because they are deterred or undetected). It thereby confronts the classic sample selection problem which is endemic in all studies based on data from CA decisions. Drawing on insights from economic theory, it argues that selection bias is likely to be substantial because the unobserved cases could well be those which are most harmful. If so, the deterrence of anti-competitive mergers may have a much greater positive impact, but the effects of non-detected cartels may be more serious than is usually supposed
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