Search

Depth regulates many attributes of aquatic ecosystems, but relatively few lakes are measured, and existing datasets are biased toward large lakes. To address this, we used a large dataset of maximum (Zmax; n = 16,831) and mean (Zmean; n = 5,881) depth observations to create new depth models, focusing on lakes < 1,000 ha. We then used the models to characterize patterns in lake basin shape and volume. We included terrain metrics, water temperature and reflectance, polygon attributes, and other predictors in a random forest model. Our final models generally outperformed existing models (ZmaxR2=0.35; root mean square error [RMSE] = 8.0 m and ZmeanR2=0.36; RMSE = 3.0 m). Our models show that lake depth followed a Pareto distribution, with 2.8 orders of magnitude fewer lakes for an order of magnitude increase in depth. In addition, despite orders of magnitude variation in surface area, most size classes had a modal maximum depth of ~ 5 m. Concave (bowl‐shaped) lake basins represented 79% of all lakes, but lakes were more convex (funnel‐shaped) as surface area increased. Across the conterminous United States, 9.8% of all lake water was within the top meter of the water column, and 48% in the top 10 m. Excluding the Laurentian Great Lakes, we estimate the total volume in the conterminous United States is 1,057–1,294 km3, depending on whether Zmax or Zmean was modeled. Lake volume also exhibited substantial geographic variation, with high volumes in the upper Midwest, Northeast, and Florida and low volumes in the southwestern United States. [ABSTRACT FROM AUTHOR]

We derive and express the Yitzhaki index of relative deprivation as a function of mean and median household income within a reference group and one's income, under the assumption that individual income follows the log-normal distribution or the Pareto distribution. We also suggest a way to calculate annual US regional mean and median household income in each reference group using aggregate income and household size data. Therefore, unlike prior literature that utilizes Decennial Census data due to its big sample size, our suggested methodology allows annual calculation of relative deprivation at different levels of geography (state or county). In an application of the derived index presented to illustrate its feasibility, we analyse the relationship between relative deprivation and mental health, finding suggestive evidence that increases in relative deprivation negatively affect mental health in the United States. [ABSTRACT FROM AUTHOR]

Newly precise evidence of the trajectory of top incomes in the United States and around the world relies on shares and ratios, prompting new inquiry into their properties as inequality measures. Current evidence suggests a mathematical link between top shares and the Gini coefficient and empirical links extending as well to the Atkinson measure. The work reported in this article strengthens that evidence, making several contributions: First, it formalizes the shares and ratios, showing that as monotonic transformations of each other, they are different manifestations of a single inequality measure, here called TopBot. Second, it presents two standard forms of TopBot, which satisfy the principle of normalization. Third, it presents a new link between top shares and the Gini coefficient, showing that properties and results associated with the Lorenz curve pertain as well to top shares. Fourth, it investigates TopBot in mathematically specified probability distributions, showing that TopBot is monotonically related to classical measures such as the Gini, Atkinson, and Theil measures and the coefficient of variation. Thus, TopBot appears to be a genuine inequality measure. Moreover, TopBot is further distinguished by its ease of calculation and ease of interpretation, making it an appealing People's measure of inequality. This work also provides new insights, for example, that, given nonlinearities in the (monotonic) relations among inequality measures, Spearman correlations are more appropriate than Pearson correlations and that weakening of correlations signals differences and shifts in distributional form, themselves signals of income dynamics. [ABSTRACT FROM AUTHOR]

Precipitation is an important meteorological variable which is critical for weather risk assessment. For instance, intense but short precipitation events can lead to flash floods and landslides. Most statistical modelling studies assume that the occurrence of precipitation events is based on a Poisson process with exponentially distributed waiting times while precipitation intensities are typically described by a gamma distribution or a mixture of two exponential distributions. Here, we show by using hourly precipitation data over the United Sates that the waiting time between precipitation events is non‐exponentially distributed and best described by a fractional Poisson process. A systematic model selection procedure reveals that the hourly precipitation intensities are best represented by a two‐distribution model for about 90% of all stations. The two‐distribution model consists of (a) a generalized Pareto distribution (GPD) model for bulk precipitation event sizes and (b) a power‐law distribution for large and extreme events. Finally, we analyse regional climate model output to evaluate how the climate models represent the high‐frequency temporal structure of U.S. precipitation. Our results reveal that these regional climate models fail to accurately reproduce the power‐law behaviour of intensities and severely underestimate the long durations between events. [ABSTRACT FROM AUTHOR]

Generalized additive model (GAM) forms offer a flexible approach to capturing marginal variation. Such forms are used here to represent distributional variation in extreme values and presented in terms of spatio-temporal variation, which is often evident in environmental processes. A two-stage procedure is proposed that identifies extreme values as exceedances of a high threshold, which is defined as a fixed quantile and estimated by quantile regression. Excesses of the threshold are modelled with the generalized Pareto distribution (GPD). GAM forms are adopted for the threshold and GPD parameters, and directly estimated—in particular smoothing parameters—by restricted maximum likelihood, which provides an objective and relatively fast method of inference. The GAM models are used to produce return level maps for extreme wind gust speeds over the United States, which show extreme quantiles of the distribution of annual maximum gust speeds. for this article are available online. [ABSTRACT FROM AUTHOR]

This paper focuses on spatial city-size distribution in the United States. It proposes a new distance-based approach to analyze the influence of distance on the city-size distribution parameter by considering the Pareto distribution and using data from different definitions of US cities in 2010. Considering all possible combinations of cities within a 300-mile radius, the results indicate that the Pareto distribution cannot be rejected in most cases regardless of city size. Placebo regressions validate the results, thereby confirming the significant effect of geography on the Pareto exponent. [ABSTRACT FROM AUTHOR]

Top income inequality rose sharply in the United States over the last 40 years but increased only slightly in France and Japan. Why? We explore a model in which heterogeneous entrepreneurs, broadly interpreted, exert effort to generate exponential growth in their incomes, which tends to raise inequality. Creative destruction by outside innovators restrains this expansion and induces top incomes to obey a Pareto distribution. Economic forces that affect these two mechanisms—including information technology, taxes, and policies related to innovation blocking—may explain the varied patterns of top income inequality that we see in the data. [ABSTRACT FROM AUTHOR]

General Aviation is the main component of the United States civil aviation and the most aviation accidents concern this aviation category. Between early 2015 and May 17, 2016, a total of 1546 general aviation accidents in the United States has left 466 fatalities and 384 injured. Hence, in this study, we investigate the risk of U.S. General Aviation accidents by examining historical U.S. General Aviation accidents. Using the Peak Over Threshold approach and Generalized Pareto Distribution, we predict the number of fatalities resulting in extreme GA accidents in the future operations. We use a graphical method and intensive parameters estimates to obtain the optimal range of the threshold. In order to assess the uncertainty in the inference and the accuracy of the results, we use the nonparametric bootstrap approach. [ABSTRACT FROM AUTHOR]

We propose a new time-varying peaks over threshold model to study tail risk dynamics in equity markets: the laws of motion for the parameters are defined through the score-based approach. We apply the model to daily returns from U.S. size-sorted decile stock portfolios and show that large firms' tail risk increases during recessions more than small firms' tail risk. Our results are consistent with the granular hypothesis of aggregate fluctuations, and we quantify the impact of large firms' tail risk shocks on the economy. A measure of tail connectedness is proposed: evidence from international equity markets shows that tail connectedness increases during periods of turmoil. This paper was accepted by Lauren Cohen, finance. [ABSTRACT FROM AUTHOR]

This article explains why platforms such as Amazon and Visa rely predominantly on ad valorem fees, fees which increase proportionally with transaction prices. It also provides a new explanation for why ad valorem sales taxes are more desirable than specific taxes. The theory rests on the ability of ad valorem fees and taxes to achieve efficient price discrimination, given that the value of a transaction to buyers tends to vary proportionally with the cost of the good traded. [ABSTRACT FROM AUTHOR]

In recent years, with a growing interest in big or large datasets, there has been a rise in the application of large graphs and networks to financial big data. Much of this research has focused on the construction and analysis of the network structure of stock markets, based on the relationships between stock prices. Motivated by Boginski et al. (2005), who studied the characteristics of a network structure of the US stock market, we construct network graphs of the UK stock market using same method. We fit four distributions to the degree density of the vertices from these graphs, the Pareto I, Fréchet, lognormal, and generalised Pareto distributions, and assess the goodness of fit. Our results show that the degree density of the complements of the market graphs, constructed using a negative threshold value close to zero, can be fitted well with the Fréchet and lognormal distributions. [ABSTRACT FROM AUTHOR]

We consider a distribution, which consists of lower tail Pareto, lognormal body, and upper tail Pareto, to estimate the size distribution of all US cities. This distribution fits the data more accurately than a distribution that comprises of only lognormal and the upper tail Pareto. [ABSTRACT FROM AUTHOR]

Inconsistent censoring in the public-use March Current Population Survey ( CPS) limits its usefulness in measuring labor earnings trends, as previous approaches for imputing topcoded earnings systematically understate top earnings. Using Pareto estimation methods with less-censored internal data, we create an enhanced cell-mean series to capture top earnings in the public-use data. Annual earnings inequality trends since 1963 using our series largely mirror those found by Kopczuk, Saez, and Song using social security administration data for commerce and industry workers. When we extend our analysis to 2013 and consider all workers, earnings inequality levels are higher but its growth is more modest. ( JEL C81, D31, J01) [ABSTRACT FROM AUTHOR]

The ground level ozone concentration over the continental United States is analyzed from the point of view of modern Extreme Value Theory using ozone data from the Clean Air Status and Trends Network (CASTNET) at 25 measurement sites. At each site the data is analyzed separately for two time periods (1992–2002 and 2003–2013) approximately separated by the NO X SIP call. The Generalized Pareto Distribution is fit to extremes of the ozone concentration by using a combination of maximum likelihood estimates (MLEs) and Hill estimates. The data is appropriately transformed prior to extreme value analysis and data in the right tail is separated from that in the middle part of the distribution. This analysis is compared to current approaches by using synthetic data. Under a variety of conditions the procedure using the MLE approach is likely to underestimate the tail of the distribution. The analysis of the CASTNET ozone data shows that at some locations the ozone probability distribution is not exponentially bounded, and thus can be characterized as heavy tailed, and that at other locations this distribution is not heavy and is bounded to the right so that the ozone concentration is bounded for any return period. The tails of the distribution of ozone concentration become heavier following the NO X SIP call at most of the sites with heavy tails prior to this call. [ABSTRACT FROM AUTHOR]

This paper seeks to explore the relationship among the large declines in China’s stock market and stock markets in the United States, the United Kingdom, Japan, and Hong Kong, and to forecast future large declines in China’s stock market. We apply mutually exciting marked Hawkes processes—where the occurrences of large declines are regarded as events whose magnitudes follow generalized Pareto distributions—to study the interaction between the different stock markets. We find that these interactions are asymmetric. The occurrences of large declines in the Chinese stock market will stimulate declines in the United States and the United Kingdom, and especially in Japan’s and Hong Kong’s stock markets. However, only large declines in Hong Kong’s market have a substantial influence on the occurrence of big declines in China’s market. Finally, we try to predict the time of occurrence of the next major decline in China’s stock market using information from our empirical results. [ABSTRACT FROM PUBLISHER]

The coal industry is dominated by the largest mines, with 1% of coal mines in the U.S. being responsible for 65% of the coal cumulatively produced. We show that this "heavy tail" can be well approximated by a power law, where mine-level cumulative production is inversely proportionate to distributional rank. Maximum likelihood and regression-based procedures estimate the counter-cumulative power-law parameter to be less than one, indicating there is no well-defined mean or variance for cumulative production. Goodness of fit tests indicate the power-law is a better fit to the data than other competing distributions, including the lognormal. • Mine-level cumulative coal production is power-law distributed. • Distribution indicates there is no well-defined mean or variance. • Distribution shows how heavily concentrated coal production is in the US. [ABSTRACT FROM AUTHOR]