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We study the parametric distribution of log-growth rates of CO2 and CO2 per capita emissions for 207 countries and territories taking data from 1994 to 2010. We define the log-growth rates for different duration periods, from one year apart to fifteen years apart. The considered probability distributions have been the following: the normal (N), the asymmetric double Laplace normal (adLN), the exponential tails normal (ETN) and a mixture of two normal (2N) or three normal (3N) distributions. The main result is that the best one is different depending on the period considered, in such a way that there is not a systematically dominant distribution. Thus, the behavior may change from one year to the next one, and possibly this is influenced by policy measures such as the Kyoto protocol or the Clean Development Mechanism. Moreover, a policy measure that can be derived from this paper is that some countries can still reduce their emissions of CO2 compared with others, as seen by the non-uniformity of the preferred probability distribution for each period. We also model a stochastic differential equation whose associated Fokker–Planck equation has as a solution the observed time-dependent probability density function.
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This paper proposes an alternative method to the iceberg approach for modeling transport costs. Instead of part of the good evaporating in transit, we take as our basis the idea that certain workers in companies are in productive occupations in the strict sense, and others work in transport. This simple change is found to be capable of overcoming most of the criticisms associated with the iceberg approach.
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In this paper we want to test whether the choice of different types of urban data for the same country exerts an influence or not on the selection of the best parametric density function (among the Pareto, truncated lognormal, the double Pareto lognormal and mixtures of lognormals) to describe the city size distribution. We have employed four different definitions of city for Spain. We have concluded that the outperforming density is different for each type of data.
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We studied the parametric description of the city size distribution (CSD) of 70 different countries (developed and developing) using seven models, as follows: the lognormal (LN), the loglogistic (LL), the double Pareto lognormal (dPLN), the two-lognormal (2LN), the two-loglogistic (2LL), the three-lognormal (3LN) and the three-loglogistic (3LL). Our results show that 3LN and 3LL are the best densities in terms of non-rejections out of standard statistical tests. Meanwhile, according to the information criteria AIC and BIC, there is no systematically dominant distribution.
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We have studied the parametric description of the distribution of the log-growth rates of the sizes of cities of France, Germany, Italy, Spain and the USA. We have considered several parametric distributions well known in the literature as well as some others recently introduced. There are some models that provide similar excellent performance, for all studied samples. The normal distribution is not the one observed empirically.
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This paper incorporates technological interdependence into a neoclassical regional growth framework with imperfect factor mobility, leading to a convergence equation with spatial effects. The empirical analysis is based on the estimation of a spatial Durbin panel data model and the implementation of multiple imputation techniques. Our results show that taking into account both unobserved heterogeneity and spatial dependence increases the estimated regional convergence rate. This provides an explanation for puzzling findings in the related literature. We also obtain evidence of heterogeneity across country groups regarding the regional speed of convergence and the degree of diffusion of technology.
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Modeling demographic data has been on the agenda of statisticians for many years. Some of the distributions used are Pareto, reverse Pareto, q-exponential and log-normal models. An approach to this problem is to consider three statistical models: one for the upper tail, one for the middle range, and another for the lower tail. This paper deals with the size distribution of urban and rural agglomerations in Romania for the 1992–2017 period, by comparing the recently introduced three log-normal mixture (3LN), Pareto tails log-normal (PTLN), and threshold double Pareto Generalized Beta of second kind (tdPGB2) models. The tdPGB2 statistical model has the PTLN distribution as a limiting case. The maximum likelihood estimates of the distributions are computed, and goodness-of-fit tests are performed using the Kolmogorov–Smirnov (KS), Cramér–von Mises (CM) and Anderson–Darling (AD) statistics. Also, we use the Vuong and Bayes factor log-likelihood tests. Using both graphical and formal statistical tests, our results rigorously confirm that the 3LN model is statistically equivalent to PTLN and tdPGB2 distributions, the preferred model being the PTLN probability law. Both the PTLN and tdPGB2 distributions have Pareto tails but the 3LN model does not. All the three models prove to be very well suited parameterizations of Romania’s city size data.
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Our main purpose is to study the evolution of the urban structure of Spanish and Italian municipalities from 1900 to 2010. We use the estimation of the pareto exponents to show that the most important behavior is the increase of inequality in the distribution over time. Convergence is more likely in Italy and fro larger urban units.
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This paper analyzes the possible presence of Granger causality between debt and growth in 16 OECD countries from 1980 to 2009. This is done considering not only government debt but also non-financial corporate and household debt. The panel bootstrap Granger causality test applied allows us to control for both the presence of cross-country heterogeneity and cross-sectional dependence. Our results barely provide evidence against the null hypothesis according to which government debt does not cause real GDP growth. More interestingly, we find evidence against the absence of causality from non-financial private debt – especially that of households – to growth.
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We study the parametric description of the city size distribution of four European countries: France, Germany, Italy and Spain. The parametric models used are the lognormal, the double Pareto lognormal, the normal-Box–Cox and the threshold double Pareto Singh–Maddala (last two of these are defined in this paper). The results are quite regular. The preferred model is always the threshold double Pareto Singh–Maddala in the four countries. However, the dPln is not rejected always for the case of France, and in the case of Italy, the dPln is the runner-up distribution.