Peaks Over Threshold Vs. Lognormal Estimates Of The Czech Household Incomes
Abstract
Income distributions are usually long-tailed and the right tail is often important part of income inequality metrics, but it is also problematic part of income distribution to be modeled. The POT method is theoretically well established method for modeling tails of unknown underlying distribution and thus candidate to become complement of the standard estimates. The article deals with the problem of parameter estimates using deHaan and CME methods and comparing the resulting quantile estimates with the one-distributional fitting. All of these estimates are done for the net money incomes of the Czech households. The results shows, that due to the data problems deHaan method usually gives.more robust estimates than CME method. The log-normal distribution usually fits the data well up to the quantile x0,995 but for the rest of the distribution, the GPD is better fitting distribution. The peaks over threshold method is then useful only for the genuine extremes and even there its estimates depends on the quality of data.
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