AbstractThe seasonality of demographic data has been of great interest. It depends mainly on the climatic conditions, and the findings may vary from study to study. Commonly, the studies are based on monthly data. The population at risk plays a central role. For births or deaths over short periods, the population at risk is proportional to the lengths of the months. Hence, one must analyze the number of births (and deaths) per day. If one studies the seasonality of multiple maternities, the population at risk is the total monthly number of confinements and the number of multiple maternities in a given month must be compared with the monthly number of all maternities. Consequently, when one considers the monthly rates of multiple maternities, the monthly number of births is eliminated and one obtains an unaffected seasonality measure of the rates. In general, comparisons between the seasonality of different data sets presuppose standardization of the data to indices with common means, mainly 100. If one assumes seasonality as ‘non-flatness’ throughout a year, a chi-squared test would be an option, but this test calculates only the heterogeneity and the same test statistic can be obtained for data sets with extreme values occurring in consecutive months or in separate months. Hence, chi-squared tests for seasonality are weak because of this arbitrariness and cannot be considered a model test. When seasonal models are applied, one must pay special attention to how well the applied model fits the data. If the goodness of fit is poor, nonsignificant models obtained can erroneously lead to statements that the seasonality is slight, although the observed seasonal fluctuations are marked. In this study, we investigate how the application of seasonal models can be applied to different demographic variables.
A Hypothesis Test for the Goodness-of-Fit of the Marginal Distribution of a Time Series with Application to Stablecoin Data
