“…Considering the fact that number of individuals interviewed (number of valid observation) is small (N=350), the histogram optimization has been considered with great care according to Scot and Freidman-Diaconics rule as discussed in [19]. We applied a tuning technique as proposed in [18] around the bin size found in standard procedures. We observe that q parameter is obtained in the range [1.3-2.9] and all variables except one have q>5/3 and therefore distributions are non-stationary.…”
In standard econometric application all variables are analyzed statistically before being used in mathematical models. In this framework we considered non-stationary distribution as an starting procedure on the study of consumer behavior in a local market area whereof non-homogeneity of buyers and small size effect could be present. By evaluation of the degree of non-stationary of the actual state for particular variable as observed, we hope to be able to estimate and interpret the model outcomes. Assuming the non-stationary of variables as indicator of the overall stet itself, we argue that the state where observation were made is non-stationary too, and for that reason, models are expected to not fit well. In the other hand, by dropping the significance level in model fitting process we expect to count for this instability whereas the model remains valid. Herewith, the logistic model for consumer behavior in our system is applied and calculated using significance level 0.85-0.90. Under such limiting constraint assumption we identified the variables that mostly affected the proportion between expense categories and the characteristics of the expenses that mostly describe the market consumer behavior in the unity studied. We hope that methodically this procedure could be helpful for other similar market or socio-metric study as well.
“…Considering the fact that number of individuals interviewed (number of valid observation) is small (N=350), the histogram optimization has been considered with great care according to Scot and Freidman-Diaconics rule as discussed in [19]. We applied a tuning technique as proposed in [18] around the bin size found in standard procedures. We observe that q parameter is obtained in the range [1.3-2.9] and all variables except one have q>5/3 and therefore distributions are non-stationary.…”
In standard econometric application all variables are analyzed statistically before being used in mathematical models. In this framework we considered non-stationary distribution as an starting procedure on the study of consumer behavior in a local market area whereof non-homogeneity of buyers and small size effect could be present. By evaluation of the degree of non-stationary of the actual state for particular variable as observed, we hope to be able to estimate and interpret the model outcomes. Assuming the non-stationary of variables as indicator of the overall stet itself, we argue that the state where observation were made is non-stationary too, and for that reason, models are expected to not fit well. In the other hand, by dropping the significance level in model fitting process we expect to count for this instability whereas the model remains valid. Herewith, the logistic model for consumer behavior in our system is applied and calculated using significance level 0.85-0.90. Under such limiting constraint assumption we identified the variables that mostly affected the proportion between expense categories and the characteristics of the expenses that mostly describe the market consumer behavior in the unity studied. We hope that methodically this procedure could be helpful for other similar market or socio-metric study as well.
“…To realize that, we checked the log periodic fit of the data series. In this procedure we changed the start and the end date for partial series under test, using an ad hoc genetic algorithm as in [18]. Firstly we considered trimestral data so we had 4 times more points than yearly ones, but still the number is smaller for such quantitative analysis.…”
Section: The Dynamics Of Some Important Variablesmentioning
Abstract:Here we consider some proposals in the calculation of the shadow economy using linear models that involve if the number of data point is small and observables are highly dynamical. We firstly suggest checking for possible critical points in the series by testing a log-periodic fit to the data. To improve the results from the model, we used intervals that do not contain critical points. Next, the presence of regimes is analyzed by using empirical mode decomposition technique, and we estimate that the best truncated series to be used should exclude the edges of such regimes. In the case of short term regimes, we propose to use series in intervals that include many cycles. This technique worked for the calculation of informal economy in the Republic of Macedonia for the short period of [2004, 2016] but it is supposed to improve calculation for other similar cases as well.
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