International Journal of Scientific Research and Management (IJSRM) ||Volume||06||Issue||07||Pages||EM-2018-627-632||2018|| Website: www.ijsrm.in ISSN (e): 2321-3418 Index Copernicus value (2015): 57.47, (2016):93.67, DOI: 10.18535/ijsrm/v6i7.em12 Dashamir Asani, IJSRM Volume 06 Issue 07 July 2018 [www.ijsrm.in] EM-2018-627 A practical strategy to improve econometric modeling–a case study for informal economy on the Republic of Macedonia Dashamir Asani 1 , Dode Prenga 2 1 PhD student, Department of Informatics and Statistics, Faculty of Economy, University of Tirana 2 Department of Physics, Faculty on Natural Sciences, University of Tirana 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. Keywords: informal economy, linear model, log-periodic, empirical mode decomposition. 1. Some remarks on modeling with economic and financial time series In a general consideration for econometric modeling, we attempt to fit observed values for an economic observable to a functional of other ones. From mathematical point of view it is plausible that variables involved in the model could be stationary. If data series are not stationary the remove of unit root by some additional operation is needed. In some models for estimation of unregistered economy, a key variable that is money aggregate [1],[2], by nature is non-stationary and usually volatile. Procedures of data elaboration for this case are detailed largely in literature as for example in [2], [3] etc. More detailed algebraic analysis and remarks on those procedures have been described in the reference [4], etc. However, indexes and money aggregates could be characterized from a special non-linear dynamics called self-organization behavior [5], [6] that could not be elaborated easily to be used in linear models. When dealing with a model of estimation for a latent quantity as the informal economy for example, it worth to consider the additional effects of critical behavior in the dynamics model‟s variables. Those effects are expected to become important if the series consist on a small number of data. In the calculation of the informal economy in Republic of Macedonia for the period [2004, 2014] using standard models CDA (currency demand approach) and MIMIC (multiple indicators, multiple causes) as proposed in [2] etc., we noticed that the results were not so good. Calculation for other periods have been reported in many references as in [8], [9] etc., but apparently by using more data. In our initial work we proposed to use monthly data to improve the calculation and to overcome the problem of small number of data that make regressions less suitable. But the key variable of the model, the currency in circulation, in monthly series showed selforganization-like behavior. So we must consider this problematical behavior in the modeling process. Next, in a brief regard we can expect that an observable can be measured or known accurately only if its (eigen) state is stationary. But in transitive economies (as our case is to be), there exist at least one point when a total regime change occur. Other disturbances could be present too. In short, specific systems are not as good as mathematical models want them to be. Therefore we assumed that removing those shortcomings could have improved the result of linear modeling and therefore we considered them in a preliminary analysis. In our case study we considered the analysis of the state itself from a general point of view and next we explored about specifics of the dynamics on the series used for the calculation.