Ankete pouzdanja poduzeća i potrošača (engl. Business and Consumer Surveys, BCS) koristan su izvor podataka za ekonomske analize i prognoze, a bezrezervna dostupnost BCS podataka stimulira nove ...primjene ovih podataka u empirijskim znanstvenim istraživanjima kao i kontinuirana metodološka unapređenja provođenja anketa i izračunavanja kompozitnih pokazatelja. Pokazatelj pouzdanja potrošača (engl. Consumer Confidence Indicator, CCI), koji se računa upravo pomoću BCS podataka, jedan je od široko prihvaćenih i empirijski potvrđenih vodećih (engl. leading) indikatora osobne potrošnje i ekonomske aktivnosti općenito. Iako je metodologija izračuna pokazatelja pouzdanja harmonizirana na europskoj razini, argumentirani prijedlozi poboljšanja istih uvijek su dobrodošli. Stoga se u ovom radu zadržavaju varijable komponente CCI-a koje su definirane na razini EU-a, ali se promjenom metodologije izračuna CCI-a nastoji ukazati na mogućnost poboljšanja prognostičkih svojstava ovog pokazatelja sentimenta potrošača. Umjesto standardne metode izračuna pokazatelja kao jednostavne aritmetičke sredine četiriju varijabli komponenti, što znači da su sve četiri varijable jednako značajne u izračunu pokazatelja, rabi se metoda nelinearne optimizacije. U istraživanju se polazi od pretpostavke da se inoviranjem metodologije izračuna pokazatelja, novim načinom određivanja težina pridruženih pojedinim varijablama komponentama CCI-a, mogu poboljšati njegova prognostička svojstva. Rezultati empirijskog istraživanja povezanosti inoviranog CCI-a i osobne potrošnje kao referentne serije potvrđuju pretpostavku istraživanja. Naime, rezultati pokazuju da se temeljem promjena inoviranog CCI-a može uspješno predvidjeti smjer promjene osobne potrošnje s dva tromjesečja prethođenja.
U istraživanju su rabljeni tromjesečni BCS podatci za četiri standardne CCI komponente i godišnje stope rasta osobne potrošnje na agregatnoj razini EU-a. Podatcima je obuhvaćeno razdoblje od prvog tromjesečja 1996. do drugog tromjesečja 2019. Izvori podataka su Europska komisija i Eurostat. Ovo istraživanje provedeno je samo za EU na agregatnoj razini, no ovo ograničenje uklonit će se budućim istraživanjima koja će se usredotočiti na ispitivanje i uočavanje potencijalnih razlika u sentimentu potrošača po pojedinim zemljama članicama EU-a. Očekuju se različiti sustavi ponderiranja u izračunu CCI-a za različita nacionalna gospodarstva u EU-u. Izvornost ovoga rada ogleda se u inoviranom načinu izračuna pokazatelja pouzdanja potrošača primjenom nelinearne optimizacije u odnosu na standardni izračun koji primjenjuje EU.
Business and Consumer Surveys (BCS) surveys are useful source of data for various economic analysis and forecasts. The general availability of BCS data stimulates new applications of this data in empirical scientific research, as well as continuous methodological improvements in conducting surveys and calculating composite indicators. The Consumer Confidence Indicator (CCI), which is calculated using BCS data, is one of the widely accepted and empirically proven leading indicators of consumer sentiment and economic activity in general. Although the methodology for calculating confidence indicators has been harmonized at European level, suggestions for improving them have always been welcome. Therefore, in this research the CCI component variables defined at EU level have been retained. However, the possibility of improving the prognostic properties of this consumer sentiment indicator has been indicated by changing the CCI calculation methodology. Instead of the standard method of calculating the CCI indicator as a simple arithmetic mean of its four component variables, that is the methodology which considers all four variables as equally significant in the calculation of the indicator, the nonlinear optimization method is used. The hypothesis of the study is that by updating the methodology of the indicator calculation, its prognostic properties can be improved through a new method of determining the weights associated with individual variables of CCI’s components. The results of an empirical study of the relationship between the innovated CCI and personal consumption as a reference series confirm the research hypothesis. Specifically, the results show that, based on changes in innovated CCI, one can successfully predict the direction of change in personal consumption two quarters in advance. The empirical part of the study is based on quarterly BCS data for four standard CCI’s components and annual growth rates of personal consumption at EU aggregate level. The data covers the period from the first quarter of 1996 to the second quarter of 2019. Data sources are the European Commission and Eurostat. This research was conducted only for the EU at the aggregate level, but this limitation will be removed by future research that will focus on examining and spotting potential differences in consumer sentiment across individual EU member states. Different weighting systems are expected in the calculation of CCI for different national economies in the EU. The originality of this paper is reflected in the innovative way of calculating consumer confidence indicators which is based on applying nonlinear optimization, unlike the standard calculation applied by the EU.
In this paper, TOA localization model is presented for passive source localization. Localization problem is formulated as an optimization problem with the sum of squared errors as the objective ...function. Gauss-Newton optimization method is applied in order to determine optimal transmitter location. We considered radio channel in the presence of Gaussian noise. Computer numerical simulation results in MATLAB are presented for linear and nonlinear TOA localization methods, as well as their mutual correlation.
