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Open access

Fahir Kanlić and Ademir Abdić

Abstract

National Statistical Institutes (NSIs) strive to produce short-term business statistics (STS) indicators with the high quality estimates in a timely manner. NSIs are usually faced with the challenges, such as differences in definitions, incompleteness of administrative data, periodicity and timeliness, coverage issues, etc. Administrative Value Added Tax (VAT) turnover data can be used to partially or completely replace survey data for the estimation of short-term business turnover indicators. In this paper, main characteristics of administrative VAT turnover data in Bosnia and Herzegovina will be examined through cleaning of VAT turnover data and matching them with survey data. Hence, the main objective of this study is to investigate the relationship between VAT turnover data and survey data in in Bosnia and Herzegovina. The Monthly Retail Trade Turnover Indices (RTI) for in Bosnia and Herzegovina will be estimated by using administrative VAT turnover data. Spearman’s correlation coefficients are used to examine the presence of a linear relationship between VAT turnover data and survey data. Results gained by using survey and administrative VAT turnover data will be compared. Based on the results of the analysis, future challenges and perspectives for expansion of using administrative VAT turnover data will be identified.

Open access

Rudi Seljak, Lea Bregar, Sanda Colić and Maja Dozet

Abstract

In spite of theoretical inferiority, a large majority of statistical institutes use non-probabilistic sampling techniques in price surveys. The main disadvantage of non-probabilistic sample design is that the risk of biased results is increased. Attempting to handle this risk in the domain of service producer price indices (SPPI) of professional services, the Croatian Bureau of Statistics (CBS) developed an innovative methodology and implemented it relying on the probability proportional to size (PPS) sample design. The purpose of the paper is to evaluate the impact of the probabilistic sampling strategy on the quality of price indices as shown in the case of SPPI for professional services at the CBS. The paper outlines respective methodological upgrading of SPPI compilation at the CBS, including also the method for variance estimation. The effect of the probabilistic sampling on SPPI is analysed by comparison with traditional purposive sampling surveys. The quality of SPPI based on the probabilistic sample approach is examined by coefficients of variation and confidence intervals.

Open access

Berislav Žmuk and Iris Mihajlović

Abstract

The expansion of the Internet has radically changed the way in which citizens travel, book and organise travel arrangements. Since innovation and new information technologies have become crucial determinants to encourage competitiveness in the tourism sector in Europe, this article investigates how selected development indicators influence the Percentage of individuals that use Internet for travel and accommodation services. Eurostat data for 34 European, European Union (EU-28) and selected EU candidates, countries for 2017 were analysed. It has been presented that (1) Gross Domestic Product per capita in Purchasing Power Standards; (2) Percentage of population aged 15 to 64, by tertiary education; as well as (3) Percentage of individuals aged 16 to 74, who have basic or above basic overall digital skills, all correlate positively and strongly with the main variable under study. The conducted regression analysis has shown that variable digital skills has the greatest impact on the main variable under the study. The K-mean clustering of countries resulted with four clusters. The Western Balkan countries can be found in a cluster which has in average the lowest values of all four variables in compare to the averages of other three clusters.

Open access

Gloria Gheno

Abstract

If there are no heavy sanctions in place to prevent it, the problem of the cancellation of appointments can lead to huge economic losses and can have a significant impact on underutilized resources of healthcare facilities. A good model to predict the appointment cancellations could be an effective solution to this problem. Therefore, a new Bayesian method is proposed to estimate accurately the probability of the cancellation of visits to healthcare institutions based on specific factors such as age. This model uses the regression for binary variables, linking the explanatory variables to the probability of appearance at a previously made appointment with a new weighted function and estimating the parameters with the Bayesian method. The goodness of the new method is demonstrated by applying it to a real case and by comparing it to other methodologies. Therefore, the advantages of the proposed method are exposed and possible real-world applications are described.

Open access

Martin Noveski

Abstract

Although a decade has passed since the global financial and economic crisis of 2008, the expansionary fiscal policy in Macedonia can still be felt, primarily through an increased level of public expenditures aimed at stimulation of the economic growth. From 2008 onwards, the Republic of Macedonia has continuously recorded a negative budget balance, which affects the resources allocation and the overall economic situation. The question that arises is whether such interference by the Government in the functioning of the market economy is necessary, especially having in mind the EU regulation in this area. Using a multiple regression model for the period 1996-2015, this paper examines the impact of the budget deficit on Gross Domestic Product (GDP) per capita in Macedonia. Results show that the budget deficit is not a statistically significant determinant of GDP per capita, supporting thus the Ricardian equivalence theory. The analysis is conducted on the basis of statistical data from the World Bank’s database, as well as data from the National Bank of the Republic of Macedonia. Household final consumption expenditure, the unemployment rate and the official exchange rate of the Macedonian Denar against the U.S. Dollar are also taken into consideration as controlling variables. GDP per capita and household final consumption expenditures are in current prices, with natural logarithms applied, whereas the other variables are in nominal terms. The purpose of this paper is to provide an insight into the empirical relationship between the two main variables of interest and to initiate further discussion and analysis.

Open access

Vesna Bucevska and Goran Mojanoski

Abstract

This paper aims to evaluate the relationship of real exchange rates of domestic currencies with macroeconomic variables in Macedonia, Croatia and Serbia by using econometric approaches. Macedonia is characterized by the regime of a fixed exchange rate, Croatia is characterized by a managed floating exchange rate, while Serbia is characterized by the regime of a floating exchange rate. The choice of an exchange rate regime is an important aspect of economic management, in order to ensure competitiveness, macroeconomic stability and development. Evaluation of the relationship of Croatian, Macedonian and Serbian real exchange rates is performed by employing the consistent methodology of vector error correction modelling (VECM). According to the results of the analyses of the real exchange rates on the long run, the selected independent variables have long-run causality in case of the real exchange rate of Croatian Kuna. In case of Macedonian Denar and Serbian Dinar the VECM is inappropriate.

Open access

Gábor Rappai and Diána Ivett Fűrész

Abstract

Based on previous research it can be stated that modelling sport economics related demand curves (e.g. demand for sport events and athletes) is different from other types of modelling. The difference lies in the fact that some parts of the demand curves are nearly horizontal in case of sport goods and nearly vertical in case of athletes, because the price of sport events is inflexible and at the same time, salaries of top athletes are extremely flexible. This study investigates parameter estimation methods appropriate for the relevant demand functions of sport economics. In this cases the generally used ordinary least squares estimator is less robust, so the weighted least squares estimators are able to handle heteroskedasticity. If the distribution of the variables is known, the Newey-West heteroscedasticity corrected estimates give even stronger results. The empirical study analyses footballer transfer fees in top European leagues and identifies a threshold at which the traditional supply-demand functions are not appropriate. According to the results, word class athletes, in a way, can be considered prestige goods for which demand may be irrational.

Open access

Diána Ivett Fürész

Abstract

The transfer market of European football can be classified as a system. In this system, the effectiveness of participant teams can depend on the activity in players’ transfers. This article assesses the utility of network analysis in analysing connections between the mentioned concepts. The hypothesis is that there is causality between a club’s activity in the transfer market and its profit from transfers. This research is based on empirical transfer data of major soccer teams, which have had a significant role in the last 12 years in Europe. It is assumed that the most active clubs in the transfer system have more financial power in the transfer market, while teams which are not active in transfers have less profit from transfers. In the network analysis, the teams can be defined as a set of nodes and connected by edges (interactions). The thickness of the edges and the size of the nodes depend on the volume of transfers among clubs. The number of interactions and the amount of the transfer price can measure this volume also. Considering the results of network indices, the relationships between the two phenomena were reviewed. In order to explore these relationships, the correlations among all of the relevant variables in the transfer market were also measured.

Open access

Marijana Zekić-Sušac, Rudolf Scitovski and Adela Has

Abstract

Although energy efficiency is a hot topic in the context of global climate change, in the European Union directives and in national energy policies, methodology for estimating energy efficiency still relies on standard techniques defined by experts in the field. Recent research shows a potential of machine learning methods that can produce models to assess energy efficiency based on available previous data. In this paper, we analyse a real dataset of public buildings in Croatia, extract their most important features based on the correlation analysis and chi-square tests, cluster the buildings based on three selected features, and create a prediction model of energy efficiency for each cluster of buildings using the artificial neural network (ANN) methodology. The main objective of this research was to investigate whether a clustering procedure improves the accuracy of a neural network prediction model or not. For that purpose, the symmetric mean average percentage error (SMAPE) was used to compare the accuracy of the initial prediction model obtained on the whole dataset and the separate models obtained on each cluster. The results show that the clustering procedure has not increased the prediction accuracy of the models. Those preliminary findings can be used to set goals for future research, which can be focused on estimating clusters using more features, conducted more extensive variable reduction, and testing more machine learning algorithms to obtain more accurate models which will enable reducing costs in the public sector.