Speeding problem detection in business surveys: benefits of statistical outlier detection methods

Speeding describes the unusually fast responses provided to survey questions. A characteristic of speeders is that answers by-pass cognitive process. Consequently, this low respondent engagement results in the poor quality and validity of data. The issue at hand is how to detect speeders in a survey. The presumption is the use of different statistical outlier detection methods. This paper presents graphical methods for outlier detection, such as: dot-plot diagrams, scatter diagrams, histograms and box-plot diagrams. Furthermore, the quantitative methods for outlier detection in this paper are the z-score, modified z-score, Dixons’ test, Grubbs’ test, Tietjen-Moore test, Rosners’ or the generalized extreme studentized deviate (ESD) test. The performance of these outlier detection methods was observed on completion times of 217 surveys from enterprises which participated in a web survey on the use of statistical methods, and which use them in their business processes. The analysis has shown that none of the observed outlier detection methods were able to detect speeders in an appropriate and satisfactory way as shown by the threshold. The main reasons for this can be found in slowers, the violations of assumptions on normal distribution and in masking. Hence, existing outlier detection methods should be improved and adjusted in future research in order to detect speeders. The introduction of novel speeders detection methods would be a good choice for future research

Quality of life indicators in selected European countries: Statistical hierarchical cluster analysis approach

The average expected duration of human life is rising because of different reasons. On the other hand, not only the duration, but the quality of life level is important, too. The higher the quality of life level, the citizens’ happiness and satisfaction levels are higher, which has positive impact on the development and operating of an economy. The goal of this paper is to identify groups of European countries, using statistical hierarchical cluster analysis, by using the quality of life indicators, and to recognise differences in quality of life levels. The quality of life is measured by using seven different indicators. The conducted statistical hierarchical cluster analysis is based on the Ward’s clustering method, and squared Euclidean distances. The results of conducted statistical hierarchical cluster analysis enabled recognizing of three different groups of European countries: old European Union member states, new European Union members, and non-European Union member states. The analysis has revealed that the old European Union member states seem to have in average higher quality of life level than the new European Union member states. Furthermore, the European Union member states have in average higher quality of live level than non-European Union members do. The results indicate that quality of life levels and economic development levels are connected

Measuring Efficiency of Statistical Methods Use in Enterprises: Development of a System of Indicators

The paper introduces a system of indicators for measuring statistical methods use efficiency in enterprises. The indicators are formed based on the questionnaire, which is used to inspect the attitude of employees towards statistical methods use in Croatian enterprises. With the purpose of understanding statistical methods use effectiveness better, indicators are classified into two groups: comparative and individual indicators. These indicators were used in the construction of the E-score indicator, which can be used to predict if an enterprise will achieve a positive net income due to an effective use of statistical methods or not. The system of indicators of statistical methods use efficiency developed within this research can be easily used by any enterprise. Using these indicators an enterprise can estimate its competitive position compared to other enterprises and it can predict if the difference will increase or decrease. Despite the existence of many business systems of indicators, the impact of statistical methods use used to be neglected. This paper corrects this and introduces statistical methods as a very important part of business decision making and continuous monitoring of business processes

UPOTREBA ANALIZE SNAGE TESTA U PRONALAŽENJU ODGOVARAJUĆE VELIČINE UZORKA ZA POTREBE KONTROLE KVALITETE

Selection of an appropriate sample size is one of the most important aspects of any quality inspection. The right sample size enables making valid conclusions about the quality of the product with minimum necessary resources. The choice of sample size and the probability of Type II error (ß) are closely connected. The purpose of this paper is to examine how usage of a statistical power analysis can improve researcher’s decision about choosing an appropriate sample size for experimental purposes and quality inspection. Apart from sample size, the power of a study (1- ß) or the probability that the study will yield signifi cant results is determined by the magnitude of the treatment effect and by the level of statistical signifi cance required (α). The goal of power analysis is to balance values of these three factors to get an appropriate power. Impact of individual changes of each factor on the power was observed through a hypothetical statistical example based on a signifi cance test in which the diff erence between proportions of two independent samples was estimated. Power and Precision soft ware was used for calculations. Results of the power analysis have shown that increase in eff ect size, sample size and/or level of statistical signifi cance lead to an increase in power. This means if power increased and/or eff ect size and/or level of statistical significance decreased, a larger sample size would be required. This paper demonstrates the usefulness of using statistical power analysis in determining appropriate sample size and warns about possible consequences of erroneously selected sample size.Izbor odgovarajuće veličine uzorka jedan je od najvažnijih problema prilikom provođenja kontrole kvalitete. Odgovarajuća veličina uzorka omogućava donošenje valjanih zaključaka o kvaliteti proizvoda korištenjem minimalne razine potrebnih resursa. Izbor veličine uzorka i vjerojatnost pogreške tipa II (ß) usko su povezani. Cilj ovog rada je istražiti kako analiza snage testa može pomoći istraživaču u odabiru odgovarajuće veličine uzorka za potrebe provođenja određenog eksperimenta i kontrole kvalitete. Osim veličine uzorka, snaga testa (1- ß) odnosno vjerojatnost da će istraživanje pokazati signifikantne rezultate određena je veličinom efekta tretmana i razinom tražene razine signifi kantnosti (alfa). Cilj analize snage testa je da pronađe ravnotežu između tih tri faktora kako bi se postigla zadovoljavajuća razina snaga testa. Utjecaj pojedinačnih promjena svakog od faktora na vrijednost snage testa promatrano je primjenom hipotetičkog statističkog testa u kojem se testirala vrijednost razlike proporcija na dva nezavisna uzorka. Za potrebe izračuna korišten je program Power and Precision. Rezultati analize snage testa pokazali su da povećanje veličine efekta, veličine uzorka i/ili povećanje razine značajnosti utječe na povećanje snage testa. Iz navedenog proizlazi da se povećanjem snage testa, veličinom efekta i/ili povećanjem razine značajnosti smanjuje potrebna veličina uzorka. Ovim radom ukazuje se na korisnost primjene statističke analize snage testa u određivanju optimalne veličine uzorka te se upućuje na posljedice pogrešno izabrane veličine uzorka

ISTRAŽIVANJE URBANIH ZAKONITOSTI ZA HRVATSKU U RAZDOBLJU OD 1857. DO 2011. GODINE

Two main regularities in the field of urban economics are Zipf’s law and Gibrat’s law. Zipf’s law states that distribution of largest cities should obey the Pareto rank-size distribution while Gibrat’s law states that proportionate growth of cities is independent of its size. These two laws are interconnected and therefore are often considered together. The objective of this paper is the investigation of urban regularities for Croatia in the period from 1857 to 2011. In order to estimate and evaluate the structure of Croatian urban hierarchy, Pareto or Zipf’s coefficients are calculated. The results have shown that the coefficient values for the largest settlements in different years are close to one, indicating that the Croatian urban hierarchy system follows the rank-size distribution and therefore obeys Zipf’s law. The independence of city growth regarding the city size is tested using penal unit roots. Results for Gibrat’s law testing using panel unit root tests have shown that there is a presence of unit root in growth of settlements therefore leading to the acceptance of Gibrat’s law.Dvije temeljne zakonitosti na polju urbane ekonomike su Zipfov zakon i Gibratov zakon. Zipfov zakon nalaže da bi distribucija najvećih gradova trebala slijediti raspodjelu prema Pareto rangu veličine dok Gibratov zakon navodi da je proporcionalni rast gradova neovisan o njihovoj veličini. Ta dva zakona su međusobno povezana i stoga se često razmatraju zajedno. Cilj ovog rada je istraživanje urbanih zakonitosti za Hrvatsku u razdoblju od 1857. do 2011. godine. Kako bi se procijenila i ocijenila struktura hrvatske urbane hijerarhije, Pareto ili Zipfovi koeficijenti su izračunati. Rezultati su pokazali da vrijednosti koeficijenata za najveća naselja u različitim godinama iznose približno 1, što upućuje na zaključak da hrvatski urbani hijerarhijski sustav slijedi pravilo veličine ranka odnosno Zipfov zakon. Neovisnost rasta gradova u ovisnosti o njihovoj veličini ocijenjeno je pomoću testa jediničnog korijena. Rezultati testiranja Gibratovog zakona uz pomoć panel testa jediničnog korijena su pokazali da postoji prisutnost jediničnog korijena za rast naselja što upućuje na prihvaćanje Gibratovog zakona

Forecasting Stock Market Indices using Machine Learning Algorithms

In recent years machine learning algorithms have become a very popular tool for analysing financial data and forecasting stock prices. The goal of this article is to forecast five major stock market indexes (DAX, Dow Jones, NASDAQ, Nikkei 225 and S&P 500) using machine learning algorithms (Linear regression, Gaussian Processes, SMOreg and neural network Multilayer Perceptron) on historical data covering the period February 1, 2010, to January 31, 2020. The forecasts were made by using historical data in different base period lengths and forecasting horizons. The precision of machine learning algorithms was evaluated with the help of error metrics. The results of the analysis have shown that machine learning algorithms achieved highly accurate forecasting performance. The overall precision of all algorithms was better for shorter base period lengths and forecast horizons. The results obtained from this analysis could help investors in determining their optimal investment strategy. Stock price prediction remains, however, one of the most complex issues in the field of finance

A Machine Learning Approach to Forecast International Trade: The Case of Croatia

Background: This paper presents a machine learning approach to forecast Croatia\u27s international bilateral trade. Objectives: The goal of this paper is to evaluate the performance of machine learning algorithms in predicting international bilateral trade flows related to imports and exports in the case of Croatia. Methods/Approach: The dataset on Croatian bilateral trade with over 180 countries worldwide from 2001 to 2019 is assembled using main variables from the gravity trade model. To forecast values of Croatian bilateral exports and imports for a horizon of one year (the year 2020), machine learning algorithms (Gaussian processes, Linear regression, and Multilayer perceptron) have been used. Each forecasting algorithm is evaluated by calculating mean absolute percentage errors (MAPE). Results: It was found that machine learning algorithms have a very good predicting ability in forecasting Croatian bilateral trade, with neural network Multilayer perceptron having the best performance among the other machine learning algorithms. Conclusions Main findings from this paper can be important for economic policymakers and other subjects in this field of research. Timely information about the changes in trends and projections of future trade flows can significantly affect decision-making related to international bilateral trade flows

Modelling Croatian Export Dynamics Using Global Macroeconometric Model

Five years following the occurrence of the global economic and financial crisis, Croatia is one of the few countries in the region whose export has still not recovered to the pre-crisis level. In order to properly account for international linkages and possible crisis spillover effects, a Global Vector AutoRegressive (GVAR) model is defined. The GVAR model is a consistent global macroeconometric model which enables modelling interactions between Croatia and a set of Central and Southeast European (CSEE) countries. The empirical analysis reveals that the domestic variables are the main factor explaining Croatian export dynamics in the short run. However, in the long run, the main determinants of Croatian export are the US and German real exchange rates. These findings provide evidence in favour of low competitiveness of Croatian export

Statistical Control Charts: Performances of Short Term Stock Trading in Croatia

Background: The stock exchange, as a regulated financial market, in modern economies reflects their economic development level. The stock market indicates the mood of investors in the development of a country and is an important ingredient for growth. Objectives: This paper aims to introduce an additional statistical tool used to support the decision-making process in stock trading, and it investigate the usage of statistical process control (SPC) methods into the stock trading process. Methods/Approach: The individual (I), exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts were used for gaining trade signals. The open and the average prices of CROBEX10 index stocks on the Zagreb Stock Exchange were used in the analysis. The statistical control charts capabilities for stock trading in the short-run were analysed. Results: The statistical control chart analysis pointed out too many signals to buy or sell stocks. Most of them are considered as false alarms. So, the statistical control charts showed to be not so much useful in stock trading or in a portfolio analysis. Conclusions: The presence of non-normality and autocorellation has great impact on statistical control charts performances. It is assumed that if these two problems are solved, the use of statistical control charts in a portfolio analysis could be greatly improved

Modeling stock market volatility in Croatia: A reappraisal

Purpose: In this paper, the volatility of the Croatian stock market index CROBEX is investigated using the GARCH(1,1) model. Methodology: The novelty provided by this paper is the estimation of the GARCH(1,1) model by using three conditional error distributions (normal (Gaussian) distribution, Student’s-distribution with fixed degrees of freedom and generalized error distribution (GED) with fixed parameters). Results: The findings obtained in the research are in the line with previous research in this field (Erjavec & Cota, 2007; Sajter & Ćorić, 2009). The volatility of CROBEX returns is positively correlated with the volume of trade on the Zagreb Stock Exchange and movements on the main European and American stock markets. The movement of S&P 500 stock market index returns is transmitted from the previous day, providing signals for the direction of change of CROBEX index returns in the present. Conclusion: Therefore, this paper provides evidence that investors in Croatia strongly rely on the past information received from the American S&P500 stock market index. Furthermore, there seems to exist the co-movement between CROBEX and main European indexes on the same trading day