The results of happiness analysis are presented in the form of a World Happiness Report that covers 156 countries and 17 different indicators. In the article model-based clustering ensemble is built to determine what selected European countries have similar patterns of happiness. The results are analyzed using multidimensional scaling and a decision tree to find out what factors determine cluster memberships. In the empirical part, three clusters were detected The first contains countries: Austria, Denmark, Finland, Germany, Ireland, Luxembourg, the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. They have the highest values for all the variables, except the negative affect. The second cluster contains seven countries: Bulgaria, Estonia, Hungary, Lithuania, Poland, Romania and Slovakia. This cluster is also the most homogeneous one. The third cluster contains eight countries: Cyprus, the Czech Republic, France, Greece, Italy, Portugal, Slovenia and Spain.
In videogames industry, time series analysis can be very useful in determining the general evolution and behaviour of the market dynamics. These methods are applicable to any time series forecasting problem, regardless of the application sector. This article discusses time series approaches to forecast the sales of console games for the Italian market. In particular two univariate techniques were evaluated, exponential smoothing and the SARIMA technique. The aim is to exploit the capabilities of these statistical methods in order to have a comparison of the results and to choose the most accurate model through an ex-post evaluation. Using monthly time-series data from November 2005 to September 2017, the selection of the most suitable model was indicated by the smallest value of the measures of accuracy (MAPE, sMAPE, RMSE) for the out-of-sample observations regarding the period October 2017-September 2018. The implementation of the models was done using Forecast PRO and Gretl. The time series involved is related to the sales regarding the first party manufacturers of consoles and handhelds (Microsoft, Sony and Nintendo).
Good graphical presentation of data is useful during the whole analysis process from the first glimpse into the data to the model fitting and presentation of results. The most popular way of longitudinal data presentation are separate (for each wave, in cross-sectional dimension) comparisons of figures. However, plotting the data over time is useful in suggesting appropriate modeling techniques to deal with the heterogeneity observed in the trajectories. The main aim of this paper is to present the changing perceptions of the financial situation in Poland using different graphical tools for the heterogonous discrete longitudinal data sets and present demographics features for those changes. We will focus on the most important features of the categorical longitudinal data – category sequences and their graphical presentation. We aim to characterize the analyzed sequences on the basis of unidimensional indicators and composite complexity measures, as well as using mainly TraMineR [Gabadinho et al. 2017] package of R.
In a duration analysis of enterprises, as a rule there are determined four basic functions related to the time of their duration, i.e.: the density function; the distribution function; the survival function, and the hazard function. It turns out that the hazard function and its cumulative version are the key to understanding modern survival analysis. The aim of the paper is to indicate the best method of the estimation of the values of individual functions in survival analysis based on other functions. The paper provides compiled and classified information on particular functions used in the non-parametric duration analysis of enterprises. It examines some theoretical and practical problems related to the determination of, among others, the hazard function and the cumulative hazard function on the basis of data in cohort tables and the results of the estimation of the survival function with the use of the Kaplan-Meier method. The considerations included in the paper are illustrated with the results of analyses for enterprises established in the Łódzkie Voivodeship in 2001-2015 (including those which went into liquidation).
Statistical data on foreign trade are collected in all EU member states separately and then passed on to Eurostat where the data are aggregated. Continuous actions are to ensure that all datasets collected at national level are fully comparable. The aim of the paper is to provide a classification as well as an ordering of CN chapters (2-digit codes) according to the quality of data on intra-Community trade of goods. Data were taken from Eurostat’s COMEXT database. In ordering the chapters, we utilized the distance from the ideal solution with GDM as the distance measure. The study reveals a structure of goods subject to intra-Community trade that is supplementary to the official nomenclature. In addition, we provided CN chapters ordering according to the overall level of irregularities in reported mirror values of ICS and ICA. The results we obtained are of practical value for both researchers and authorities interested in foreign trade.
The last financial crisis affected the SMEs sector in different countries at different levels and strength. SMEs represent the backbone of the economy of every country. Therefore, they need bankruptcy prediction models easily adaptable to their characteristics. In our analysis we verified hypothesis: including information about macroeconomic conditions significantly increases the effectiveness of the bankruptcy model. The data set used in our research contained information about 1,138 SMEs. All information was taken from the financial statements covering the period 2002-2010. The sample included enterprises from sectors: industry, trade and services. Selected financial ratios were used to build the model and the macroeconomic variables were added: GDP, inflation, and the unemployment rate. Logistic regression as the research method was applied. In our study we showed that the incorporation of the macro variables improved the prediction of the SMEs bankruptcy risk.
The creation of an effective growth policy requires the identification of its key determinants. The study used one of the methods of multidimensional analysis – discriminant analysis. It is widely used on a microeconomic scale, especially in the area of forecasting bankruptcy of enterprises, but in the area of economic growth, it has not been used in practice so far. In addition to the main objective of identifying the most important economic growth factors of the European Union countries in 2000-2016, the impact of the crisis and accession to the EU was examined. The statistical data sources were the databases of Eurostat and the Conference Board (Total Economy Database). The results obtained allowed us to conclude that the rate of Gross Domestic Product growth in the EU countries was determined by consumption, investment, export and labour productivity, and in periods of economic slowdown also public debt. The enlargement of the EU resulted in an increase in the importance of export.
The aim of the research was an assessment of the relative risk of liquidation of a company depending on its age. The research covered economic entities established in Szczecin in the period 1990-2010. The analysis was carried out with the use of a logit model. The risk of company liquidation was examined depending on the entity’s age expressed both in months (continuous variable) and in grouped intervals (year, half-year). In this way, attention was drawn to the benefits of continuous variable coding (rank and 0-1 coding). The research covered companies established during 1990-2010 in total (over 120 thousand) and in time periods resulting from the cyclical character of liquidation of companies (in accordance with the earlier research findings). The research showed that the risk of company liquidation decreases as the company grows older (the use of a continuous variable and a rank variable). On the other hand, the risk of subsequent age groups (using the 0-1 variable) prevents the risk from being monotonous.
This paper presents the methods for the evaluation of budget variance risk, i.e. the risk of a difference between the budgeted and actual figures. The postulated approach is based on extreme value analysis (EVA), to offer, among other things, the evaluation of maxima distribution parameters for studied phenomena. The proper recognition of these parameters yields potential for calculation of probabilities for budget variance to pass certain levels established as critical. This methodology can be used to evaluate deviation levels by time period, and to compare them against historical data. The main objective of this paper was to examine the utility of the theory of extreme values in the estimation of budget deviation risks. The study presents the results of probabilistic analyses of data obtained from a budgetary cost control unit of a production company located in eastern Poland, for the period of 2011-2012. The developed method of analysis and assessment of budget deviations is in line with the development of concepts and methods of management accounting.
The beta parameter is a popular tool for the evaluation of portfolio performance. The Sharpe single-index model is a simple regression model in which the stock’s returns are regressed against the returns of a broader index. The beta parameter is a measure of the strength of this relation. Extensive recent research has proved that the beta is not constant in time and should be modelled as a time-variant coefficient. One of the most popular methods of the estimation of a time-varying beta is the Kalman filter. As the output of the Kalman filter, one obtains a sequence of the estimates of a time-varying beta. This sequence shows the historical dynamics of sensitivity of a company’s returns to the variations of market returns. The article proposes a method of clustering companies listed on the Warsaw Stock Exchange according to time-varying betas.