Jarque-Bera Research Articles

RENEWABLE ENERGY USE AND ITS EFFECTS ON ENVIRONMENT AND ECONOMIC GROWTH: EVIDENCE FROM MALAYSIA

The purpose of this study was to analyse the impact of renewable energy use in Malaysia on the environment and economic growth and directing study findings to stimulate developing economies in Arab region to evaluate its economic returns from renewable energy uses. In this study, a set of time series data has been used which is ranging from the year 1989 to 2018. Moreover, the stationary of the data was tested through Augmented-Dickey Fuller (ADF) test. The normality test of the data distribution was also conducted with the help of Jarque Bera test. In addition to the methods, due to the existence of unit roots, an ARDL technique was also incorporated which tested the influence of renewable energy use on the growth and environment of Malaysia. The findings of the study imply that the renewable sources usage in Malaysia was found to have a positive and significant relation on the CO2 emissions in the case of Malaysia. Furthermore, the findings of the study also suggested that there is no significant relation between renewable energy use and GDP. Also, it was identified that there was no long-run influence in the variables and hence a short-run influences were recognised.

Study of the Impact of Different Investor Behaviours on Market Volatility

The purpose of this research is to study the impact of different investor behavior on the volatility of the bourse market. The field study consists of the companies listed on the stock exchange during the years 2012-2016. In this study, the different investor behaviour is considered the independent variable and the market volatility is the dependent variable. The present research is an applied study, in case the classification of researches in characteristics and methodology is considered, this study is considered descriptive research based on its characteristics and it is in the correlation study category based on its methodology. In this study collecting data and information has been done by library method and the data compilation has been fulfilled by referring to the financial statements, explanatory notes and monthly magazine of the bourse. In sample size determination based on data collecting system, 114 companies have been selected as the sample statistics. In order to describe and summarize the collected data descriptive and inferential statistics have been utilized. In analyzing the data first pre-test of variance homogeneity, Limer F test, Hausman test and JB test and then the multivariable regression (Eviews software) for confirmation or rejection of the hypothesis of the research has been used. The results show that the different investor behaviour consisting of the trading value of capital, investors net business flow and the investor trading volume share has a considerable impact on the volatility of the stock exchange.

Impact of Macroeconomic Variables on Stock Return Volatility: Evidence from Sub-Sahara Africa

The main purpose of this paper was to investigate the impact of macroeconomic variables on stock market return volatility in Sub-Sahara markets. The study concentrated on three stock markets including Ghana, Nigeria and South Africa using GARCH-X (1,1) model on monthly data from January 2000 to December 2017. Preliminary analyses from descriptive statistics show that show mean monthly returns are positive for all the stock markets. Skewness coefficients show that the stock returns and interest rates distribution of all Sub-Sahara Africa stock markets are negatively skewed but inflation rate is positively skewed for Nigeria and South Africa, and flat for Ghana. Excess kurtoses are positive for all the stock markets and macroeconomic indicators, and Jarque-Bera statistics indicate the stock markets’ series and macroeconomic indicators are not normally distributed. The Unit roots tests results indicate that all the stock markets and macroeconomic indicators are first difference stationary. The results of the GARCH-X (1,1) model show that macroeconomic variables do not significantly impact stock market returns volatility in Nigeria, Ghana and South Africa at the 5% significance Level. We therefore recommend that stock market regulators, market participants and investors should concentrate more efforts on other macroeconomic variables aside interest rate and inflation rate, in estimating stock market return volatility in Sub-Sahara Africa.

Predictability of Stock Returns on the Dhaka Stock Exchange

The study examines the predictability of index returns on the Dhaka stock market within the framework of the weak-form efficient market hypothesis using historical daily returns for a period of 1st June, 2014 to 29th May, 2020. The Jarque-Bera statistics test explored the return distribution of Dhaka Stock Exchange is non-normal. The random walk hypothesis (RWH) was tested using autocorrelation test, runs test, unit root tests(Augmented Dickey-Fuller (ADF) and, Phillip-Perron (PP) test) and variance ratio test. The results explored that all tests rejected the random walk hypothesis required by the weak-form efficient market hypothesis. This provides empirical basis to infer that the DSE is inefficient at weak-form and stock return can be predicted. The rejection of the RWH on a daily basis is possibly an indication that the weak-form inefficient characteristic of the DSE is not sensitive to return frequency.

Sustainability of Railway Undertaking Services with Lean Philosophy in Risk Management—Case Study

The sustainability of services in undertakings which operate in railway passenger transport is closely connected with efforts to provide high-quality and time-acceptable services to the customer, to achieve lean processes, to standardise work processes, to eliminate wastage and to avoid those activities which do not add any value. Considering its ecological nature and big volume of passenger transport, railway passenger transport represents an irreplaceable sustainable system for the future. These facts and the entry of new railway carriers into this system highlight the importance of managing risks in a progressive way. This article focuses on the options of risk management when versatile and flexible tools, such as Lean tools, are set correctly. The aim of this article is to introduce a proposal for the application and a specific application of Lean philosophy in risk management which impacts train delays in railway passenger transport. From the point of view of quantitative data analysis, multiple mathematical-statistical tests have been used to assess data (e.g., Anderson–Darling test, Kolmogorov–Smirnov test, Jarque–Bera test, two-tailed test, Grubbs’s test and Thompson Tau test). Also, descriptive statistics and ANOVA test (Analysis of Variance) have been applied in the process of evaluating the monitored attributes. In order to assess the data dependency, Chi-Square Test and a correlation analysis have been applied. The research outcomes focus on highlighting the need for a systematic approach for train delays and to avoid delays through risk management. The case study applies selected Lean methods to monitor train delays in selected stations within the main railway corridor of the Slovak Republic for the period from 2015 to 2018. In 2019, the results were processed and created a basis for proposing a unique software tool like an aid for risk management in the case of train delays using Lean philosophy. The flexible software includes the participation of employees, and at the same time, it respects the needs of customers. The research results provide grounds for further study of risks in a challenging environment of railway undertakings, with different types of trains, railway stations as well as entire railway networks. The processed results of the article represent valuable material for railway undertakings not only in Slovakia but also for railway organisations in V4 countries.

In day 150 of COVID-19 disease, forecasting the number of cases and deaths in Turkey

Objective: This study aims to forecast the number of deaths and cases in Turkey 150 days after (6 August 2020) the first occurrence of COVID-19 in Turkey. The data used is from 10 March 2020 (the first day has seen of COVID-19 in Turkey) to 15 June 2020 and includes people of all ages from all provinces of Turkey.
 Material and Method: The relationship between cases, deaths, patients in intensive care units, intubated patients, and recovered patients, which are observations of COVID-19, was examined with a correlation matrix. Afterward, the ARIMA (0,2,4) model to forecast the number of COVID-19 cases in Turkey and the ARIMA(0,3,1) model to forecast the number of COVID-19 deaths in Turkey were established.
 Result: COVID-19 cases were forecasted that there may be 266.692 cases in Turkey on 6 August in the 1st model. Subsequently, a similar forecast has been made on COVID-19 deaths in Turkey on 6 August in the 2nd model. COVID-19 deaths were forecasted that there may be 5718. The p-values of these parameters of models were observed statistically significant (p<0.05). Later, the stationarity of ARIMA models related to these estimates was examined. According to the Augmented Dickey-Fuller (ADF) test results, ARIMA models were stationary and statistically convenient to use (p<0.05). Finally, the Jarque-Bera (JB) test examining the normal distribution assumption was applied and the models were found to be normally distributed.
 Conclusions: Consequently, there is an increase in both predicted cases and predicted deaths by the 150th day of COVID-19. These estimates show that the number of cases and deaths will not decrease to zero level until August 6. Factors such as the biological development of the COVID-19 virus, the rate of spread of COVID-19 disease, or the presence of COVID-19 therapy may not cause any increase in these observations. On the contrary, more than expected increase may occur in observed cases.

Cointegrating Relationship between Macroeconomic Variables and Stock Market Prices in Nairobi Securities Exchange

The study of stock market prices movements and macroeconomic indicators has been imperative in view of the country’s economic growth because the most sensitive segment of any developing economy is its stock market. The buy and sell decision rule are affected by the investor’s psychology which exerts influence on the macroeconomic events. The very critical question when it comes to this is that how instantaneous the information is transferred to the investors and market analyst and in return reflects on stock market prices. Therefore, the purpose of this paper was to analyze cointegrating relationship between macroeconomic indicators and the stock market prices in the context of Nairobi Securities Exchange. The paper used longitudinal research design using monthly secondary data for the period 2005 to 2018. The data were sourced from NSE, KNBS and Central Bank of Kenya. Augmented Dickey Fuller test confirmed the presence of unit root at levels for some variables, and all the variables attained stationarity after first difference. The Optimum lag length selected was 3. Johansen cointegration test showed that the variables were cointegrated thus Vector Error Correction Model was used to estimate the parameters. The error correction term was -1.1804 and significant at p-value 0.000 indicating a long-term existence between variables and the stock market prices. Jarque-Bera test showed the residuals followed normal distribution. Inflation and interest rate negatively and significantly affected stock market prices at coefficient -0.8371 (p-value 0.005) and -4.0876 (0.000) respectively. However, Exchange rate and nominal GDP had positive and significant effects on stock prices at 0.0001 (p-value=0.012) and 0.00002 (p-value=0.000) respectively. It was recommended based on the findings that the government should adopt expansionary monetary policy to by regulating interest rate and stabilizing exchange rate to create more money for investors. There is need for the government to encourage activities that increases GDP since it is an important macroeconomic indicator for health economy.

Statistical normality and homogeneity of a 71-year rainfall dataset for the state of Rio de Janeiro—Brazil

Studies are scarce on the application of normality and homogeneity tests for rainfall series in the state of Rio de Janeiro, Brazil. Therefore, this study applies normality and homogeneity tests in a 71-year time series (1943–2013) of rainfall, seeking to identify which tests were most indicated in the evaluation of the normality and homogeneity of rainfall data in Rio de Janeiro. The tests of normality and homogeneity of variance were divided into (i) parametric—Shapiro-Wilk (SW) and Jarque-Bera (JB)—and (ii) non-parametric—Bartlett (B) and Fligner-Killeen (FK). All statistical procedures were performed using the open software R version 3.4.2. The tests of normality (SW and JB) and homogeneity of variance (B and FK) applied to the raw (unfilled) data showed that both the SW and JB tests were not satisfactory in determining the normality of the series, with only two stations reaching over 95% reliability. Regarding the homogeneity of variance of the standardized residues in the raw data, the B test stands out when compared to the FK test. After completing data faults of the data, the SW, JB, B, and FK tests pointed to rejection of the normality and homogeneity hypotheses for the specific time series. According to the adopted methodology, there are 15 useful stations (65.22%), 7 dubious (30.43%), and one suspicious (4.35%). The SW and B tests presented better results when matched to the other tests. The proposed methodology should be considered for further investigation of normality and homogeneity in other climate datasets.

In vitro glistening formation in IOLs: automated method for assessing the volumetric density and depth distribution of microvacuoles.

To develop a method to measure the depth profile of microvacuoles (MVs) in intraocular lenses (IOLs) and to characterize, after accelerated aging, the glistening of an acrylic hydrophobic IOL. University of Milano-Bicocca, Milan, Italy. In vitro study. A heat treatment was applied in vitro to Basis V IOLs exposed to deionized water (24 hours at 45 ± 1°C, rapid cooling, and 24 hours at 24 ± 1°C). Thirty images (area 1.2 mm) of each IOL were acquired by a microscope, focusing on sequential planes every 23 ± 2 μm. By tracking the traces of each MV in consecutive images, the coordinates of the MV centroids along the IOL thickness were construed by an automated procedure, and in the generated single-focus stacked image, MVs were counted by an automated method. MV density was found normally distributed along the IOL depth profile (Jarque-Bera test). In focus-stacked images, the MV automated counting was found accurate within 5% vs manual counting, and MV volume density of the order of 10 mm was estimated. It was observed that stacks of 15 images provided a 4% lower MV volume density compared with the stacking of 30 images. The assessment of the number of MVs by the acquisition of a single image of an IOL was influenced by the distance of the selected plane from the IOL surface. The decrease in MV density approaching the IOL edges can be explained as a consequence of the diffusion of water toward the external environment after accelerated aging.

ARE FINANCIAL INVESTORS PRONE TO EXOGENOUS (CRICKET) SENTIMENTS THAT AFFECTS EQUITY INVESTMENT DECISION AND INDUCES VOLATILITY IN STOCK MARKET?

This paper examines the volatility in stock returns due to mood-swings of financial investors affected by the outcome of one-day international (ODI) cricket matches played by Pakistan against cricketing nations. The impact of matches is analyzed on same-day and for next-day volatility in returns by using Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH 1,1) and Glosten, Jagannathan & Runkle (GJR 1,1) methodology, supported by Engle (arch), L-Jung Q-stats (auto-correlation) and Jarque-Bera (normality) tests. Empirical time-series results show volatility can be predicted through past volatility and can be generalized. The win or loss position of Pakistan in ODI has a significant influence on next day volatility of stock returns. However, GJR analysis provides strong evidence of asymmetric behavior on next day in Karachi Stock Exchange (KSE)-100 index, states bad-news resulting from ODI matches has a significant negative influence on the next-day volatility of stock returns, due to less trading on the subsequent day of the match.

New fat-tail normality test based on conditional second moments with applications to finance

In this paper we introduce an efficient fat-tail measurement framework that is based on the conditional second moments. We construct a goodness-of-fit statistic that has a direct interpretation and can be used to assess the impact of fat-tails on central data conditional dispersion. Next, we show how to use this framework to construct a powerful normality test. In particular, we compare our methodology to various popular normality tests, including the Jarque–Bera test that is based on third and fourth moments, and show that in many cases our framework outperforms all others, both on simulated and market stock data. Finally, we derive asymptotic distributions for conditional mean and variance estimators, and use this to show asymptotic normality of the proposed test statistic.

Inflation and Standard of Living in Nigeria

Standard of living is very germane to every economy. It gives a broad view of how the economy is fairing on a global scale. If an individual cannot get required basic necessities due to low purchasing power, his/her marginal propensity to consume (MPC) rises which makes it more difficult for the individual to live comfortably hence the standard of living dips Several researches have been carried out but the standard of living still remains abysmally low. From empirical review we find that several countries in sub-Sahara Africa, Nigeria inclusive suffers from low standard of living. The case of Nigeria being evidently sever as the Nigeria currently the poverty capital of the world with over 91 million people living below in abject poverty. Time series data on inflation rate and standard of living proxied by the Human Development Index (HDI) between 1998 and 2017 was used for this research. Augmented Dickey Fuller and Phillip-Perron unit root tests were used to test for stationarity of the data. Based on findings, the Auto Regressive Distributed Lagged (ARDL) model was adopted for inferential analyses. Descriptive statistics employed include skewness, kurtosis, Jarque-Bera test and Breuch-Pagan-Godfrey serial correlation LM test, Breuch-Pagan test for heteroscedasticity and the Durbin-Watson test. Results indicated that there exists a long-run relationship between Inflation and standard of living. Inflation exhibited a negative and significant effect with a coefficient of -0.034 against a P-value of 0.017 which implied that a unit increase in inflation brings about 0.034unit decrease in standard of living over the period of study. Based on findings we recommend that a proper blend of fiscal and monetary policies should be employed to improve the standard of living of Nigerians. Keywords: Inflation rate, Monetary policies, Poverty, Standard of living, Fiscal policies DOI: 10.7176/DCS/10-4-06 Publication date: April 30 th 2020

An Autoregressive Distributed-Lag Modeling Approach to Nigeria’s External Reserves Dynamics

The objective of this study was to develop and analyze a regression model that explains the dynamics of foreign exchange reserves in Nigeria by examining the short-run and long-run impacts of some macroeconomic variables (exchange rate, inflation, interest rate, crude oil price, and real gross domestic product) on foreign reserves in Nigeria from 1986 to 2018. The data were sourced from the publications of the Central Bank of Nigeria (CBN) and World Bank. Unit root and co-integration tests were performed on the variables before estimation. An empirical multivariate autoregressive distributed-lag model (ARDL) was identified, specified, and estimated with the aid of EViews econometric software. Diagnostic tests for serial correlation (Breusch-Godfrey serial correlation LM test), normality (Jarque-Bera test), stability (CUSUM-of-squares test), and forecasting performance test were conducted to evaluate the estimated model. The study found that the estimated ARDL model could provide information on the behaviour of foreign exchange reserves in Nigeria. The regression results showed that none of the explanatory variables (exchange rate, inflation rate, interest rate, crude oil price, and real gross domestic product) shared contemporaneous and lagged relationship with foreign reserves dynamics in Nigeria. However, in the long run, only the previous value of foreign reserves was significant in explaining foreign reserves dynamics in Nigeria during the sample period. Given that foreign reserves play an important role in the design and evaluation of current and future macroeconomic policies aimed at achieving trade balance, the study makes the following recommendations: (1) government policies directed at managing and improving foreign reserves should largely consider the short-run and long-run behaviour of foreign reserves and these policies should be pursued with high degree of transparency because foreign reserves dynamics largely find explanation in adaptive expectation in Nigeria, (2) policy makers should ensure the evolution of appropriate exchange rate policies and regimes that will enable the economy build its reserves and adequate and balanced reserves should be accumulated to help to smoothen out the volatility in the exchange rate only in the short run, (3) government and the relevant agencies should block all foreign reserves leakages in the economy by ensuring that foreign exchange allocation and utilization are purely for genuine economic purposes and build fiscal buffers through the replenishment of the Excess Crude Account or the Sovereign Wealth Fund, and (4) the implementation of appropriate policies to facilitate the diversification of the Nigerian economy along areas of key comparative advantage (such as agriculture and small scale labour-intensive manufacturing).

Prefeasibility Study of Photovoltaic Power Potential Based on a Skew-Normal Distribution

Solar energy does not always follow the normal distribution due to the characteristics of natural energy. The system advisor model (SAM), a well-known energy performance analysis program, analyzes exceedance probabilities by dividing solar irradiance into two cases, i.e., when normal distribution is followed, and when normal distribution is not followed. However, it does not provide a mathematical model for data distribution when not following the normal distribution. The present study applied the skew-normal distribution when solar irradiance does not follow the normal distribution, and calculated photovoltaic power potential to compare the result with those using the two existing methods. It determined which distribution was more appropriate between normal and skew-normal distributions using the Jarque–Bera test, and then the corrected Akaike information criterion (AICc). As a result, three places in Korea showed that the skew-normal distribution was more appropriate than the normal distribution during the summer and winter seasons. The AICc relative likelihood between two models was more than 0.3, which showed that the difference between the two models was not extremely high. However, considering that the proportion of uncertainty of solar irradiance in photovoltaic projects was 5% to 17%, more accurate models need to be chosen.

Supporting Factors in Forming Islamic Village-Owned Enterprise (I-BUMDes) in Nagan Raya District

It is important and urgent to conduct a scientific study on the establishment of Islamic Village-Owned Enterprises (I-BUMDes) through the local potential to realize independent villages, by analyzing local potential variables, financial capital, human resources, and community participation in the establishment of I-BUMDes in Nagan Raya District. The population of this study was all villages that have Bumdes in Nagan Raya district. The sampling technique used was purposive sampling. The study sample was 10 I-BUMDes that have run in Nagan Raya district. Primary data was obtained by interviews and questionnaires to I-BUMDes administrators and communities in the local area. Secondary data was obtained from the Central Bureau of Statistics (BPS), Regency Community Empowerment Agency (BPM), and the internet. Regression analysis was performed to determine whether an independent variable has an effect on the dependent variable by using the regression model estimation by entering a dummy variable, the joint hypothesis test model. Significance test was carried out using the normality test method through the Jarque-Bera (JB) statistical test through histogram screening, with a significance level of 5% indicating that the statistical value was 163,393 while the square chi value α = 5% and (df = n-2 = 130-2 = 128, α = 5%). Therefore, the Ho assumption is accepted which means the norm is distributed data. The regression results using qualitative variables can be seen, showing different results and in accordance with the hypothesis that was built. The regression results by entering the dummy variable show a positive and significant effect at α = 5%, towards the formation of I-BUMDes in Nagan Raya District. Only human power variables do not affect the formation of I-BUMDes with an error term value of α = 5%. DOI: https://doi.org/10.5281/zenodo.3672391

Was Quetelet’s Average Man Normal?

Quetelet’s data on Scottish chest girths are analyzed with eight normality tests. In contrast to Quetelet’s conclusion that the data are fit well by what is now known as the normal distribution, six of eight normality tests provide strong evidence that the chest circumferences are not normally distributed. Using corrected chest circumferences from Stigler, the χ2 test no longer provides strong evidence against normality, but five commonly used normality tests do. The D’Agostino–Pearson K2 and Jarque–Bera tests, based only on skewness and kurtosis, find that both Quetelet’s original data and the Stigler-corrected data are consistent with the hypothesis of normality. The major reason causing most normality tests to produce low p-values, indicating that Quetelet’s data are not normally distributed, is that the chest circumferences were reported in whole inches and rounding of large numbers of observations can produce many tied values that strongly affect most normality tests. Users should be cautious using many standard normality tests if data have ties, are rounded, and the ratio of the standard deviation to rounding interval is small.

Detection of Non-Normality in Data Sets and Comparison between Different Normality Tests

The paper provides five tests of data normality at different sample sizes. The tests are the Shapiro-Wilk (SW) test, Anderson-Darling (AD) test, Kolmogorov-Smirnov (KS) test, Ryan-Joiner (RJ) test, and Jarque-Bera (JB) test. These tests were used to test for normality for two secondary data sets with sample size (155) for large and (40) for small; and then test the simulated scenario with standard normal “N(0,1)” data sets; where the large samples of sizes (150, 140, 130, 130, 110 and 100) and small samples of sizes (40. 35, 30, 25, 20, 15 and 10) are considered at two levels of significance (5% and 10%). However, the aim of this paper is to detect and compare the performance of the different normality tests considered. The normality test results shows Kolmogorov-Smirnov (KS) test is a most powerful test than other tests since it detect the simulated large sample data sets do not follow a normal distribution at 5%, while for small sample sizes at 5% level of significance; the results showed the Jarque-Bera (JB) test is a most powerful test than other tests since it detects that the simulated small sample data do not follow a normal distribution at 5%. This paper recommended JB test for normality test when the sample size is small and KS test when the sample size is large at 5% level of significance.

Forecasting hourly emergency department arrival using time series analysis

Background/aims The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients. Methods Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals. Results The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained. Conclusions The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.

Testing normality of chosen R-estimates used in deformation analysis

Abstract The normal distribution is one of the most important distribution in statistics. In the context of geodetic observation analyses, such importance follows Hagen’s hypothesis of elementary errors; however, some papers point to some leptokurtic tendencies in geodetic observation sets. In the case of linear estimators, the normality is guaranteed by normality of the independent observations. The situation is more complex if estimates and/or the functional model are not linear. Then the normality of such estimates can be tested theoretically or empirically by applying one of goodness-of-fit tests. This paper focuses on testing normality of selected variants of the Hodges-Lehmann estimators (HLE). Under some general assumptions the simplest HLEs have asymptotical normality. However, this does not apply to the Hodges-Lehmann weighted estimators (HLWE), which are more applicable in deformation analysis. Thus, the paper presents tests for normality of HLEs and HLWEs. The analyses, which are based on Monte Carlo method and the Jarque–Bera test, prove normality of HLEs. HLWEs do not follow the normal distribution when the functional model is not linear, and the accuracy of observation is relatively low. However, this fact seems not important from the practical point of view.

Alter Ego Only Four Times? The Case Study of Business Profits in the Visegrad Group

The paper studies a new point of view and the approach to profit as an inherent part of business finance as well as a symbol of every healthy economy. The fundamental function of the profit is a stimulus; it means initial motivator of the business activity. The profit provides core resources for survival at the business start and after the stabilization, it is the synonym for progress. The aim of this paper is to detect significant change-points in times series of EBITDA during the analysed period in every country of the Visegrad Group to recognize the progress years in the monotonic development. We use a method of homogeneity test of time series that delivers significant robust results. We observe the variable EBITDA to eliminate different tax, interest and depreciation policies of these emerging countries. The original research of this article is based on empirical results of business profits of the sample of 3,853 enterprises covered by the broad theoretical review. Firstly, we identify missing values; and detect the outliers by Z-score and Grubbs test. EBITDA of 1,058 Slovak enterprises, 688 Czech enterprises, 1,376 Polish enterprises and 731 Hungarian enterprises is analysed during the period from 2010 to 2018. We eliminate the inconsistent observations and construct average values of EBITDA. Secondly, we prove normality by Jarque-Bera test, and support it by Shapiro-Wilk test, Anderson-Darling test, Lilliefors test to deliver reliable results. Thirdly, we find an independency of distribution that confirm randomness by the Box-Pierce test. And finally, we identify the years that affect heterogeneity of EBITDA in the countries of the Visegrad Four. We uncover some really surprising results. For all countries in the Visegrad Four, the year 2013 is detected as a change-point at a significance level of 0.05. This significant year shifts EBITDA between two homogeneous series with corresponding central lines and recognizes the similar annual development within the groups. In addition, we discuss the results to the areas and factors affecting the business risk. The adjustable area represented by the business dynamism has no significant impact on the development of EBITDA. The uncontrollable macroeconomic factors such as a GDP, unemployment rate, inflation rate, average monthly gross wage, and Ease of doing business index demonstrate the same development of Slovak, Czech, Polish and Hungarian enterprises. We connect our gained results to the undisputed influence of these factors and its derived components on monotonic development of EBITDA. Despite the fact, that the countries are not economically interconnected as they used to be in the past, in has to be underlined that their mutual relations are still very narrow and close and that might be the reason, why identical results are achieved in the countries with divergent development.