Profiling Some of the Dire Household Debt Determinants: A Metric Multidimensional Scaling Approach

The purpose of this paper was to use the metric Multidimensional scaling (MDS) to explore the ten dire household debt determinants in the context of South Africa. Macroeconomic data used was collected from the South African reserve bank and Statistics South African websites for the first quarters of 1990 to 2013. SPSS 22 was used to execute the analysis. A Standardized Residuals Sum of Squares (STRESS 1) measure calculated as 0.00077confirmed the best fit of the MDS model and the Tucker’s Coefficient of Congruence implied that 99.9% of variance in the model is accounted for by the two dimensions. This was also a confirmation that the ten selected determinants can better be represented in a two dimensional perpetual map. The findings revealed two profiles of household debts. Gross domestic product and house prices are associated with high levels of household debts. The remainder of the determinants is found to have low effects. MDS demonstrated its effectiveness in classifying household debt determinants according to their contribution. Also revealed is that an MDS is a useful tool to use in quantifying the ubiquitous, but slimy, notion of similarity

The robustness and accuracy of Box-Jenkins ARIMA in modeling and forecasting household debt in South Africa

Abstract: This paper adopted the Box-Jenkins methodology to estimate a univariate time series model. Quarterly data collected from the South African Reserve Bank covering the period 1994 to 2014 was used. The initial plot of the series revealed that household debt is explained by an irregular and non-seasonal component. Owing to the non stationarity of the series, first differencing was applied to induce stationarity. The ACFs and PACFs identified six models. Of the six identified models,ð´ð‘…ð¼ð‘€ð´ 3, 1, 0 was selected according to the standard error estimates and the information criteria. The proposed model passed all the diagnostic tests and was further used for producing ten period forecasts of household debt. The forecasted household debt rates obtained were above 75% and within confidence bounds of 95%. Insample and out-of-sampling forecasts moved together confirming the reliability of the model in forecasting household debt and vigour in predictive ability. The proposed model exhibited the best performance in terms of Max APE and Max AE and ascertained the robustness and accuracy of the BoxJenkins ARIMA in forecasting. Both a trend of the data captured and non-seasonal peaks were predicted by the model. These forecasts were proven to be realistic and a true reflection of economic reality in the country. The paper recommended a non-seasonalð´ð‘…ð¼ð‘€ð´ 3, 1, 0 be used by researchers, policy makers and decision makers of different countries to make forecasts of household debt. The South African authorities were also encouraged to use this model to produce further forecasts of the series when making long term planning

Household Debts-and Macroeconomic factors Nexus in the United States: A Cointegration and Vector Error Correction Approach

This study applies cointegration and error correction approaches to determine the effect of macroeconomic determinants on household debt in the United States of America. Cointegration analysis provides an effective framework used for estimating and modelling relationships from time series data. Short-run and long-run cointegration models explaining the relationships between the US household debt and related macroeconomic factors are estimated. The data used covers a period of 1990 Q1 to 2013 Q1 and is sourced from the electronic data delivery system of the OECD, USA Federal Housing Finance Agency and the USA Department of the Treasury among others. SAS 9.3 version was used to obtain the results. The sample and variables were meritorious according to KMO and Cronbach’s alpha. Unit root test results provided enough evidence to conclude that the series were stationary after first differencing. Further data analysis was carried out with the first lag chosen by the AIC and SBC. Three cointegrating vectors were identified and were later standardised to correctly provide parameter estimates of the vector error correction model of household debts. The model revealed some short and long-run relationships. Revealed by the model is that 1.5 % of long-run equilibrium was corrected per quarter. The results of the current study are crucial to households and policy makers. Researchers may also refer to these results

The Performance of Maximum Likelihood Factor Analysis on South African Stock Price Performance

Abstract: The purpose of this paper is to explore the effectiveness and applicability of Maximum Likelihood Factor Analysis (MLFA) method on stock price performance. This method identifies the variables according to their co-movement and variability and builds a model that can be useful for prediction and ranking or classification. The results of factor analysis in this study provide a guide as far as investment decision is concerned. Stock price performance of the seven well-known and biggest companies listed in the Johannesburg stock exchange (JSE) was used as an experimental unit. Monthly data was available for the period 2010 to 2014.Details of a trivariate factor model is: Factor 1 comprises of Absa and Standard Bank (Financial sectors), Factor 2 has Shoprite and Pick ‘n Pay (Retail sectors) while Factor 3 collected Vodacom MTN and Sasol (Industrial sectors). The companies contribute 46.9%, 12.7% and 10.8% respectively to the three sectors and these findings are confirmed by a Chi-square and the Akaike information criterion to be valid. The three factors are also diverse and reliable according to Tucker and Lewis and Cronbach’s coefficients. The findings of this study give economic significance and the study is relevant as it gives investors and portfolio manager’s sensible investment reference.Keywords: Maximum Likelihood Factor Analysis, stock price

Structural Equation Modelling applied to proposed Statistics Attitudes-Outcomes Model: A case of a University in South Africa

The purpose of the study is to investigate the structural relationships among constructs of the statistics attitudes-outcomes model (SA-OM) using exploratory structural equation modelling (ESEM) methodology. The sample consists of 583 first-year undergraduate students enrolled for statistics courses at the university in South Africa. ESEM reveal that all but two of the nine constructs have well to excellent reliability. To enhance the model, we deleted the eight variables. All other indicators have a significant loading into a construct. Congruency of the SA-OM and expectancy value model (EVM) is noted. The SRMR for all modified models are less than 0.10 suggesting that all these models have acceptable fit. Moreover, all the modified models have RMSE values within the ranges of adequate fit. On the contrary, all the models have unacceptable fit according to PCF, CFI, AGFI and PGFI statistics, i.e. according to all parsimony fit indices except the RMSE. The results also reveal that all incremental fit indices but the BBNFI approve the modified models as acceptable since most of these indices are almost equal to a cut-off point of 0.9. However, BBNNI disapprove the ML3 and ML5 models as being acceptable. A host of inconsistencies in fit indices are noted

The Effect of Sample Size on the Efficiency of Count Data Models: Application to Marriage Data

Abstract: Sample size requirements are common in many multivariate analysis techniques as one of the measures taken to ensure the robustness of such techniques, such requirements have not been of interest in the area of count data models. As such, this study investigated the effect of sample size on the efficiency of six commonly used count data models namely: Poisson regression model (PRM), Negative binomial regression model (NBRM), Zero-inflated Poisson (ZIP), Zero-inflated negative binomial (ZINB), Poisson Hurdle model (PHM) and Negative binomial hurdle model (NBHM). The data used in this study were sourced from Data First and were collected by Statistics South Africa through the Marriage and Divorce database. PRM, NBRM, ZIP, ZINB, PHM and NBHM were applied to ten randomly selected samples ranging from 4392 to 43916 and differing by 10% in size. The six models were compared using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Vuong’s test for over-dispersion, McFadden RSQ, Mean Square Error (MSE) and Mean Absolute Deviation (MAD).The results revealed that generally, the Negative Binomial-based models outperformed Poisson-based models. However, the results did not reveal the effect of sample size variations on the efficiency of the models since there was no consistency in the change in AIC, BIC, Vuong’s test for over-dispersion, McFadden RSQ, MSE and MAD as the sample size increased

Modeling Stock Market Returns of BRICS with a Markov-Switching Dynamic Regression Model

This article adopted a Markov-switching dynamic regression (MS-DR) model to estimate appropriate models for BRICS countries. The preliminary analysis was done using data from 01/1997 to 01/2017 and to study the movement of 5 stock market returns series. The study further determined if stock market returns exhibit nonlinear relationship or not. The purpose of the study is to measure the switch in returns between two regimes for the five stock market returns, and, secondly, to measure the duration of each regime for all the stock market returns under examination. The results proved the MS-DR model to be useful, with the best fit, to evaluate the characteristics of BRICS countries

Clusters of Leading Death Causes in South Africa: Application of Hierarchical Agglomerative Clustering Technique

This paper presents an exploratory method for investigating the structure underlying the data. The methods used are reported effective for finding similarity between groups of cases or variables. Furthermore, these methods (hierarchical agglomerative clustering algorithm) are useful when a priori groups are unknown. The results from these methods are in a form of clusters presented in a hierarchy-like structure. Data consisting of 537 out of 1079 variables collected from January to December in 2009 by the Department of Home Affairs, disseminated by Statistics South Africa head office was analysed using SPSS 22. A dendogram of a single linkage method from the hierarchical agglomerative algorithm revealed the five clusters formed from the 537 leading death causes. These causes were collected in clusters according to their hazards with respiratory tuberculosis and pneumonia as main leading causes of death followed by diarrhea, stroke and heart failure. The clusters formed were validated using discriminant analysis which reported about 0.4% of classification error rate. Wilk’s Lambda proved that all the clusters were significant accordingly. While long term plans can be secured for death causes in the fifth cluster, it is important to pay special attention to death causes in clusters 1 to 4 urgently, more specifically those in the first cluster. This may reduce death rates in the country and life spans of residents may also be prolonged. Further analysis may be done where these clusters will be used as variables. A confirmatory factor analysis may be used to further confirm these clusters. DOI: 10.5901/mjss.2014.v5n20p84

Modelling the BRICS Exchange Rates Using the Vector Autoregressive (VAR) Model

The paper modelled the BRICS exchange rates using the Vector Autoregressive (VAR) model. Monthly time series data ranging from January 2008 to January 2018 was used. All the analysis was computed using the R programming software. The study aimed to determine a suitable VAR model in modelling the BRICS exchange rates and determine the linear dependency between the financial markets (in particular BRICS exchange rates). Optimal lag length of one (1) was selected using the SIC. The VAR model with lag length one was fitted and the parameters were estimated. The results revealed that there is a unidirectional relationship amongst the BRICS exchange rates. The VAR (1) model did not satisfy all the diagnostic tests, therefore forecasting future values of the BRICS exchange rates could not be computed. Recommendations for different approaches were formulated