Open Access Case Study

An Analysis of the Predictors of Financial Distress for Zimbabwe Listed Corporates

Louisa Muparuri, Victor Gumbo

Asian Journal of Probability and Statistics, Page 1-14
DOI: 10.9734/ajpas/2021/v12i430291

This study brings novelty to the area of corporate distress modelling in Zimbabwe by exploring company-specific indicators of corporate distress, unlike most of the previous studies, which used financial performance indicators. Using a binary logistic regression on a time series dataset collated between 2010 and 2017, this study establishes book value, book value per share, average debt to equity and equity per share as very significant determinants of corporate distress on the Zimbabwe Stock Exchange (ZSE). Future studies incorporating artificial intelligence and a combination of both the traditional financial ratios and market-based indicators is recommended to expand the scope of the study.

Open Access Original Research Article

Exploration of D-, A-, I- and G- Optimality Criteria in Mixture Modeling

Samson W. Wanyonyi, Ayubu A. Okango, Julius K. Koech

Asian Journal of Probability and Statistics, Page 15-28
DOI: 10.9734/ajpas/2021/v12i430292

A design optimality criterion, such as D-, A-, I-, and G- optimality criteria, is often used to analyze, evaluate and compare different designs options in mixture modeling test. A mixture test is an experiment where the descriptive variable and response rely only on the mixture's relative ratio in the mix but not its composition. The study geared toward exploring D-, A-, I-, and G- optimality criteria and their efficiency in determining an optimal split-plot design in mixture modeling within the presences of process variables. We evaluated and discussed in detail D-, A-, I-, and G- optimality criteria based on literature review. We also explored and examine why I- and D-optimal criteria are often involved within the formulation of an optimal design in the context of mixture process variable settings. We recommend that optimality criterion must always be used when assessing the various styles of designs so as to search out a desirable design that matches a combination model.

Open Access Original Research Article

Statistical Analysis of Nigeria’s Price Sector: An Econometric Approach

D. M. O. Omebo, T. D. Ailobhio, G. I. Fanen

Asian Journal of Probability and Statistics, Page 29-40
DOI: 10.9734/ajpas/2021/v12i430293

This study analyzed Nigeria’s price sector using a formulated model for the price sector of the Nigeria economy. A set of simultaneous equations were used to reflect the implicit gross domestic product deflators for each of the sectors of the Nigeria economy and was found to be over identified under the order condition for identification. The model was estimated by ordinary least square method and two stage least square methods. All the variables have expected signs and as indicated by the F –statistic, the overall performance of the entire regression is significant.  The high measure of R2 and Ṝ2, in each case indicates that the explanatory variables included in the equation jointly account for the entire variation. The small RMSE also indicates that the equations have good fit. Durbin –Watson statistics shows that there is no positive first order autocorrelation. The small value of the Theil’s inequality indicates that the equation has good predictive performance. The researcher therefore recommends that government should employ the model so as to be able to monitor price of each of the sectors of the economy and put proper mechanism in place to control those sectors that affect the overall price sector of the economy.

Open Access Original Research Article

Markov-Switching Vector Autoregressive Modelling (Intercept Adjusted); Application to International Trade and Macroeconomic Stability in Nigeria (2000M1–2019M6)

Tuaneh, Godwin Lebari, Essi, Isaac Didi

Asian Journal of Probability and Statistics, Page 41-57
DOI: 10.9734/ajpas/2021/v12i430294

Economic relationships are often modelled without consideration of a possible regime switch, the transmission from one regime to another and the duration of stay in a particular regime which are not captured by linear models. This study aimed to model and estimate the interdependence existing among Nigeria’s International Trade and Macroeconomic Stability. Specifically, this study sought to estimate and compare the estimated Models, select the best Model and determine the probabilities of stay, the expected duration of stay in a particular regime. The study adopted a quasi-experimental design. Time series data on the study variables from January 2000 to June 2019 were obtained from the Statistical Bulletin of the Central Bank of Nigeria. Models were specified accordingly, the statistical analyses were carried out using the Markov Switching Intercept Vector Autoregressive Models, the pre and post-diagnostic tests were also conducted. The unit root test results showed I (1). VAR lag length selection criteria choose lag 2. The MS-VAR analysis identified two regimes (expansion and contraction), the information criteria selected the Markov-Switching Intercept Autoregressive Heteroschedastic 2 Variance Auto-regression 2 [MSIARH (2) - VAR (2)]. The MS-VAR results in regime 1 showed that lags 1 and 2 of total export significantly affected total export and total import, Lags 1 and 2 of total import had significant effects on exchange rate while lags 1 of exchange rate and lags 1 and 2 of exchange rate had significant effects on inflation rate. In Regime 2, lag 1 of total export and lag 2 of exchange rate had significant effects on total export. Only lag 2 of inflation rate had significant effects on exchange rate while lag 2 of total export and lags 1 and 2 of exchange rate had significant effects on the inflation rate. The results also showed an 89% probability of staying in regime 1 for a duration of 8 months 8 days and 57% probability of staying in regime 2 for 2 months 10 days. It was concluded that the MSIARH (2) - VAR (2). It was recommended that the right-hand side variables should be tested for endogeneity before concluding on single or system equation. It was also recommended that the possibility of regimes should be verified before concluding on linear or nonlinear models.

Open Access Original Research Article

Modelling Dynamic Micro and Macro Panel Data with Autocorrelated Error Terms

Kafayat T. Uthman, Iyabode F. Oyenuga, Taiwo M. Adegoke, Adewale P. Onatunji, Olanrewaju V. Oni

Asian Journal of Probability and Statistics, Page 58-70
DOI: 10.9734/ajpas/2021/v12i430295

Aims: The aim of this study is to determine the best estimator for estimating dynamic panel data model with serially uncorrelated disturbances and exogenous regressors.

Methodology: In this study, properties of some Dynamic Panel Data estimators are investigated. These are Ordinary Least Squares (OLS), the Anderson-Hsiao(AH(d), Arellano-Bond Generalized Method of Moment (ABGMM) one-step, Blundell- Bond System (BBS) one-step, M- estimator,  MM estimators and proposed estimator, Modified Anderson-Hsiao with Arellano-Bond(MAHAB) estimator in the presence of autocorrelation. Also, this new estimator was proposed by modifying the existing estimators.

Results: Monte-Carlo simulations were carried out at varying sample size (n) ranges from 10-200 and time period (T) ranges from 5-20 when autocorrelation ( ) is fixed at 0.3, 0.5 and 0.7. The estimators considered performed well except OLS and BBS for all time periods.

Conclusion: AH estimator performed relatively well when the time period is small while ABGMM estimator outperformed all other estimators when sample size (n) is large for all the time periods considered. ABGMM shows the largest improvement as sample size (n) and time periods (T) increase. The MAHAB estimator outperformed all other estimators in small and large sample size irrespective of time period in the presence of autocorrelation.