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A Markov Regime Switching Approach of Estimating Volatility Using Nigerian Stock Market
Yahaya Haruna Umar,
Matthew Adeoye
Issue:
Volume 9, Issue 4, July 2020
Pages:
80-89
Received:
26 January 2020
Accepted:
7 April 2020
Published:
28 May 2020
Abstract: Understanding and forecasting the behavior of volatility in stock market has received significant attention among researchers and analysts in the last few decades due to its crucial roles in financial markets. Portfolios managers, option traders, and market makers are all interested in the possibility of forecasting, with a reasonable level of accuracy. This study examined the volatility on the Nigeria stock market by comparing two Markov regime switching Autoregressive (MS-AR) Models estimated at different lagged values using the Nigeria stock exchange monthly All Share Index data from 1988 to 2018 in the Central Bank of Nigeria (CBN) Statistical Bulletin. It was found that factors like financial crisis, information flow, trading volume, economical aspects and investor’s behavior are the causes of volatility in the stock market. The results and forecasts obtained from the statistical analysis in this research showed that the stock market will experience a steady growth in 2020 and beyond. Also, the stock market is experiencing fluctuations in the price indices which show that over the years, investors have been exposed to some certain risks in the time past. We therefore recommended that researchers should focus more attention in developing robust statistical model that will reflect and continue to monitor future trends and realities.
Abstract: Understanding and forecasting the behavior of volatility in stock market has received significant attention among researchers and analysts in the last few decades due to its crucial roles in financial markets. Portfolios managers, option traders, and market makers are all interested in the possibility of forecasting, with a reasonable level of accu...
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Generalized Regression Control Chart for Monitoring Crop Production
Olatunji Taofik Arowolo,
Matthew Iwada Ekum
Issue:
Volume 9, Issue 4, July 2020
Pages:
90-100
Received:
31 January 2020
Accepted:
7 April 2020
Published:
28 May 2020
Abstract: Recently, Nigeria focused on Agriculture as a way to diversify her economy. Crop production, which is a proxy to measure agricultural output is considered very important. So, controlling crop production (output) among states in Nigeria is very key. In this study, the generalized regression control chart was used rather than the conventional control chart. The conventional control chart does not put into consideration factor(s) that affect crop production. The generalized regression control chart considers the factor (independent variable) that affect crop production (dependent variable). The normal distribution is a special case of the generalized regression control chart. The possibility of using Weibull regression and other non-normal models were considered. In this research, Gaussian distribution was used as the underlying distribution because it fitted the crop production data. The cost of seed/seedling was selected from a set of independent variables, because it is most significant among other independent variables. The data were collected from secondary sources, precisely National Bureau of Statistics (NBS). All the 36 states in Nigeria, including the Federal Capital Territory (FCT) were involved in the study. The result of the generalized regression control chart showed that crop production is not in control in Nigeria, which was traced to assignable cause of variation in FCT, Abuja. This implied that FCT, Abuja produced below the lower control limit of crop production, despite the relative cost of seed/seedlings.
Abstract: Recently, Nigeria focused on Agriculture as a way to diversify her economy. Crop production, which is a proxy to measure agricultural output is considered very important. So, controlling crop production (output) among states in Nigeria is very key. In this study, the generalized regression control chart was used rather than the conventional control...
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Exploring Data-Reflection Technique in Nonparametric Regression Estimation of Finite Population Total: An Empirical Study
Issue:
Volume 9, Issue 4, July 2020
Pages:
101-105
Received:
6 May 2020
Accepted:
25 May 2020
Published:
3 June 2020
Abstract: In survey sampling statisticians often make estimation of population parameters. This can be done using a number of the available approaches which include design-based, model-based, model-assisted or randomization-assisted model based approach. In this paper regression estimation under model based approach has been studied. In regression estimation, researchers can opt to use parametric or nonparametric estimation technique. Because of the challenges that one can encounter as a result of model misspecification in the parametric type of regression, the nonparametric regression has become popular especially in the recent past. This paper explores this type of regression estimation. Kernel estimation usually forms an integral part in this type of regression. There are a number of functions available for such a use. The goal of this study is to compare the performance of the different nonparametric regression estimators (the finite population total estimator due Dorfman (1992), the proposed finite population total estimator that incorporates reflection technique in modifying the kernel smoother), the ratio estimator and the design-based Horvitz-Thompson estimator. To achieve this, data was simulated using a number of commonly used models. From this data the assessment of the estimators mentioned above has been done using the conditional biases. Confidence intervals have also been constructed with a view to determining the better estimator of those studied. The findings indicate that proposed estimator of finite population total that is nonparametric and uses data reflection technique is better in the context of the analysis done.
Abstract: In survey sampling statisticians often make estimation of population parameters. This can be done using a number of the available approaches which include design-based, model-based, model-assisted or randomization-assisted model based approach. In this paper regression estimation under model based approach has been studied. In regression estimation...
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Panel Data Analysis of International Trade in West African Sub Region
Yahaya Haruna Umar,
Bamanga Muhammad,
Udeme Omoren
Issue:
Volume 9, Issue 4, July 2020
Pages:
106-120
Received:
2 February 2020
Accepted:
2 March 2020
Published:
4 June 2020
Abstract: West African countries have suffered so much under poor economic growth rate; this issue is largely caused as a result of the poor performance in international trade, which is one of the most essential tools for economic growth. The study investigated the factors influencing international trade in West African Sub region. A Pooled OLS, Fixed Effect Model, and Random Effect Model were adopted to fit the panel regression model for the panel data sets. The study divided the models into three in other to have proper view of factors influencing international trade across West African Sub-region, each model contain the same independent variables and different dependent variables. The result shows that fixed effect model was accurate for the study. Also from the study it was observed that for the first model which use import as dependent variable, gross domestic production, foreign direct investment, and exchange rate are positively significant to import which implies that all the regressor variable influence import across west African sub region positively, while only GDP and FDI are positively significant to export and only FDI is positively significant to trade balance (TB). We therefore conclude that foreign direct investment is the key macro-economic variable that positively influences the policy of international trade across West African over the period of consideration.
Abstract: West African countries have suffered so much under poor economic growth rate; this issue is largely caused as a result of the poor performance in international trade, which is one of the most essential tools for economic growth. The study investigated the factors influencing international trade in West African Sub region. A Pooled OLS, Fixed Effect...
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The Uniform Variants of the Glivenko-Cantelli and Donsker Type Theorems for a Sequential Integral Process of Independence
Abdushukurov Abdurahim Ahmedovich,
Kakadjanova Leyla Reshitovna
Issue:
Volume 9, Issue 4, July 2020
Pages:
121-126
Received:
15 February 2020
Accepted:
22 May 2020
Published:
4 June 2020
Abstract: In the analysis of statistical data in biomedical treatments, engineering, insurance, demography, and also in other areas of practical researches, the random variables of interest take their possible values depending on the implementation of certain events. So in tests of physical systems (or individuals) on duration of uptime values of operating systems depend on subsystems failures, in insurance business insurance company payments to its customers depend on insurance claims. In such experimental situations, naturally become problems of studying the dependence of random variables on the corresponding events. The main task of statistics of such incomplete observations is estimating the distribution function or what is the same, the survival function of the tested objects. To date, there are numerous estimates of these characteristics or their functionals in various models of incomplete observations. In this paper investigated the asymptotic properties of sequential processes of independence of the integral structure and uniform versions of the strong law of large numbers and the central limit theorem for integral processes of independence by indexed classes are established. The obtained results can be used to construct statistics of criteria for testing a hypothesis of independence of random variables on the corresponding events.
Abstract: In the analysis of statistical data in biomedical treatments, engineering, insurance, demography, and also in other areas of practical researches, the random variables of interest take their possible values depending on the implementation of certain events. So in tests of physical systems (or individuals) on duration of uptime values of operating s...
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Quality Characterisation and Capability Assessment of a Tobacco Company
Adepeju Opaleye,
Oladunni Okunade,
Taiwo Adedeji,
Victor Oladokun
Issue:
Volume 9, Issue 4, July 2020
Pages:
127-135
Received:
11 May 2020
Accepted:
28 May 2020
Published:
17 June 2020
Abstract: This is an empirical study on the application of SPC techniques for monitoring and detecting variation in the quality of locally produced tobacco in Nigeria. The result provides base evidence for intervention in the quality behavior of the heavily automated tobacco production process in which slight undetected deviation can result in significant wastes. An observational study was carried out within the primary manufacturing department of the tobacco company. The study analysis was conducted using descriptive statistics, goodness of fit test and SPC charts.. These charts were constructed and examined for significant variation in expected output quality as well as the capability of the process. The goodness of fit test and SPC identified CTQs that were approximately normally distributed and out of process control across periods of observations. These deviations were not evident with the summary data or its presentation on the histogram. Subsequently, the out of control process charts were transformed to in-control charts by repetitive elimination of out-of-control instances. At this state, it was observed that the process was only capable of meeting specification for the dust level for all capability measures. These results illustrate a proof of SPC for process monitoring and product quality improvement.
Abstract: This is an empirical study on the application of SPC techniques for monitoring and detecting variation in the quality of locally produced tobacco in Nigeria. The result provides base evidence for intervention in the quality behavior of the heavily automated tobacco production process in which slight undetected deviation can result in significant wa...
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An Analysis of Some Physical Features of Babies at Birth (A Case Study of Federal Medical Centre, Umuahia)
Henrietta Ebele Oranye,
Kingsley Chinedu Arum,
Agnes Nneoma Kalu
Issue:
Volume 9, Issue 4, July 2020
Pages:
136-142
Received:
1 July 2019
Accepted:
16 August 2019
Published:
6 July 2020
Abstract: New born baby is a gift from God and what a newborn baby looks like is not a baby model, rather a newborn baby looks varies from baby to baby in terms of weight, height and head circumferences. In this research, a sample of 200 male and female babies was used for the analysis. The aim is to identify if there is a significant difference between the means of the variables considered. In this research, three variables were considered for both male and female babies at birth. The result showed that the mean birth weight is 3.55kg and 3.39kg for male and female babies respectively. The mean height and head circumference of female babies recorded higher than their male counterpart. The Hoteling’s T2-test showed that there is a significant difference between the mean vectors of the variables considered; hence a discriminant analysis was conducted. The discriminant function obtained fairly classifies the group at 42% error rate. From the results gotten, there is significant difference between the height, weight and head circumference of male and female babies and conclude that male babies are heavier in terms of weight while female babies have bigger head circumference than the male babies.
Abstract: New born baby is a gift from God and what a newborn baby looks like is not a baby model, rather a newborn baby looks varies from baby to baby in terms of weight, height and head circumferences. In this research, a sample of 200 male and female babies was used for the analysis. The aim is to identify if there is a significant difference between the ...
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Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors
Issue:
Volume 9, Issue 4, July 2020
Pages:
143-153
Received:
11 June 2020
Accepted:
22 June 2020
Published:
13 July 2020
Abstract: In recent years, the Consumer Price Index (CPI) prediction has attracted the attention of many researchers due to its excellent measurement of macroeconomic performance. It is an important index that is used to measure the rate of inflation or deflation of commodities. In this paper, Autoregressive Integrated Moving Average (ARIMA) and regression with ARIMA errors, where the covariate is the time, were compared to forecast Somaliland Consumer Price Index using monthly time series data from 2013 – 2020. The study used and applied both models to produce the necessary forecasts. Also, Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and other model accuracy measures were used to measure model’s predictive ability. By utilizing these methods, it is obtained that ARIMA (0, 1, 3) is the most suitable model for predicting CPI in Somaliland. Furthermore, the diagnostic tests show that the model presented is reliable and appropriate for forecasting Somaliland CPI data. The study results obviously indicate that CPI in Somaliland is more likely to proceed on an upward trend in the coming year. The study guides policymakers to use strict monetary and fiscal policy measures to address Somaliland’s inflation.
Abstract: In recent years, the Consumer Price Index (CPI) prediction has attracted the attention of many researchers due to its excellent measurement of macroeconomic performance. It is an important index that is used to measure the rate of inflation or deflation of commodities. In this paper, Autoregressive Integrated Moving Average (ARIMA) and regression w...
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On Survival Function Estimation in Dependent Partially Informative Random Censorship
Abdushukurov Abdurahim Ahmedovich,
Bozorov Suxrob Baxodirovich
Issue:
Volume 9, Issue 4, July 2020
Pages:
154-161
Received:
8 July 2020
Accepted:
29 July 2020
Published:
10 August 2020
Abstract: In such areas as bio-medicine, engineering and insurance researchers are interested in positive variables, which are expressed as a time until a certain event. But observed data may be incomplete, because it is censored. Moreover, the random variables of interest (lifetimes) and censoring times can be influenced by other variable, often called prognostic factor or covariate. The basic problem is the estimation of survival function of lifetime. In this article we propose three asymptotical equivalent estimators of survival function in partially informative competing risks model. This paper deals with the estimation of a survival function with random right censoring and dependent censoring mechanism through covariate. We extend exponential – hazard, product - limit and relative - risk power estimators of survival functions in partially informative censoring model in which conditional on a covariate, the survival and censoring times are assumed to be independent. In this model, each observation is the minimum of one lifetime and two censoring times. The survival function of one of these censoring times is a power of the survival function of the lifetime. The distribution of the other censoring time has no relation with the distribution of the lifetime (non-informative censoring). For estimators we show their uniform strong consistency and convergence to same Gaussian process. Comparisons of estimators with the Jensen-Wiedmann’s estimator are included.
Abstract: In such areas as bio-medicine, engineering and insurance researchers are interested in positive variables, which are expressed as a time until a certain event. But observed data may be incomplete, because it is censored. Moreover, the random variables of interest (lifetimes) and censoring times can be influenced by other variable, often called prog...
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Population Total Estimation in a Complex Survey by Nonparametric Model Calibration Using Penalty Function Method with Auxiliary Information Known at Cluster Levels
Janiffer Mwende Nthiwa,
Ali Salim Islam,
Pius Nderitu Kihara
Issue:
Volume 9, Issue 4, July 2020
Pages:
162-172
Received:
16 July 2020
Accepted:
8 August 2020
Published:
19 August 2020
Abstract: Nonparametric methods are rich classes of statistical tools that have gained acceptance in most areas of statistics. They have been used in the past by researchers to fit missing values in the presence of auxiliary variables in a sampling survey. Nonparametric methods have been preferred to parametric methods because they make it possible to analyze data, estimate trends and conduct inference without having to fully specify a parametric model for the data. This study, therefore, presents some new attempts in the complex survey through the nonparametric imputation of missing values by the use of both penalized splines and neural networks. More precisely, the study adopted a neural network and penalized splines to estimate the functional relationship between the survey variable and the auxiliary variables. This complex survey data was sampled through a cluster - strata design where a population is divided into clusters which are in turn subdivided into strata. Once missing values have been imputed, this study performs a model calibration with auxiliary information assumed completely available at the cluster level. The reasoning behind model calibration is that if the calibration constraints are satisfied by the auxiliary variable, then it is expected that the fitted values of the variable of interest should satisfy such constraints too. The population total estimators are derived by treating the calibration problems at cluster level as optimization problems and solving it by the method of penalty functions. A Monte Carlo simulation was run to assess the finite sample performance of the estimators under complex survey data. The efficiency of the estimator’s performance was then checked by MSE criterion. A comparison of the penalized spline model calibration and neural network model calibration estimators was done with Horvitz Thompson estimator. From the results, the two nonparametric estimator’s performances seem closer to that of Horvitz Thompson estimator and are both unbiased and consistent.
Abstract: Nonparametric methods are rich classes of statistical tools that have gained acceptance in most areas of statistics. They have been used in the past by researchers to fit missing values in the presence of auxiliary variables in a sampling survey. Nonparametric methods have been preferred to parametric methods because they make it possible to analyz...
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