Ezekiel Nortey
University of Ghana, Statistics, Faculty Member
- Education, Marketing, Development Economics, Statistics, Econometrics, Probability Theory, and 21 moreTime Series Analysis and Forecasting, Bayesian statistics & modelling, Applied Econometrics, Multivariate Statistics, Network, Multivariate Analysis, ARIMA, Higher Education, Statistical Methods, Markov-chain model, Inference In Stochastic Processes, Expenditure, Regression Analysis, Nonparametric Statistics, Hypothesis testing, Economics, Management, Social Psychology, Market Research, Survey Research, and Stataedit
Modelling of extreme events has always been of interest in fields such as hydrology and meteorology. However, after the recent global financial crises, appropriate models for modelling of such rare events leading to these crises have... more
Modelling of extreme events has always been of interest in fields such as hydrology and meteorology. However, after the recent global financial crises, appropriate models for modelling of such rare events leading to these crises have become quite essential in the finance and risk management fields. This paper models the extreme values of the Ghana stock exchange all-shares index (2000–2010) by applying the extreme value theory (EVT) to fit a model to the tails of the daily stock returns data. A conditional approach of the EVT was preferred and hence an ARMA-GARCH model was fitted to the data to correct for the effects of autocorrelation and conditional heteroscedastic
terms present in the returns series, before the EVT method was applied. The Peak Over Threshold approach of the EVT, which fits a Generalized Pareto Distribution (GPD) model to excesses above a certain selected threshold, was employed. Maximum likelihood estimates of the model parameters were obtained and the model’s goodness of fit was assessed graphically using Q–Q, P–P and density plots. The findings indicate that the GPD provides an adequate fit to the data of excesses. The size of the extreme daily Ghanaian stock market movements were then computed using the value at risk and expected shortfall risk measures at some high quantiles, based on the fitted GPD
model.
terms present in the returns series, before the EVT method was applied. The Peak Over Threshold approach of the EVT, which fits a Generalized Pareto Distribution (GPD) model to excesses above a certain selected threshold, was employed. Maximum likelihood estimates of the model parameters were obtained and the model’s goodness of fit was assessed graphically using Q–Q, P–P and density plots. The findings indicate that the GPD provides an adequate fit to the data of excesses. The size of the extreme daily Ghanaian stock market movements were then computed using the value at risk and expected shortfall risk measures at some high quantiles, based on the fitted GPD
model.
Research Interests:
Although, there exists numerous literature on the procedure for forecasting or predicting election results, in Ghana only opinion poll strategies have been used. To fill this gap, the paper develops Markov chain models for forecasting the... more
Although, there exists numerous literature on the procedure for forecasting or predicting election results, in Ghana only opinion poll strategies have been used. To fill this gap, the paper develops Markov chain models for forecasting the 2016 presidential election results at the Regional, Zonal (i.e. Savannah, Coastal and Forest) and the National levels using past presidential election results of Ghana. The methodology
develops a model for prediction of the 2016 presidential election results in Ghana using the Markov chains Monte Carlo (MCMC) methodology with bootstrap estimates. The results were that the ruling NDC may marginally win the 2016 Presidential Elections but would not obtain the more than 50 % votes to be declared an outright winner. This means that there is going to be a run-off election between the two giant political parties: the ruling NDC and the major opposition party, NPP. The prediction for the 2016 Presidential run-off election between the NDC and the NPP was rather in favour of the major opposition party, the NPP with a little over the 50 % votes obtained.
Keywords: Markov chains, Stochastic matrix, Elections, Forecasting, NDC, NPP, Ghana
develops a model for prediction of the 2016 presidential election results in Ghana using the Markov chains Monte Carlo (MCMC) methodology with bootstrap estimates. The results were that the ruling NDC may marginally win the 2016 Presidential Elections but would not obtain the more than 50 % votes to be declared an outright winner. This means that there is going to be a run-off election between the two giant political parties: the ruling NDC and the major opposition party, NPP. The prediction for the 2016 Presidential run-off election between the NDC and the NPP was rather in favour of the major opposition party, the NPP with a little over the 50 % votes obtained.
Keywords: Markov chains, Stochastic matrix, Elections, Forecasting, NDC, NPP, Ghana
Research Interests:
This is a report on the national human development indices which tends to analyze indicators of pertaining to social exclusion issues.
Research Interests:
This report analyzes analytically the demographic profile of the 2010 population and Housing Census data for the Northern region of Ghana.
Research Interests:
This is an analytical report for the 2010 Population and Housing Census for Ghana
Research Interests:
The purpose of this study was to identify a metric for measuring students’ performance in the Department of Mathematics and Statistics of a public university in Ghana. Some of the students of the department are of the view that the... more
The purpose of this study was to identify a metric for measuring students’ performance in the Department of Mathematics and Statistics of a public university in Ghana. Some of the students of the department are of the view that the current grading system used by the Department does not do a good job of distinguishing between the performances of students, as at times students of different academic performance could end up with the same Grade Point Average (GPA), a performance measure. Data for the research which covers the 2012/2013 third year students of the Department were obtained from the university’s student records unit. Principal Component Analysis (PCA) was used to analyze the data. Three principal components were retained as rules or indices for the classification of students’ performance. A derivative of the first principal component, RSI, could serve as a new performance measure for the Department as it takes into consideration differences in the raw scores of the students.
Keywords: Academic performance, principal component analysis, relative score index (RSI).
Keywords: Academic performance, principal component analysis, relative score index (RSI).
Research Interests:
This paper was aimed at investigating the volatility and conditional relationship among inflation rates, exchange rates and interest rates as well as to construct a model using multivariate GARCH DCC and BEKK models using Ghana data from... more
This paper was aimed at investigating the volatility and conditional relationship among inflation rates, exchange rates and interest rates as well as to construct a model using multivariate GARCH DCC and BEKK models using Ghana data from January 1990 to December 2013. The study revealed that the cumulative depreciation of the cedi to the US dollar from 1990 to 2013 is 7,010.2% and the yearly weighted depreciation of the cedi to the US dollar for the period is 20.4%. There was evidence that, the fact that inflation rate was stable, does not mean that exchange rates and interest rates are expected to be stable. Rather, when the cedi performs well on the forex, inflation rates and interest rates react positively and become stable in the long run. The BEKK model is robust to modelling and forecasting volatility of inflation rates, exchange rates and interest rates. The DCC model is robust to model the conditional and unconditional correlation among inflation rates, exchange rates and interest rates. The BEKK model, which forecasted high exchange rate volatility for the year 2014, is very robust for modelling the exchange rates in Ghana. The mean equation of the DCC model is also robust to forecast inflation rates in Ghana.
Keywords: DCC, BEKK, GARCH, Ghana, volatility, inflation, exchange, interest rates
Keywords: DCC, BEKK, GARCH, Ghana, volatility, inflation, exchange, interest rates
Research Interests:
We consider preferential attachment random graphs which may be obtained as follows: It starts with a single node. If a new node appears, it is linked by an edge to one or more existing node(s) with a probability proportional to function... more
We consider preferential attachment random graphs which may be obtained as follows: It starts with a single node. If a new node appears, it is linked by an edge to one or more
existing node(s) with a probability proportional to function of their degree. For a class of linear preferential attachment random graphs we find a large deviation principle (LDP) for
the empirical degree measure. In the course of the prove this LDP we establish an LDP for the empirical degree and pair distribution see Theorem 2.3, of the fitness preferential
attachment model of random graphs.
existing node(s) with a probability proportional to function of their degree. For a class of linear preferential attachment random graphs we find a large deviation principle (LDP) for
the empirical degree measure. In the course of the prove this LDP we establish an LDP for the empirical degree and pair distribution see Theorem 2.3, of the fitness preferential
attachment model of random graphs.
Research Interests:
This study sought to model rates of inflation in Ghana using the Autoregressive Conditional Heteroscedastic models. In particular, the ARCH, GARCH and EGARCH models were compared. Monthly rates of inflation from January 2000 to December... more
This study sought to model rates of inflation in Ghana using the Autoregressive Conditional Heteroscedastic models. In particular, the ARCH, GARCH and EGARCH models were compared. Monthly rates of inflation from January 2000 to December 2013 were used in the study with the rates from January 2000 to December 2012 serving as the training set and January 2013-December 2013 serving as the validation set. The result revealed that the EGARCH (1, 2) model with a mean equation of ARIMA (3, 1, 2) × (0, 0, 0)12 was appropriate for modelling Ghana’s monthly rates of inflation. A one year out-of-sample forecast for the year 2014 shows that Ghana
would experience double digit inflation with an end of year inflation rate of 15.0% and a margin of error of 0.9%.
This study would inform and guide policy-makers as well as investors and businessmen on management of expected future rates of inflation.
would experience double digit inflation with an end of year inflation rate of 15.0% and a margin of error of 0.9%.
This study would inform and guide policy-makers as well as investors and businessmen on management of expected future rates of inflation.
Research Interests:
Most central banks’ policy initiatives throughout the world have been aimed at achieving and maintaining price stability and in Ghana; the Bank of Ghana is no exception. The exchange rate of the GH cedi to the U.S. Dollar, Japanese Yen,... more
Most central banks’ policy initiatives throughout the world have been aimed at achieving and maintaining price stability and in Ghana; the Bank of Ghana is no exception. The exchange rate of the GH cedi to the U.S. Dollar, Japanese
Yen, C.F.A., Pound Sterling and the Euro (major trading currencies) are not normalized (i.e. it fluctuates with upward
tendencies) in the country. In recent years, a number of related formal models for time-varying methodologies have been developed. The study uses Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) approach to mathematically fit models that describe the monthly trading currencies between the Ghana Cedi against the major trading
currencies. We then forecast one year ahead and compare the predictive powers of the models. This study attempts to
outline the practical steps which need to be undertaken, in order to use the ARIMA model for forecasting changes/variabilities in the major trading currencies in Ghana which then helps in predicting inflation to near perfection. All the five major trading currencies used were ARIMA (1, 1, 0). They all fitted well with the exception of the C.F.A. This may be attributable to the re-denomination of the Cedi in July, 2007. Also none of the models were seasonal and the predominant components were trend and random variation.
Yen, C.F.A., Pound Sterling and the Euro (major trading currencies) are not normalized (i.e. it fluctuates with upward
tendencies) in the country. In recent years, a number of related formal models for time-varying methodologies have been developed. The study uses Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) approach to mathematically fit models that describe the monthly trading currencies between the Ghana Cedi against the major trading
currencies. We then forecast one year ahead and compare the predictive powers of the models. This study attempts to
outline the practical steps which need to be undertaken, in order to use the ARIMA model for forecasting changes/variabilities in the major trading currencies in Ghana which then helps in predicting inflation to near perfection. All the five major trading currencies used were ARIMA (1, 1, 0). They all fitted well with the exception of the C.F.A. This may be attributable to the re-denomination of the Cedi in July, 2007. Also none of the models were seasonal and the predominant components were trend and random variation.
Research Interests:
This study expands on previous studies by examining the purchasers’ criteria choice of automobile in the Ghanaian market and establishing the factors that influence the purchase and estimating the choice of brand. The study examined 1,130... more
This study expands on previous studies by examining the
purchasers’ criteria choice of automobile in the Ghanaian market and establishing the factors that influence the purchase and estimating the choice of brand. The study examined 1,130 automobile owners. Data was collected on 20 automobile attributes considered important when purchasing an automobile. The preliminary result of the research show that five major factors—interior, safety, value for money, modernity, and economy—influence consumers to purchase a particular automobile. Conclusion can be drawn that a relationship exists between the influencing factors and the brands of automobiles purchased by respondents
purchasers’ criteria choice of automobile in the Ghanaian market and establishing the factors that influence the purchase and estimating the choice of brand. The study examined 1,130 automobile owners. Data was collected on 20 automobile attributes considered important when purchasing an automobile. The preliminary result of the research show that five major factors—interior, safety, value for money, modernity, and economy—influence consumers to purchase a particular automobile. Conclusion can be drawn that a relationship exists between the influencing factors and the brands of automobiles purchased by respondents
Research Interests:
Abstract: This study proposes a methodology for analysis of catastrophic spending on health in statistically underdeveloped countries. A binary logistic regression model, based on data from households with reported non-zero expenditure on... more
Abstract: This study proposes a methodology for analysis of catastrophic spending on health in statistically underdeveloped
countries. A binary logistic regression model, based on data from households with reported non-zero expenditure on health, is proposed for the estimation of the likelihood of spending on health for all households irrespective of whether they spent on health or not within the reference period for the survey. “Univariate” discriminant functions, also based on data from households who spent on health within the reference period of the survey, were proposed for discriminating households that made catastrophic expenditure on health from those who did not. An application of this methodology to the data from the Ghana living Standards survey (round V) indicates
that the binary logistic regression model estimates correctly at least 78% of household’s likelihood of spending on health while correctly discriminating the households as having a catastrophic expenditure.
countries. A binary logistic regression model, based on data from households with reported non-zero expenditure on health, is proposed for the estimation of the likelihood of spending on health for all households irrespective of whether they spent on health or not within the reference period for the survey. “Univariate” discriminant functions, also based on data from households who spent on health within the reference period of the survey, were proposed for discriminating households that made catastrophic expenditure on health from those who did not. An application of this methodology to the data from the Ghana living Standards survey (round V) indicates
that the binary logistic regression model estimates correctly at least 78% of household’s likelihood of spending on health while correctly discriminating the households as having a catastrophic expenditure.
Research Interests:
Abstract: In this study a binomial logistic model is used to determine the factors which influence households’ poverty status using data from three rounds of the Ghana Living Standards Survey (GLSS3, 1991/92; GLSS4, 1998/99 and GLSS5,... more
Abstract: In this study a binomial logistic model is used to determine the factors which influence households’ poverty status using data from three rounds of the Ghana Living Standards Survey (GLSS3, 1991/92; GLSS4, 1998/99 and GLSS5, 2005/06). The results obtained from the analysis indicate that households with large sizes, illiterate heads, and those with heads that have agriculture as their primary occupation are poorer. Also households in rural localities and the savanna zone are poorer. It was also evident that while the living standards of households with large sizes and those with agriculture as primary occupation were improving over the years, the households with illiterate heads and those who live in the savanna zone were becoming worse off.
Research Interests:
The study aims at recognizing images under variable facial expression using principal component analysis and singular value decomposition and to determine the recognition rate. Face recognition is an easy task for humans. One of the main... more
The study aims at recognizing images under variable facial expression using principal component analysis and singular value decomposition and to determine the recognition rate. Face recognition is an easy task for humans. One of the main driving factors for face recognition is the ever growing number of applications that an efficient and resilient recognition technique addresses. Earlier facial recognition processes are faced with the challenge of recognizing images under non-uniform illumination effects and variable facial expressions (principal emotions). This work focuses on solving these already existing recognition problems by recognizing the facial expression using the principal component analysis and singular value decomposition. The research approaches facial recognition from face extraction, categorization and identification. The entire training and recognition processes were modeled using GNU Octave 3.24. After experimental runs, recognition results showed that the above dimensionality reduction algorithms have an encouraging recognition performance of recognizing images under various principal expressions and economically reducing data dimension thereby solving the problem of large database. Key words: Principal Component Analysis, Singular value decomposition, Eigenfaces, Recognition rate, Face training.