Browsing by Author "Muriithi, D. K."

Now showing 1 - 7 of 7

Application of Principal Component Analysis and Hierarchical Regression Model on Kenya Macroeconomic Indicators.
(EJ-MATH, European Journal of Mathematics & Statisctics., 2022) Mbaluka, M. K.; Njoroge, G. G.; Muriithi, D. K.
The aim of this paper was to apply Principal Component Analysis (PCA) and hierarchical regression model on Kenyan Macroeconomic variables. The study adopted a mixed research design (descriptive and correlational research designs). The 18 macroeconomic variables data were extracted from Kenya National Bureau of Statistics and World Bank for the period 1970 to 2019. The R software was utilized to conduct all the data analysis. Principal Component Analysis was used to reduce the dimensionality of the data, where the original data set matrix was reduced to Eigenvectors and Eigenvalues. A hierarchical regression model was fitted on the extracted components, and R2 was used to determine whether the components were a good fit for predicting economic growth. The results from the study showed that the first component explained 73.605 % of the overall Variance and was highly correlated with 15 original variables. Additionally, the second principal component described approximately 10.03% of the total Variance, while the two variables had a higher positive loading into it. About 6.22% of the overall variance was explained by the third component, which was highly correlated with only one of the original variables. The first, second, and third models had F statistics of 2385.689, 1208.99, and 920.737, respectively, and each with a p-value of 0.0001<5% was hence implying that the models were significant. The third model had the lowest mean square error of 17.296 hence described as the best predictive model. Since component 1 had the highest Variance explained, and model 1 had a lower p-value than other models, Principal component 1 was more reliable in explaining economic growth. Therefore, it was concluded that the macroeconomic variables associated with the monetary economy, the trade and openness of the economy with government activities, the consumption factor of the economy, and the investment factor of the economy predict economic growth in Kenya. The study recommends that PCA should be utilized when dealing with more than 15 variables, and hierarchical regression model building technique be used to determine the partial variance change among the independent variables in regression modeling.
Evaluating the Predictive Ability of Seasonal Autoregressive Integrated Moving Average (SARIMA) Models When Applied to Food and Beverages Price Index in Kenya
(2022) Wanjuki, T. M.; Muriithi, D. K.; Wagala, A.
Price instability has been a major concern in most economies. Kenya's commodity markets have been characterized by high price volatility affecting investment and consumer behaviour due to uncertainty on future prices. Therefore, precise forecasting models can help consumers plan for their expenditure and government policymakers formulate price control measures. Due to the seasonality of Kenya's food and beverage price indices, the current study postulates that the Seasonal Autoregressive Integrated Moving Average (SARIMA) model can best be the best fit model for the data. The study used secondary data on Kenya's monthly food and beverage prices index from January 1991 to February 2020 to examine the predictive ability of the possible SARIMA models based on the minimization of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The cross- validation test results of 6, 12, 18, 24, 30, and 36 step-ahead forecasts demonstrated that SARIMA models are unstable for use in forecasting over a long-time period with a tendency of increasing prediction errors with an increase in the forecast period. It is anticipated that the findings of the current study will provide necessary valuable information to the policymakers and stakeholders to understand future trends in commodity price.
Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models.
(2021) Wanjuki, T. M.; Muriithi, D. K.; Wagala, A.
Price stability is the primary monetary policy objective in any economy since it protects the interests of both consumers and producers. As a result, forecasting is a common practice and a vital aspect of monetary policymaking. Future predictions guide monetary and fiscal policy tools that that be used to stabilize commodity prices. As a result, developing an accurate and precise forecasting model is critical. The current study fitted and forecasted the food and beverages price index (FBPI) in Kenya using seasonal autoregressive integrated moving average (SARIMA) models. Unlike other ARIMA models like the autoregressive (AR), Moving Average (MA), and non-seasonal ARMA models, the SARIMA model accounts for the seasonal component in a given time series data better forecasts. The study relied on secondary data obtained from the KNBS website on monthly food and beverage price index in Kenya from January 1991 to February 2020. R-statistical software was used to analyze the data. The parameter estimation was done using the Maximum Likelihood Estimation method. Competing SARIMA models were compared using the Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE),.and Mean Absolute Percentage Error (MAPE). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The forecasting ability evaluation statistics MAE = 2.00%, MAPE = 1.62% and MASE= 0.87%. The 24-step ahead forecasts showed that the FPBI is unstable with an overall increasing trend. Therefore, the monetary policy committee ought to control inflation through monetary or fiscal policy, strengthening food security and trade liberalization.
Foreign Exchange Rate Volatility and Its Effect on International Trade in Kenya
(2022) Titus, O. M.; Muriithi, D. K.; Wagala, A.
Exchange rate volatility has received much attention in economic research especially with the advent of floating exchange regimes. The volatile nature of exchange rate is generally perceived as having negative affect on international trade. However, the theoretical and empirical perspective are mixed on the nature of the relationship. This study aimed at examining analyzing the moderating effect of foreign exchange reserves on the relationship between foreign exchange rate volatility and international trade in Kenya. The study used error correction model in the analysis of the time series data for the study period which spanned between 1966-2018. Results show that controlling for inflation rate, interest rate and gross domestic product, foreign exchange reserves had a positive and statistically significant moderating effect at 5% significant level on the relationship between foreign exchange rate volatility and international trade with R2 of 0.9557. The study recommends maintaining enough stock of foreign exchange reserves to cushion the economy from adverse effects of exchange rate volatility. The findings of the study will provide relevant information in the formulation of and implementation of an effective monetary policy that will promote exchange rate stability and improve the country’s performance in international trade.
Mathematical Analysis of a Comprehensive HIV AIDS Model: Treatment versus Vaccination
(HIKARI Ltd,, 2013) Okongo, M. O.; Kirimi, J.; Murwayi, A. L. Murwayi; Muriithi, D. K.
A comprehensive deterministic HIV/AIDS transmission model incorporating social behaviour, treatment, vaccination, stages of infection, age structures, discrete time delay and vertical transmission is presented and rigorously analyzed. Two age structures are considered with group one consisting of children aged (0 - a) years and group two consisting of adults aged (a) years and above. In this study we investigate wether a trade-off exists between vaccination and treatment. Numerical simulations shows that treatment that does not reduce infectiousness is worse than when the treatment is not applied at all, however when coupled with effective counseling, then it is very effective in combating the spread of the disease and finally eliminating it. A trade off seems to exists between vaccination and treatment and therefore careful considerations should be made when vaccination and treatment is to be applied together because a combination of the two could be counterproductive or helpful depending on how it is implemented.
Ordinal Logistic Regression Versus Multiple Binary Logistic Regression Model for Predicting Student Loan Allocation
(2012) Muriithi, D. K.; Kihoro, J.; Waititu, A.
This paper examines two different methodologies to a classification problem of higher education loan applicants. The paper looks into the allocations made by the Higher Education Loans Board (HELB) relative to the economic status of the applicant. In this article, we modeled Higher Education Loans Board (HELB) loan application data from three public universities to determine whether the loan was allocated based on the needs of the respective applicants. The data was classified into two natural categories of those not allocated the loan (0) and those allocated the loan (1). This paper classified further to consider the amounts awarded by the HELB. This was possible since we observed that HELB loans were awarded in distinct categories (Kshs 0, Kshs 35,000, Kshs 40,000, Kshs 45,000, Kshs 50,000), Kshs 55,000 Kshs 60,000). In this study, we used ordinal logistic regression and multiple binary logistic regressions in classifying the applicants into the identified categories. The models were generated that included all predictor variables that were useful in predicting the response variable. This study found that HELB allocate a loan amount to Kshs 40,000 but anything behold Kshs 40,000 is based on information provided by an applicant. The study revealed that the loans were not awarded based on the need of respective applicants. This has led to misclassification when allocating loan. The study found that wealth and amount of fees paid for siblings were other factors that could be considered to identify needy applicants. This results show that an ordinal regression model gives accurate estimates that can enable HELB make a viable awarding decision. It is expected that proper determination of the most accurate model will go a long way in minimizing the number of mis-classifications when awarding HELB loan. The study raises questions on the criteria used by HELB in loan allocation but further studies may be commissioned to confirm or disapprove our findings.
Singular Spectrum Analysis: An Application to Kenya’s Industrial Inputs Price Index
(EJ-MATH, European Journal of Mathematics and Statistics., 2022) Emmanuel, K. K.; Wagala, A.; Muriithi, D. K.
Time series modeling and forecasting techniques serve as gauging tools to understand the time-related properties of a given time series and its future course. Most financial and economic time series data do not meet the restrictive assumptions of normality, linearity, and stationarity of the observed data, limiting the application of classical models without data transformation. As non-parametric methods, Singular Spectrum Analysis (SSA) is data- adaptive; hence do not necessarily consider these restrictive assumptions as in classical methods. The current study employed a longitudinal research design to evaluate how SSA fist Kenya’s monthly industrial inputs price index from January 1992 to April 2022. Since 2018, reducing the costs of industrial inputs has been one of Kenya’s manufacturing agendas to level the playing field and foster Kenya’s manufacturing sector. It was expected that Kenya’s Manufacturing Value Added hit a tune of 22% by 2022. The study results showed that the SSA (L = 12, r =7) (MAPE = 0.707%) provides more reliable forecasts. The 24-period forecasts showed that the industrial inputs price index remains high above the index in 2017 before the post-industrial agenda targeting a reduction in the cost of industrial inputs. Thus, the industrial input prices should be reduced to a sustainable level.