Mathematics and Statistics

Permanent URI for this community

http://192.168.30.20:4000/handle/123456789/11

Browse

Browsing Mathematics and Statistics by Author "Ilembo, Bahati"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Analysis of multi-SIM behaviour in Tanzania’s telecom market using binary logit model
    (Tanzania Journal of Development Studies, 2022) Ilembo, Bahati ; Walwa, Jackson
    This paper examines factors for multi SIMs usage in the mobile services, and its economic implications to mobile operators in Tanzania. A random sample of 288 mobile phones subscribers from six mobile operators were included in the study. The study used binary logit model to estimate drivers for multi-SIMs usage, with marginal effects calculated to indicate appropriately the probabilities of usage for individual parameters used in the model. The findings showed that differences in perceiving quality of services and product differentiation are the main drivers for multi-SIMs usage. The multi-SIMs users are satisfied with multiple operators as no one operator provides a combination of their communication needs successfully. Also, customer-care related reasons like inaccessibility of sim swap raises customer’s SIM multiplicity. The behaviour of customers to own multiple SIM cards increases the level of customer spending to multiple operators and reduces customer’s profitability. This demands that network managements improve network quality, promotional activity, and customer care to win customers’ share of usage.
  • No Thumbnail Available
    Item
    Unfolding the potential of the ARIMA model in forecasting maize production in Tanzania
    (Business Analyst Journal (Emerald Publishing Limited ), 2023) Lwaho, Joseph; Ilembo, Bahati
    Purpose – This paper was set to develop a model for forecasting maize production in Tanzania using the autoregressive integrated moving average (ARIMA) approach. The aim is to forecast future production of maize for the next 10 years to help identify the population at risk of food insecurity and quantify the anticipatedmaize shortage. Design/methodology/approach – Annual historical data on maize production (hg/ha) from 1961 to 2021 obtained from the FAOSTAT database were used. The ARIMA method is a robust framework for forecasting time-series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung-Box test. Findings – The results suggest that ARIMA (1,1,1) is the most suitable model to forecast maize production in Tanzania. The selected model proved efficient in forecasting maize production in the coming years and is recommended for application. Originality/value – The study used partially processed secondary data to fit for Time series analysis using ARIMA (1,1,1) and hence reliable and conclusive results
  • Thumbnail Image
    Item
    Vector autoregressive approach after first differencing: A time series analysis of inflation and its determinants in Tanzania
    (Oradea Journal of Business and Economics, 2021) Cheti, Rachel R. ; Ilembo, Bahati
    The objective of the study was to examine the trend of inflation and its key determinants in Tanzania. We used secondary time series data observed annually from January 1970 to 2020 which are inflation rate, GDP, Exchange rate and money supply. The vector autoregressive (VAR) model was employed for modeling. Augmented Dickey-Fuller test (ADF) found that inflation rate, Gross Domestic Product (GDP), exchange rate and Money supply (M3) were initially non-stationary but they became stationary after first differencing so as to proceed with the analysis. Preliminary tests before obtaining vector auto regressive model were carried out before determining the relationship between the variables. Diagnostic test such as serial correlation, heteroscedasticity, stability and normality were also important to evaluate the model assumptions and investigate whether or not there are observations with a large, undue influence on the analysis. We used Granger causality test (GCT) to determine causal- effect relationship between the variables. The results show that, there is a long run relationship between the variables, also the results showed that exchange rate and money supply (M3) both have a positive impact on inflation rate while gross domestic product (GDP) revealed a negative impact on inflation rate. Finally, the forecast of inflation rate for 15 years ahead was performed. The study recommends that the government should pursue both contractionary monetary policy and fiscal policy in order to control inflation in the country