| dc.creator |
Wagofya, Guntram M. |
|
| dc.date |
2019-09-05T12:29:47Z |
|
| dc.date |
2019-09-05T12:29:47Z |
|
| dc.date |
2018 |
|
| dc.date.accessioned |
2021-05-06T12:58:59Z |
|
| dc.date.available |
2021-05-06T12:58:59Z |
|
| dc.identifier |
Wagofya, G. M. (2018). Time series arima modelling of loan borrowing and repayment at Kifanya saccos limited in Njombe, Tanzania. Dodoma: The University of Dodoma |
|
| dc.identifier |
http://hdl.handle.net/20.500.12661/1778 |
|
| dc.identifier.uri |
http://hdl.handle.net/20.500.12661/1778 |
|
| dc.description |
Dissertation (MSc Statistics) |
|
| dc.description |
Savings and credit cooperative societies (SACCOS) in developing countries like Tanzania are very importance in solving the financial problem to low income groups especially in rural areas. SACCOS have the sole role of mobilizing savings that creates the source of funds to benefit their members. But for a SACCOS to prosper in its role of financial intermediation, it has to give more loans and ensure that most of them are repaid in time. To plan for such achievements the SACCOS needs accurate forecasting techniques to predict the future value of its loan portfolio. This study was conducted at Kifanya SACCOS in Njombe Region, Tanzania. The study focuses on modelling and forecasting of the monthly loan borrowing and repayment on short-term basis by using the autoregressive integrated moving average (ARIMA) models. The modelling process was preceded by analysing the components of the series of interest which revealed the presence of increasing trend and cyclic variations. Seasonal variation appeared to be very significant in loan borrowing only. The findings showed that the best time series model for monthly loan borrowing is ARIMA (0,1,1)(0,0,1)12 whilst that for loan repayment is ARIMA (1,1,2). Also, it was revealed that the developed models fit well the historical data and can be used in short-term forecasting of the future values of loan borrowing and its recovery. However, in some months the forecasts may not agree strongly with the observed values as the series under study are very volatile and this increases unpredictability in their future values. Therefore, it was recommended for future researchers to consider non-linear models such as the ARCH model together with its variants and SVM models in addition to the ARIMA model. |
|
| dc.language |
en |
|
| dc.publisher |
The University of Dodoma |
|
| dc.subject |
Tanzania |
|
| dc.subject |
Njombe |
|
| dc.subject |
SACCOS |
|
| dc.subject |
Kifanya SACCOS |
|
| dc.subject |
Loan |
|
| dc.subject |
Loan borrowing |
|
| dc.subject |
Loan repayment |
|
| dc.subject |
ARIMA modelling |
|
| dc.title |
Time series arima modelling of loan borrowing and repayment at Kifanya saccos limited in Njombe, Tanzania |
|
| dc.type |
Dissertation |
|