American Journal of Applied Mathematics and Statistics
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American Journal of Applied Mathematics and Statistics. 2018, 6(1), 17-24
DOI: 10.12691/ajams-6-1-4
Open AccessArticle

Forecasting Household Credit in Kenya Using Bayesian Vector Autoregressive (BVAR) Model

Caspah Lidiema1, , Anthony Waititu1, Thomas Mageto1 and Anthony Ngunyi2

1Department of Statistics and Actuarial Sciences Jomo Kenyatta University of Agriculture and Technology

2Department of Statistics and Actuarial Sciences Dedan Kimathi University of Technology

Pub. Date: March 28, 2018

Cite this paper:
Caspah Lidiema, Anthony Waititu, Thomas Mageto and Anthony Ngunyi. Forecasting Household Credit in Kenya Using Bayesian Vector Autoregressive (BVAR) Model. American Journal of Applied Mathematics and Statistics. 2018; 6(1):17-24. doi: 10.12691/ajams-6-1-4


This research paper use Bayesian VAR framework to forecast the household credit in the dynamic market of foreign remittances inflow to Kenya. The Bayesian VARs model in this study employs the sims-Zha prior to estimate. Bayesian vector autoregressive (BVAR) uses Bayesian methods to estimate a vector autoregressive (VAR). In that respect, the difference with standard VAR models lies in the fact that the model parameters are treated as random variables, and prior probabilities are assigned to them. This study employed data from the Kenyan Market for the period January 2005-December 2017. The forecast results were compared with the standard ARIMA model and the findings confirm that the BVAR approach outperforms the ARIMA model. Financial institutions can therefore use Bayesian VAR and other Bayesian models in predicting credit uptake given several micro-economic conditions. Banks should also find ways of tapping into these remittances especially those that pass through informal channels to improve their earnings from processing fees and also enhance the financial inclusion agenda through increasing account opening and loan uptake.

Bayesian remittance household credit

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