eng Science and Education Publishing American Journal of Applied Mathematics and Statistics 2328-7292 2018-04-04 6 1 17 24 10.12691/ajams-6-1-4 AJAMS2018614 article Caspah Lidiema lidiema.research@gmail.com 1 Anthony Waititu 1 Thomas Mageto 1 Anthonyn Ngunyi 2 Department of Statistics and Actuarial Sciences Jomo Kenyatta University of Agriculture and Technology Department of Statistics and Actuarial Sciences Dedan Kimathi University of Technology 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. http://pubs.sciepub.com/ajams/6/1/4/ajams-6-1-4.pdf Bayesian remittance household credit