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Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer Science & Business Media.

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

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

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


American Journal of Applied Mathematics and Statistics. 2018, Vol. 6 No. 1, 17-24
DOI: 10.12691/ajams-6-1-4
Copyright © 2018 Science and Education Publishing

Cite this paper:
Caspah Lidiema, Anthony Waititu, Thomas Mageto, 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.

Correspondence to: Caspah  Lidiema, Department of Statistics and Actuarial Sciences Jomo Kenyatta University of Agriculture and Technology. Email: lidiema.research@gmail.com

Abstract

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.

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