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

Caspah Lidiema^1, , 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

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

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.

Keywords

Bayesian, remittance, household credit

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]	Bayes T, Price R (1763). “An Essay Towards Solving a Problem in the Doctrine of Chances. By the late Rev. Mr. Bayes, communicated by Mr. Price, in a letter to John Canton, MA. and F.R.S.” Philosophical Transactions of the Royal Society of London, 53, 370-418.
[2]	Statisticat, L. L. C. (2016). Laplaces Demon: Complete Environment for Bayesian Inference. R package version 16.0. 1.
[3]	Sims CA. 1980. Macroeconomics and reality. Econometrica 48: 1-48.
[4]	Chen, F. Y., & Liao, S. L. (2009). Modelling VaR for foreign-asset portfolios in continuous time. Economic Modelling, 26(1), 234-240.
[5]	De Medeiros, O. R., Van Doornik, B. F. N., & de Oliveira, G. R. (2011). Modeling and forecasting a firm's financial statements with a VAR-VECM model. CEP, 70843, 030.
[6]	Pecican, E. S. (2010). Forecasting based on open VAR model. Romanian Journal of Economic Forecasting, 13(1), 59-69.
[7]	Sims (2007) Bayesian Methods in Applied Econometrics, or, Why Econometrics Should Always and Everywhere Be Bayesian. Technical Report, Princeton University.
[8]	Villani, M. (2009). Steady-state priors for vector autoregressions. Journal of Applied Econometrics, 24(4), 630-650.
[9]	https://www.centralbank.go.ke/diaspora-remittances/.
[10]	Meyer, D., & Shera, A. (2017). The impact of remittances on economic growth: An econometric model. Economi A, 18(2), 147-155.
[11]	Khan, Z. S., & Islam, S. (2013). The effects of remittances on ination: evidence from Bangladesh. Journal of Economics and Business Research, 19(2), 198-208.
[12]	Timsina, N. (2014). Impact of bank credit on economic growth in Nepal. NBER Working Paper, (22).
[13]	Qurbanalieva, N. (2013). An empirical study of factors affecting ination in Republic of Tajikistan. Journal of Applied Economic Sciences Quarterly, ASERS Publishing, 2, 229-246.
[14]	Dragutinovic Mitrovic, R., & Jovičić, M. (2006). Macroeconomic analysis of causes and effects of remittances: a panel model of the SEE countries and a case study of Serbia (No. 63). The Vienna Institute for International Economic Studies, wiiw.
[15]	Ramos, F. F. R. (2003). Forecasts of market shares from VAR and BVAR models: a comparison of their accuracy. International Journal of Forecasting, 19(1), 95-110.
[16]	Caraiani, P. (2010). Forecasting Romanian GDP using BVAR model. Romanian Journal of Economic Forecasting,13(4),76-87
[17]	Sacakli-Sacildi, I. (2015), Do BVAR Models Forecast Turkish GDP Better Than UVAR Models? British Journal of Economics, Management & Trade. 7(4), 259-268.
[18]	Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer Science & Business Media.
[19]	Robert, C. P. (2001), The Bayesian Choice, Second Edition, New York: Springer-Verlag.
[20]	Villa, C., & Walker, S. G. (2014). Objective prior for the number of degrees of freedom of at distribution. Bayesian Analysis, 9(1), 197-220.
[21]	Karlsson, Sune (2015). Forecasting with Bayesian Vector Autoregression. Handbook of Economic Forecasting. 2 B: 791-897
[22]	OECD (2015), “How to restore a healthy financial sector that supports long-lasting, inclusive growth?”, OECD Economics Department Policy Notes, No. 27, June 2015.
[23]	Di Maggio, M., Kermani, A., Ramcharan, R., & Yu, E. G. (2017). Household Credit and Local Economic Uncertainty. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2991227.
[24]	Brown, R. P., & Carmignani, F. (2015). Revisiting the effects of remittances on bank credit: a macro perspective. Scottish Journal of Political Economy, 62(5), 454-485.
[25]	Brandt, P. T., & Freeman, J. R. (2005). Advances in Bayesian time series modeling and the study of politics: Theory testing, forecasting, and policy analysis. Political Analysis, 14(1), 1-36.
[26]	Koop, G., & Korobilis, D. (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends in Econometrics, 3(4), 267-358.
[27]	Sims, Christopher A. and Tao A. Zha. 1998. “Bayesian Methods for Dynamic Multivariate Models.” International Economic Review 39(4): 949-968.
[28]	Adenomon, M. O., Michael, V. A., & Evans, O. P. (2016). On the Performances of Classical VAR and Sims-Zha Bayesian VAR Models in the Presence of Collinearity and Autocorrelated Error Terms. Open Journal of Statistics, 6(01), 96-132.
[29]	Samuel, T., & Abdul, F. (2017). Policy Innovations and Sectoral Credit Expansion in Kenya. KBA Working paper Series, 20.
[30]	Ronayne, D. (2011). Which impulse response function?.
[31]	Dziak, JJ., Cofiman, D.L., Lanza, S.T., & Li, R. (2017). Sensitivity and Specificity of information criteria. Peer J Pre Prints.
[32]	Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Fourth Edition. Nelson Education.