Non-parametric Approach in Modelling Effects of Remittances on Household Credit in Kenya

Caspah Lidiema^1, , Anthony Waititu¹, Thomas Mageto¹ and Anthonyn 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 Anthonyn Ngunyi. Non-parametric Approach in Modelling Effects of Remittances on Household Credit in Kenya. American Journal of Applied Mathematics and Statistics. 2018; 6(1):25-35. doi: 10.12691/AJAMS-6-1-5

Abstract

Generalized Additive Models for Location, Scale and Shape (GAMLSS)is a very flexible model class, extending the classical Generalized Additive Model (GAM) framework. Not only the mean, but all distribution parameters are regressed to the predictors. It is suitable for fitting linear or non-linear parametric models using the distributions. Artificial Neural Networks (ANN) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience. The main advantages of using ANN is that, it has the ability to implicitly detect complex nonlinear relationships between dependent and independent variables and also has ability to detect all possible interactions between predictor variables. Given all the dynamic nature of these two models are their outlined merits, it’s important to test and see which of this model estimates parameters better and which of them a better model in forecasting financial data. To test and compare this models an application of effect remittances on household credit was be used. The study employed monthly data for period January 2005- December 2017 in Kenya. Our findings showed that mixed results where, GAMLSS performed better than ANN in estimation while ANN provided a better model in prediction than GAMLSS. Our results confirm that the surge in Remittances leads to increase credit uptake due to increased resource mobilization by financial institutions and also resource availability for loan repayment. The research recommends Banks and Financial institutions should also carry out their assessment using GAMLSS and ANN and come up with ways of tapping into remittances not only to boost their deposits but also increase their funds for issuing credit and hence increase interest income, and also boost financial inclusion in Kenya through increased consumer loans.

Keywords

GAMLSS, neural network, remittance, 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/

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