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, itâs 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.
Highlights
The empirical data analysis is based on a set of monthly data for the period January 2005 to December 2017 for Household Credit as a dependent variable, foreign remittances into Kenya as independent variable and lending rates as a moderating variable
The main objective of the study was to establish the best model between GAMLSS and Artificial Neural Networks (ANN) in estimating and predicting effects of remittances on Household credit
The two models employed in this study showed almost similar results, in which the relationship between remittances and household credit is positive and inverse relationship between the Lending rates and household credit, their main difference was in the accuracy if estimation and Prediction
Summary
A remittance is a transfer of money by a foreign worker to an individual in his or her home country. Money sent home by migrants competes with international aid as one of the largest financial inflows to developing countries. Is the paradigm shift in savings mobilization and credit extension which has increased the level of financial inclusion and the intermediation space. This Financial Intermediation has greatly been influenced by remittances from foreign countries. Aggarwal and Peria [4] confirmed that remittances tend increase during economic slowdowns and natural disasters as compared to private capital flows. While a surge in inflows, including aid flows, can erode a country’s competitiveness, remittances do not seem to have this adverse effect
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