MODELING REGRESSION ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (RANFIS) FOR PANEL DATA

Abstract

Panel data combine cross-sectional and time-series data. Data on economic, business, social, and development issues are often presented in panel data. In constructing the panel data regression model, it is necessary to take various steps for testing the model specifications, including the Chow test and the Hausman test. This study constructed a classical panel data regression model and the regression adaptive neuro-fuzzy inference system (RANFIS). The RANFIS model is a regression model by applying fuzzy and neural network (NN) techniques expected to overcome the problem of uncertainty. One of the main problems in constructing an optimal RANFIS is selecting input variables. The input variables of RANFIS are selected on the basis of the best classical regression. These inputs are classified into optimal clusters, which depend on the degree of fuzzy membership functions. The rule bases of RANFIS are determined on the basis of optimal inputs and its clusters. The empirical study in this research is to construct a panel data regression model for the Human Development Index (HDI) in Central Java in 2017-2019. HDI depends on several variables such as the school participation rate, number of health workers, public health complaints, population growth rate, and poverty severity index as predictor variables. Based on classical regression, three variables were used as optimal inputs for RANFIS modeling. Evaluation of model performance was measured based on the RMSE and MAPE values. Based on the RANFIS, the values of RMSE and MAPE were 3.227 and 3.299, respectively. Keywords: Panel Data Regression, Human Development Index, Regression Adaptive Neuro-Fuzzy Inference System DOI： https://doi.org/10.35741/issn.0258-2724.58.3.57