Estimation and forecasting of the inflation, interest,literacy and unemployment rate of Pakistan using nonlinear regression models

Abstract

Background/Objectives: To analyze and investigate the relationships for the inflation rate, interest rate, literacy rate and unemployment rate of Pakistan with a high level of accuracy. Methods/Statistical analysis: Method of nonlinear least squares have been adopted to fit the nonlinear regression models to estimate the present and future trends for the inflation rate, interest rate, literacy rate and unemployment rate of Pakistan based on the data from the year 2000 to 2019. Various nonlinear regression models were tested by changing their degree and number of coefficients. For each trial, the goodness of fit was set at a 95% confidence level. The best regression models were selected on basis of goodness of fit, the correlation with the present data and the logical trend of future forecasts. Findings: The proposed nonlinear regression models are quite different from the conventional linear and nonlinear polynomial regression models. The fitted and forecasting graphs show very realistic results that can be used by policymakers with good accuracy. Novelty/Applications: Since the periodic abrupt changes in the quantitative response variables like inflation rate, interest rate, literacy rate and the unemployment rate of Pakistan have been smoothly incorporated; therefore, the government or other socio-economic practitioners may use the results for future planning and management of the resources depending upon these factors. Keywords: Inflation rate; literacy rate; interest rate; unemployment rate; nonlinear regression; forecasting