OLS CONFIDENCE INTERVALS IN NON-LINEAR MODELS: BOOTSTRAP APPROACH
Abstract
Linear models has been a powerful econometric tool used to show the relationship between two or more variables. Many studies also use linear approximation for nonlinear cases as it still might show valid results. OLS method requires the relationship of dependent and independent variables to be linear, although many studies employ OLS approximation even for nonlinear cases. In this study, we are introducing alternative method of intervals estimation, bootstrap, in linear regressions when the relationship is nonlinear. We compare the traditional and bootstrap confidence intervals when data has nonlinear relationship. As we need to know the true parameters, we carry out a simulation study. Our research findings indicate that when error term has non-normal shape, bootstrap interval will outperform the traditional method due to no distributional assumption and wider interval width
Keywords:
OLS nonlinear model sample size confidence Interval bootstrap, accuracy interval size varianceReferences
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