APPLICATION OF QUANTILE REGRESSION TO ASYMMETRIC PRICE TRANSMISSION ESTIMATION: INSIGHTS FROM MONTE CARLO SIMULATIONS

De-Graft H. Acquah
2017 Russian Journal of Agricultural and Socio-Economic Sciences  
Paper introduces and compares performances of Quantile regression approach to the conventional Ordinary Least Squares methods for estimation of asymmetric price transmission model when the true data generating process is known. Monte Carlo simulation results indicate that the estimates of the coefficients of the asymmetric price transmission model derived from the Least squares and the Quantile regression approaches are accurate and equivalent or close to their true values for normal data
more » ... r normal data regardless of variability in sample size. Least squares method is affected by outliers and yields inaccurate estimates of the coefficients of the asymmetric price transmission model across various sample sizes when the data contains outliers. Quantile regression estimation remains robust to outliers in large samples and provides estimates of the coefficients of the asymmetric price transmission model that are accurate and nearly equivalent to their true values. The evidence from Monte Carlo experimentation suggests that the proposed Quantile regression estimation is likely to do no worse than the OLS with normal dataset and promise to do better when the dataset has outliers within the asymmetric price transmission modelling context.
doi:10.18551/rjoas.2017-12.02 fatcat:7t4e3ri5sjgznfjckwbefh5zgq