Asymmetric effect with quantile regression for interval-valued variables

• Main Authors: Teerawut Teetranont, Woraphon Yamaka, Songsak Sriboonchitta
• Format: Book Series
• Published: 2018
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85037872340&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/43867
• Institution: Chiang Mai University

© Springer International Publishing AG 2018. In this paper, we propose a quantile regression with interval valued data using a convex combination method. The model we propose generalizes series of existing models, say typically with the center method. Three estimation techniques consisting EM algorithm, Least squares, Lasso penalty are presented to estimate the unknown parameters of our model. A series of Monte Carlo experiments are conducted to assess the performance of our proposed model. The results support our theoretical properties. Finally, we apply our model to empirical data in order to show the usefulness of the proposed model. The results imply that the EM algorithm provides a best fit estimation for our data set and captures the effect of oil differently across various quantile levels.