quantile regression

Currently, innovation in business models is considered corporate architecture. Innovation in business models shows how companies can take new approaches to develop their business. Companies can facilitate the entrepreneurship process by considering the innovations in their business model based on their capabilities. This will play a prominent role in designing and explaining the business model dynamics. This article investigates the effects of the innovation index on the business diversification index in selected listed companies in the Iranian capital market with the quantile regression approach from 2013 to 2019. The results of this study showed the positive and significant impact of the innovation index on business diversity in all three groups of competitive, concentrated, and semi-concentrated industries, but this effect was stronger among competitive industries. Also, the results of this study indicate the asymmetric effect of the innovation index in competitive and semi-centralized industries and the symmetrical effect of the innovation index in centralized industries.

Nolan and Ojeda-Revah (2013) proposed a regression model with heavy-tailed stable errors. In this paper we extend this method for multivariate heavy-tailed errors. Furthermore, A likelihood ratio test (LRT) for testing significant of regression coefficients is proposed. Also, confidence intervals based on Fisher information for Nolan and Ojeda-Revah (2013) method, called NOR, and LRT are computed and compared with well-known methods. At the end we provide some guidance for various error distributions in heavy-tailed cases.

latent variable models define as a wide class of regression models with latent variables that cannot be directly measured, the most important latent variable models are structural equation models. Structural equation modeling (SEM) is a popular multivariate technique for analyzing the interrelationships between latent variables. Structural equation models have been extensively applied to behavioral, medical, and social sciences. In general, structural equation models includes a measurement equation to characterize latent variables through multiple observable variables and a mean regression type structural equation to investigate how the explanatory latent variables affect the outcomes of interest. Despite the importance of the structural equations model, it does not provide an accurate analysis of the relationships between the latent variables. Therefore, the quantile regression method will be presented within the structural equations model to obtain a comprehensive analysis of the latent variables. we apply the quantile regression method into structural equation models to assess the conditional quantile of the outcome latent variable given the explanatory latent variables and covariates. The posterior inference is performed using asymmetric Laplace distribution. The estimation is done using the Markov Chain Monte  Carlo technique in Bayesian inference. The simulation was implemented assuming different distributions of the error term for the structural equations model and values for the parameters for a small sample size. The method used showed satisfactorily performs results.