We propose a novel estimation approach for a general class of semi-parametric time series models where the conditional expectation is modeled through a parametric function. The proposed class of estimators is based on a Gaussian quasi-likelihood function and it relies on the specification of a...

A new two-parameter model is proposed using the Kavya-Manoharan (KM) transformation family and Burr X (BX) distribution. The new model is called the Kavya-Manoharan-Burr X (KMBX) model. The statistical properties are obtained, involving the quantile (QU) function, moment (MOs), incomplete MOs,...

We characterize the quasi-likelihood functions that may elicit expectiles and find that the family has a unique representation under standard conditions for linear regression. The only distribution that elicits expectiles as its quasi-maximum likelihood estimator under general conditions is an...

In this paper, we propose a new method for estimating and forecasting asymmetric stochastic volatility models. The proposal is based on dynamic linear models with Markov switching written as state space models. Then, the likelihood is calculated through Kalman filter outputs and the estimates...