Estimation in autoregressive models with Markov regime
Ricardo Ríos, Luis Rodríguez
Abstract
In this paper we derive the consistency of the penalized likelihood method for the number state of the hidden Markov chain in autoregressive models with Markov regimen. Using a SAEM type algorithm to estimate the models parameters. We test the null hypothesis of hidden Markov Model against an autoregressive process with Markov regime.
Create a lesson
Related papers
On Natural Exponential Families with Log-Affine Generalized Variance
Zeyu Cao, Shaosai Huang
Fundamental Limits of Coherent Imaging in The Presence of Atmospheric Turbulence
Songbin Liu, Hanyu Yang, Junren Chen et al.
Regularization vs. Convolution in Kernel-Based Hypothesis Testing
Omar Hagrass, Bharath K. Sriperumbudur
Inference for Realized Quadratic Covariation of R and R2 as a Measure of Return Asymmetry
Haozhe Jiang, Ostap Okhrin, Michael Rockinger
Variational Splines and Generalized Gaussian Processes
Michael Unser
Conformal Prediction at Performative Equilibrium: The Price of Performativity
Ahmed Mahrous, Roberto Di Pietro