Open Markov Chain Scheme Models Fed by Second Order Stationary and Non Stationary Processes

Manuel L. Esquível; Gracinda R. Guerreiro; José  M. Fernandes

doi:10.57805/revstat.v15i2.213

Open Markov Chain Scheme Models Fed by Second Order Stationary and Non Stationary Processes

Authors

Manuel L. Esquível Universidade Nova de Lisboa
Gracinda R. Guerreiro Universidade Nova de Lisboa
José M. Fernandes Universidade de Cabo Verde

DOI:

https://doi.org/10.57805/revstat.v15i2.213

Keywords:

Markov chains, Open Markov chain models, Second order processes, ARIMA, SARMA, Credit Risk

Abstract

We introduce a schematic formalism for the time evolution of a random open population divided into classes. With a Markov chain model, allowing for population entrances, we consider the flow of incoming members modeled by a time series - either ARIMA for the number of new incomings or SARMA for the residuals of a deterministic sigmoid type trend - and we detail the time series structure of the elements in each class. A practical application to real data from a credit portfolio is presented.

Downloads

REVSTAt2017v15n2_6

Published

2017-04-18

How to Cite

L. Esquível , M., R. Guerreiro , G., & M. Fernandes , J. (2017). Open Markov Chain Scheme Models Fed by Second Order Stationary and Non Stationary Processes. REVSTAT-Statistical Journal, 15(2), 277–297. https://doi.org/10.57805/revstat.v15i2.213