Analytical Derivatives for Markov Switching Models
Preprint
- 1 January 1998
- preprint
- Published by Elsevier in SSRN Electronic Journal
Abstract
This paper derives analytical gradients for a broad class of regime-switching models with Markovian state-transition probabilities. Such models are usually estimated by maximum likelihood methods, which require the derivatives of the likelihood function with respect to the parameter vector. These gradients are usually calculated by means of numerical techniques. The paper shows that analytical gradients considerably speed up maximum-likelihood estimation with no loss in accuracy. A sample program listing is included.Keywords
All Related Versions
This publication has 9 references indexed in Scilit:
- Rev RenePublished by Rev Rene - Revista da Rede de Enfermagem de Nordeste ,2023
- Time series analysis. James D. Hamilton Princeton University PressWilmott, 2003
- Regime switching with time-varying transition probabilitiesPublished by Oxford University Press (OUP) ,1994
- Duration-Dependent Transitions in a Markov Model of U.S. GNP GrowthJournal of Business & Economic Statistics, 1994
- Dynamic linear models with Markov-switchingJournal of Econometrics, 1994
- 9 Estimation, inference and forecasting of time series subject to changes in regimePublished by Elsevier ,1993
- The Hamilton model with a general autoregressive component: estimation and comparison with other models of economic time seriesJournal of Monetary Economics, 1990
- A New Approach to the Economic Analysis of Nonstationary Time Series and the Business CycleEconometrica, 1989
- A Markov model for switching regressionsJournal of Econometrics, 1973