Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models
DOI10.1080/07474938.2015.1031014zbMath1491.62117OpenAlexW2126513215MaRDI QIDQ5864370
Geert Mesters, Siem Jan Koopman, Marius Ooms
Publication date: 7 June 2022
Published in: Econometric Reviews (Search for Journal in Brave)
Full work available at URL: http://papers.tinbergen.nl/11090.pdf
importance samplingstochastic volatilityKalman filterfractional integrationforecastinglatent factors
Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The Model Confidence Set
- Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment
- Testing the assumptions behind importance sampling
- Modelling and forecasting noisy realized volatility
- Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models
- A new estimator of the fractionally integrated stochastic volatility model
- The detection and estimation of long memory in stochastic volatility
- State space modeling of long-memory processes
- Long memory processes and fractional integration in econometrics
- Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
- Computationally efficient methods for two multivariate fractionally integrated models
- The Fitting of Time-Series Models
- THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS
- Long Memory in Nonlinear Processes
- Long‐Memory Time Series
- Monte Carlo Estimation for Nonlinear Non-Gaussian State Space Models
- Fractional differencing
- AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
- Numerical Optimization
- DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
- On Gibbs sampling for state space models
- Likelihood analysis of non-Gaussian measurement time series
- Monte Carlo maximum likelihood estimation for non-Gaussian state space models
- A simple and efficient simulation smoother for state space time series analysis
- Semiparametric Bayesian Inference of Long‐Memory Stochastic Volatility Models
- Bayesian Inference in Econometric Models Using Monte Carlo Integration
- The Distribution of Realized Exchange Rate Volatility
- The simulation smoother for time series models
- Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
- Likelihood‐based Analysis of a Class of Generalized Long‐Memory Time Series Models
- Multivariate Stochastic Volatility: A Review
- Long-Run Linearity, Locally Gaussian Process, H-Spectra and Infinite Variances
- Numerical Methods of Statistics
This page was built for publication: Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models