Unobserved components with stochastic volatility
Simulation‐based estimation and signal extraction
Bibliographic Data
| ID | 21653255 |
|---|---|
| Authors | Mengheng Li (0000-0002-8807-7277, UTS Business School University of Technology Sydney Sydney New South Wales Australia), Siem Jan Koopman (0000-0002-4440-9524, Department of Econometrics and Data Science, School of Business and Economics Vrije Universiteit Amsterdam Amsterdam The Netherlands, corresponding author) |
| Year | 2021 |
| Volume | 36 |
| Issue | 5 |
| Pages | 614-627 |
| Publication date | 2021-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Applied Econometrics (JOURNAL) |
| Journal identifiers | ISSN: 1099-1255 • E-ISSN: 0883-7252 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/jae.2831 |
| OpenAlex | W3172597835 |
| Language | EN |
| Citations received | 1 |
| References cited | 38 |
The unobserved components time series model with stochastic volatility has gained much interest in econometrics, especially for the purpose of modelling and forecasting inflation. We present a feasible simulated maximum likelihood method for parameter estimation from a classical perspective. The method can also be used for evaluating the marginal likelihood function in a Bayesian analysis. We show that our simulation‐based method is computationally feasible, for both univariate and multivariate models. We assess the performance of the method in a Monte Carlo study. In an empirical study, we analyse U.S. headline inflation using different univariate and multivariate model specifications
Econometrics · Economics · Estimation · Statistics · Stochastic volatility · Complex Systems and Time Series Analysis · Computer Science · Financial Risk and Volatility Modeling · Mathematics · Stochastic processes and financial applications
Time Series Analysis by State Space Methods
Forecasting, Structural Time Series Models and the Kalman Filter
Why Has U.S. Inflation Become Harder to Forecast?
The Stochastic Volatility in Mean Model With Time-Varying Parameters
Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models
A Model of the Fed's View on Inflation
Core Inflation and Trend Inflation
Measuring the Level and Uncertainty of Trend Inflation
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |