A Class of Non-Gaussian State Space Models With Exact Likelihood Inference
Bibliographic Data
| ID | 19417348 |
|---|---|
| Authors | Drew Creal (University of Chicago, corresponding author), Drew D Creal (The University of Chicago Booth School of Business, Chicago, IL 60637 ()) |
| Year | 2017 |
| Volume | 35 |
| Issue | 4 |
| Pages | 585-597 |
| Publication date | 2017-10-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2015.1092977 |
| OpenAlex | W3121777082 |
| Language | EN |
| Citations received | 1 |
| References cited | 45 |
The likelihood function of a general nonlinear, non-Gaussian state space model is a high-dimensional integral with no closed-form solution. In this article, I show how to calculate the likelihood function exactly for a large class of non-Gaussian state space models that include stochastic intensity, stochastic volatility, and stochastic duration models among others. The state variables in this class follow a nonnegative stochastic process that is popular in econometrics for modeling volatility and intensities. In addition to calculating the likelihood, I also show how to perform filtering and smoothing to estimate the latent variables in the model. The procedures in this article can be used for either Bayesian or frequentist estimation of the model’s unknown parameters as well as the latent state variables.
Algorithm · Bayesian inference · Bayesian probability · Econometrics · Estimation theory · Frequentist inference · Gaussian · Gaussian process · Inference · Likelihood function · Marginal likelihood · Smoothing · State space · State variable · State-space representation · Statistics · Stochastic volatility · Computer Science · Financial Risk and Volatility Modeling · Insurance, Mortality, Demography, Risk Management · Mathematics · Statistical Distribution Estimation and Applications · Applied Mathematics · Artificial Intelligence
Time Series Analysis
Time Series Analysis by State Space Methods
Forecasting, Structural Time Series Models and the Kalman Filter
Stochastic Volatility
A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
On Gibbs sampling for state space models
Maximum Likelihood Estimation of Misspecified Models
A New Approach to Linear Filtering and Prediction Problems
| Citations per year | 0.17 |
|---|---|
| Citation span | 2020 - 2020 (1) |
| Highly cited | No |