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Particle Filtering and Parameter Learning
| Content Provider | Semantic Scholar |
|---|---|
| Author | Johannes, Michael S. Polson, Nicholas G. |
| Copyright Year | 2007 |
| Abstract | This paper provides a new approach for sequentially learning parameters and states in a wide class of state space models using particle filters. Our approach generates direct i.i.d. samples from a particle approximation to the joint posterior distribution of both parameters and latent states, avoiding the use of and the degeneracies inherent in sequential importance sampling. We illustrate the efficiency of our approach by sequentially learning parameters and filtering states in two models: a log-stochastic volatility model and robust version of the Kalman filter model with t-errors in both the observation and state equation. In both cases, we show using simulated data that our approach efficiently learns the parameters and states sequentially, generating higher effective sample sizes than existing algorithms. We use the approach for two real data examples, sequentially learning in a stochastic volatility model of Nasdaq stock returns and about predictable components in a model of core inflation. ∗Johannes is at the Graduate School of Business, Columbia University, 3022 Broadway, NY, NY, 10027, mj335@columbia.edu. Polson is at the Graduate School of Business, University of Chicago, 5807 S. Woodlawn, Chicago IL 60637, ngp@gsb.uchicago.edu. We thank Seung Yae for extraordinary research assistance and seminar participants at University of Chicago, SAMSI 2008, the 2007 Summer Meetings of the Econometric Society, and the 2005 ICMS meeting on Parameter Estimation and Continuous Time Models. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://faculty.chicagobooth.edu/nicholas.polson/research/papers/Exact.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Approximation Broadway (microprocessor) Columbia (supercomputer) Degenerate energy levels Estimation theory Importance sampling Kalman filter Particle filter Population Parameter Sample Size Sampling (signal processing) State space Volatility |
| Content Type | Text |
| Resource Type | Article |
