Inference on Filtered and Smoothed Probabilities in Markov-Switching Autoregressive Models
Bibliographic Data
| ID | 19419941 |
|---|---|
| Authors | Rocio Alvarez (0000-0001-5882-5046, Universidad Central de Chile), Máximo Camacho (0000-0003-3158-5614, Universidad de Murcia), M Rosario Marín (0000-0001-9228-6410, Universidad Politécnica de Cartagena), Manuel Ruiz (0000-0001-8153-276X, Universidad Politécnica de Cartagena) |
| Year | 2019 |
| Volume | 37 |
| Issue | 3 |
| Pages | 484-495 |
| Publication date | 2019-07-03 |
| 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 | 10.1080/07350015.2017.1380032 |
| OpenAlex | W2757848102 |
| Language | EN |
| References cited | 20 |
We derive a statistical theory that provides useful asymptotic approximations to the distributions of the single inferences of filtered and smoothed probabilities, derived from time series characterized by Markov-switching dynamics. We show that the uncertainty in these probabilities diminishes when the states are separated, the variance of the shocks is low, and the time series or the regimes are persistent. As empirical illustrations of our approach, we analyze the U.S. GDP growth rates and the U.S. real interest rates. For both models, we illustrate the usefulness of the confidence intervals when identifying the business cycle phases and the interest rate regimes
Autoregressive model · Econometrics · Economics · Inference · Markov chain · Statistical inference · Statistics · Computer Science · Financial Risk and Volatility Modeling · Mathematics · Monetary Policy and Economic Impact · Stochastic processes and financial applications · Applied Mathematics
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| Highly cited | No |
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