Книги
Truncated priors for tempered hierarchical Dirichlet process vector autoregression
Книги
Truncated priors for tempered hierarchical Dirichlet process vector autoregression
Seleznev, S. Truncated priors for tempered hierarchical Dirichlet process vector autoregression / S. Seleznev. — Moscow : Bank of Russia, 2019. — 37 p.: il.. — (Working paper series; 47, october). — References: p. 16-18.
Поделиться
Вконтакте 
Одноклассники 
Telegram Аннотация
We construct priors for the tempered hierarchical Dirichlet process vector autoregression model (tHDP-VAR) that in practice do not lead to explosive forecasting dynamics. Additionally, we show that tHDP-VAR and its variational Bayesian approximation with heuristics demonstrate competitive or even better forecasting performance on US and Russian datasets.
-
УДК:330.4
Рекомендовано к ознакомлению
- 1. Khabibullin, R. Stochastic gradient variational Bayes and normalizing flows for estimating macroeconomic models / R. Khabibullin, S. Seleznev. — Moscow : Bank of Russia, 2020. — 49 p.. — (Working paper series. 61, september).
- 2. Kreptsev, D. DSGE model of the Russian economy with the banking sector / D. Kreptsev, S. Seleznev. — Moscow : Bank of Russia, 2017. — 79 p.. — (Working paper series. 27, december).
- 3. Congdon, P.D. Bayesian Hierarchical Models / P. D. Congdon. — 2nd ed.. — Boca Raton : CRC Press, 2020. — 579 p.. — ISBN 978-1-4987-8575-4.
- 4. Styrin, K. Forecasting inflation in Russia by dynamic model averaging / K. Styrin. — Moscow : Bank of Russia, 2018. — 44 p.. — (Working paper series. 39, december).
- 5. Khabibullin, R. Forecasting the implications of foreign exchange reserve accumulation with an agent-based model / R. Khabibullin, A. Ponomarenko, S. Seleznev. — Moscow : Bank of Russia, 2018. — 30 p.. — (Working paper series. 37, november).
- 6. Andreyev, M. Adding a fiscal rule into a DSGE model: how much does it change the forecasts? / M. Andreyev. — Moscow : Bank of Russia, 2020. — 53 p.. — (Working paper series. 64, november).
- 7. Бабешко, Л.О. Практика эконометрических исследований в Gretl / Л. О. Бабешко, И. В. Орлова. — Москва : Центркаталог, 2023. — 299 с.. — (Вузовский учебник). — ISBN 978-5-903268-74-0.
- 8. Chihwa, K. High-dimensional econometrics and identification / K. Chihwa, L. Long. — New Jersey : World Scientific, 2019. — 164 p.. — ISBN 978-981-120-015-1.
- 9. Anatolyev, S. Methods for estimation and inference in modern econometrics / S. Anatolyev, N. Gospodinov. — Boca Raton : CRC Press, 2019. — 219 p.. — ISBN 978-0-367-38266-7.
- 10. Deryugina, E.B. A large Bayesian vector autoregression model for Russia / E. B. Deryugina, A. A. Ponomarenko. — Moscow : Bank of Russia, 2015. — 23 p.. — (Working Paper Series. № 1/March).
Отзывы читателей
0