title: Applying Bayesian methods for macroeconomic modeling of business cycle phases
metadata.name: dc.contributor.author
metadata.value: Guseva, Maria E.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Silaev, Andrey M.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2021-11-17T12:38:27Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2021-11-17T12:38:27Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2021-06
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Guseva M. E., Silaev A. M. (2021) Applying Bayesian methods for macroeconomic modeling of business cycle phases. St Petersburg University Journal of Economic Studies, vol. 37, iss. 2, pp. 298–317.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu05.2021.205
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/33616
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: In the present research, the features of applying two models for estimating macroeconomic
  dynamic in the USA are investigated: Bayesian vector autoregression and Bayesian vector
  autoregression with Markov switching. The research goal is to identify periods, structure of
  fluctuations and the main directions of interaction of the variables (real US GDP and employment)
  using Bayesian vector autoregression models. Models with Markov chains include
  many equations (structures). The switching mechanisms between these structures are controlled
  by an unobservable variable that follows a first-order Markov process. The analyzed
  variables were taken from the first quarter of 1953 to the third quarter of 2015. The model parameters
  were estimated on the basis of a prior for the multivariate normal distribution — the
  inverse Wishart distribution (a generalization of the Minnesota a priori distribution). Basing
  on the results of the estimation of the two-dimensional model with Markov Switching the average
  GDP growth rate and expected duration of phases was calculated. The estimated model
  is acceptable for describing the US economy and with high accuracy describes the probability
  of being in a particular phase in different periods of time. On the basis of medium-term forecasts,
  root mean squared errors of the forecast are calculated and a conclusion is made about
  the most appropriate model. Within the framework of this paper, impulse response functions
  are built allowing to evaluate how variables in the model react on fluctuations, shocks.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: St Petersburg University Journal of Economic Studies;Volume 37; Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: Markov models
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: Bayesian estimation
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: business cycles
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Applying Bayesian methods for macroeconomic modeling of business cycle phases
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 298-317.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/33616/1/298-317.pdf
resources.format: Adobe PDF
resources.size: 1,44 MB