Bayesian Analysis of an Unobserved-Component Time Series Model of GDP With Markov-Switching and Time-Varying Growths

We propose an unobserved-component time series model of gross domestic product that includes Markov switching as an unobserved component. In addition to a trend component, the model has two time-varying drift components. One drift represents the expected rate of growth during recession; the other dr...

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Bibliographic Details
Published in:Journal of business & economic statistics 1999-10, Vol.17 (4), p.456-465
Main Authors: Luginbuhl, Rob, Vos, Aart de
Format: Article
Language:English
Subjects:
Algorithms
Analytical forecasting
Bayesian analysis
Bayesian method
Data smoothing
Economic growth rate
Economic models
Economic recessions
GDP
Gibbs sampler
Gross Domestic Product
Gross national product
Kalman filter
Kalman filters
Markov analysis
Mathematical economics
Mathematical methods
Metropolis algorithm
Recession
Stochastic processes
Studies
Switching regime
Time series
Time series models
Transition probabilities
U.S.A
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Description
Summary:We propose an unobserved-component time series model of gross domestic product that includes Markov switching as an unobserved component. In addition to a trend component, the model has two time-varying drift components. One drift represents the expected rate of growth during recession; the other drift represents the expected rate during expansion. Estimates indicate a substantial decline in the latter annual rate for the United States from 6.4% in 1950 to 3.6% by 1990. We have employed weak priors based on prewar data. The estimation makes use of the Gibbs sampler and the Metropolis algorithm.
ISSN:0735-0015
1537-2707
DOI:10.1080/07350015.1999.10524833