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Bayes Estimates of Markov Trends in Possibly Cointegrated Series: An Application to U.S. Consumption and Income

Richard Paap and Herman K. van Dijk
Journal of Business & Economic Statistics
Vol. 21, No. 4 (Oct., 2003), pp. 547-563
Stable URL: http://www.jstor.org/stable/1392327
Page Count: 17
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Bayes Estimates of Markov Trends in Possibly Cointegrated Series: An Application to U.S. Consumption and Income
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Abstract

Stylized facts show that average growth rates of U.S. per capita consumption and income differ in recession and expansion periods. Because a linear combination of such series does not have to be a constant mean process, standard cointegration analysis between the variables to examine the permanent income hypothesis may not be valid. To model the changing growth rates in both series, we introduce a multivariate Markov trend model that accounts for different growth rates in consumption and income during expansions and recessions and across variables within both regimes. The deviations from the multivariate Markov trend are modeled by a vector autoregression (VAR) model. Bayes estimates of this model are obtained using Markov chain Monte Carlo methods. The empirical results suggest the existence of a cointegration relation between U.S. per capita disposable income and consumption, after correction for a multivariate Markov trend. This result is also obtained when per capita investment is added to the VAR.

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