Title: Trends and cycles in non-stationary panel models

Authors: Wensheng Kang

Addresses: Department of Economics, Kent State University, New Philadelphia, OH, 44663, USA

Abstract: This paper utilises Bayesian approach to extract latent common trends and cycles of non-stationary panel data. I develop a Markov Chain Monte Carlo (MCMC) algorithm to explore the highly dimensional posterior distribution of the panel model. Numerical simulation shows that the Bayesian approach based on this algorithm is effective at both estimating the elements of regression coefficients and error variance matrix and extracting latent components. To illustrate the potential of the approach, the study applies the method to investigate quarterly metropolitan housing prices and daily dot-com stock prices. The empirical results show the stronger the long-run growth the higher the cyclical volatility.

Keywords: non-stationary panel data; trends; cycles; unobserved components; Bayesian approach; Markov Chain Monte Carlo; MCMC; numerical simulation; metropolitan housing prices; dot-com stock prices; long-run growth; cyclical volatility.

DOI: 10.1504/IJMMNO.2014.059939

International Journal of Mathematical Modelling and Numerical Optimisation, 2014 Vol.5 No.1/2, pp.108 - 130

Received: 08 Oct 2012
Accepted: 28 Jun 2013

Published online: 20 Mar 2014 *

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