Trends and cycles in non-stationary panel models
type: Item
instance of: scholarly article
title: Trends and cycles in non-stationary panel models
zbMATH Open document ID: 1303.91132
DOI: 10.1504/IJMMNO.2014.059939
published in: International Journal of Mathematical Modelling and Numerical Optimisation
publication date: 2014-12-10T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 91B84
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 65C40
zbMATH DE Number: 6379200
zbMATH Keywords: non-stationary panel data
zbMATH Keywords: trends
zbMATH Keywords: cycle
zbMATH Keywords: unobserved components
author: Wen-Sheng Kang
MaRDI profile type: Publication
full work available at URL: ijmmno.2014.059939
OpenAlex ID: W2067186373
description: scientific article; zbMATH DE number 6379200