|
|
|
|
||||
|
||||
A Compound Gauss-Markov Random Field (CGMRF) Modeling of Philippine Unemployment DataRolando Danganan Navarro Jr.University of the Philippines, Los Baños - School of Statistics Jose Ramon AlbertStatistical Research and Training Center Proceedings of the 24th Samahang Pisika ng Pilipinas National Physics Congress, Vol. 3, pp. 1-4, 2006 Abstract: The presence of discontinuities in the January rounds of Philippine Unemployment Data from 1981-2006 is investigated by way of modeling these data as a noisy Compound Gauss-Markov Random Fields (CGMRF). The likelihood and prior hyper-parameters are respectively estimated with wavelet shrinkage and least squares. The posterior random field signal and its line process were obtained using the Gibbs Sampler and were optimized by Simulated Annealing. Results indicate that there has been an abrupt change in unemployment rates in the mid-1980s and in 2005-2006. The former may reflect the political and economic crisis that followed the Aquino assassination, while the latter changes reflect the new official definition of unemployment adopted in 2005.
Number of Pages in PDF File: 4 Keywords: Philippine Economic Data, Unemployment, Gauss-Markov Random Fileds, Markov Chain Monte Carlo, Wavelet Shrinkage JEL Classification: C11,C13,C15,C22,C50,C51,C52,C60,E24,E27,J64 Accepted Paper SeriesDate posted: January 30, 2007Suggested CitationContact Information
|
|
||||||||||||||||
© 2013 Social Science Electronic Publishing, Inc. All Rights Reserved.
FAQ
Terms of Use
Privacy Policy
Copyright
This page was processed by apollo2 in 0.328 seconds
