A Compound Gauss-Markov Random Field (CGMRF) Modeling of Philippine Unemployment Data
Rolando Danganan Navarro Jr.
University of the Philippines, Los Baños - School of Statistics
Jose Ramon Albert
Statistical Research and Training Center
Proceedings of the 24th Samahang Pisika ng Pilipinas National Physics Congress, Vol. 3, pp. 1-4, 2006
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,J64Accepted Paper Series
Date posted: January 30, 2007
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