Title:      Binary and Ordinal Time Series with AR(p) Errors:
Bayesian Model Determination for  Latent High-Order Markov
Process 

Authors:    Xun Pang

Entrydate:  2008-07-16 01:23:32

Keywords:   Autoregressive Errors; Auxiliary Particle Filter; 
Fixed-lag Smoothing; Markov Chain Monte Carlo (MCMC); Political
Science; Sampling Importance Resampling(SIR)

Abstract:   To directly and adequately correct serial
correlation in binary and ordinal response data, this paper
proposes a probit model with errors following a pth-order
autoregressive process, and develops simulation-based methods in
the Bayesian context to handle computational challenges of
posterior estimation, model comparison, and lag order
determination. Compared to the extant methods, such as quasi-ML,
GCM, and and simulation-based ML estimators, the current method
does not rely on the properties of the big variance-covariance
matrix or the shape of the likelihood function. In addition, the
present model efficiently handles high-order autocorrelated
errors that raise computationally formidable difficulties to the
conventional methods. By applying a mixed sampler of the Gibbs
and Metropolis-Hastings algorithm, the posterior distributions
of the parameters do not depend on initial observations.  The
auxiliary particle filter, complemented by the fixed-lag
smoothing, is extended to approximate Bayes Factors for models
with latent high-order Markov processes. Computational methods
are tested with empirical data. Energy cooperation policies of
the International Energy Agency are analyzed in terms of their
effects on global oil-supply security. The current model with
different lag orders, together with other competitive models, 
is estimated and compared.  

http://polmeth.wustl.edu/retrieve.php?id=806

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