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

Authors:    Xun Pang

Entrydate:  2008-07-06 20:28:40

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

Abstract:   In models for time series discrete responses, serial
correlated errors cause serious problems of biasedness and
inconsistency as well as inefficiency. To directly and
adequately correct serial correlation in binary and ordinal
response data, this paper proposes a probit model with errors
following a $p$th-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. This time series model
does not depend on initial values by applying a mixed sampler of
the Gibbs and Metropolis-Hastings algorithm. As for Baysian model
determination, the auxiliary particle filter, complemented by the
fixed-lag smoothing, is extended to approximate Bayes Factors for
models with latent high-order Markovian processes. Computational
methods are tested with empirical data. Effectiveness of energy
cooperation policies of the International Energy Agency on
global oil-supply security are analyzed. Multiple models with
different lag orders, together with other competitive models, 
are estimated and compared.  

http://polmeth.wustl.edu/retrieve.php?id=784

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