Binomial Autoregressive Moving Average Models with an Application to U.S. Recessions

Center for Statistics in the Social Sciences Working Paper No. 56

25 Pages Posted: 1 Mar 2006

Date Written: February 20, 2006

Abstract

Binary Autoregressive Moving Average (BARMA) models provide a modeling technology for binary time series analogous to the classic Gaussian ARMA models used for continuous data. BARMA models mitigate the curse of dimensionality found in long lag Markov models and allow for non-Markovian persistence. The autopersistence function (APF) and autopersistence graph (APG) provide analogs to the autocorrelation function and correlogram. Parameters of the BARMA model may be estimated by either maximum likelihood or MCMC methods. Application of the BARMA model to U.S. recession data suggests that a BARMA(2,2) model is superior to traditional Markov models.

Keywords: time series, binary dependent variable, ARMA, BARMA, Gibbs sampling

JEL Classification: C220, C250, C110

Suggested Citation

Startz, Richard, Binomial Autoregressive Moving Average Models with an Application to U.S. Recessions (February 20, 2006). Center for Statistics in the Social Sciences Working Paper No. 56, Available at SSRN: https://ssrn.com/abstract=886092 or http://dx.doi.org/10.2139/ssrn.886092

Richard Startz (Contact Author)

UCSB ( email )

Department of Economics
University of California
Santa Barbara, CA 93106-9210
United States
805-893-2895 (Phone)

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