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Estimating Moving Average Parameters: Classical Pileups and Bayesian Posteriors

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  • DeJong, David N
  • Whiteman, Charles H

Abstract

The authors analyze posterior distributions of the moving average parameter in the first-order case and sampling distributions of the corresponding maximum likelihood estimator. Sampling distributions 'pile up' at unity when the true parameter is near unity; hence, if one were to difference such a process, estimates of the moving average component of the resulting series would spuriously tend to indicate that the process was overdifferenced. Flat-prior posterior distributions do not pile up, however, regardless of the parameter's proximity to unity; hence, caution should be taken in dismissing evidence that a series has been overdifferenced.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 11 (1993)
Issue (Month): 3 (July)
Pages: 311-17

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Handle: RePEc:bes:jnlbes:v:11:y:1993:i:3:p:311-17

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Cited by:
  1. Triantafyllopoulos, K. & Nason, G.P., 2007. "A Bayesian analysis of moving average processes with time-varying parameters," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 1025-1046, October.
  2. Frank Kleibergen & Henk Hoek, 1997. "Bayesian Analysis of ARMA Models using Noninformative Priors," Tinbergen Institute Discussion Papers 97-006/4, Tinbergen Institute.
  3. Derek Bond & Michael J. Harrison & Niall Hession & Edward J. O'Brien, 2006. "Some Empirical Observations on the Forward Exchange Rate Anomaly," Trinity Economics Papers tep2006, Trinity College Dublin, Department of Economics.
  4. Kim, Chang-Jin & Kim, Jaeho, 2013. "The `Pile-up Problem' in Trend-Cycle Decomposition of Real GDP: Classical and Bayesian Perspectives," MPRA Paper 51118, University Library of Munich, Germany.

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