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Pile-up probabilities for the Laplace likelihood estimator of a non-invertible first order moving average | | F. Jay Breidt
; Richard A. Davis
; Nan-Jung Hsu
; Murray Rosenblatt
; | | Date: |
26 Feb 2007 | | Journal: | IMS Lecture Notes Monograph Series 2006, Vol. 52, 1-19 | | Subject: | Statistics | | Abstract: | The first-order moving average model or MA(1) is given by $X_t=Z_t- heta_0Z_{t-1}$, with independent and identically distributed ${Z_t}$. This is arguably the simplest time series model that one can write down. The MA(1) with unit root ($ heta_0=1$) arises naturally in a variety of time series applications. For example, if an underlying time series consists of a linear trend plus white noise errors, then the differenced series is an MA(1) with unit root. In such cases, testing for a unit root of the differenced series is equivalent to testing the adequacy of the trend plus noise model. The unit root problem also arises naturally in a signal plus noise model in which the signal is modeled as a random walk. The differenced series follows a MA(1) model and has a unit root if and only if the random walk signal is in fact a constant. The asymptotic theory of various estimators based on Gaussian likelihood has been developed for the unit root case and nearly unit root case ($ heta=1+eta/n,etale0$). Unlike standard $1/sqrt{n}$-asymptotics, these estimation procedures have $1/n$-asymptotics and a so-called pile-up effect, in which P$(hat{ heta}=1)$ converges to a positive value. One explanation for this pile-up phenomenon is the lack of identifiability of $ heta$ in the Gaussian case. That is, the Gaussian likelihood has the same value for the two sets of parameter values $( heta,sigma^2)$ and $(1/ heta, heta^2sigma^2$). It follows that $ heta=1$ is always a critical point of the likelihood function. In contrast, for non-Gaussian noise, $ heta$ is identifiable for all real values. Hence it is no longer clear whether or not the same pile-up phenomenon will persist in the non-Gaussian case. In this paper, we focus on limiting pile-up probabilities for estimates of $ heta_0$ based on a Laplace likelihood. In some cases, these estimates can be viewed as Least Absolute Deviation (LAD) estimates. Simulation results illustrate the limit theory. | | Source: | arXiv, math/0702762 | | Services: | Forum | Review | PDF | Favorites | |
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