Non-Regular Likelihood Inference for Seasonally Persistent Processes

TL;DR

This paper introduces a new likelihood method for estimating parameters in seasonally persistent processes, especially when the spectral pole is away from zero, demonstrating good finite-sample properties and asymptotic consistency.

Contribution

It develops a novel Whittle-type likelihood explicitly accounting for the pole location, improving estimation accuracy for seasonally persistent processes.

Findings

The new likelihood is computationally straightforward.

Establishes N-consistency for spectral pole estimators.

Shows superior small sample performance compared to existing methods.

Abstract

The estimation of parameters in the frequency spectrum of a seasonally persistent stationary stochastic process is addressed. For seasonal persistence associated with a pole in the spectrum located away from frequency zero, a new Whittle-type likelihood is developed that explicitly acknowledges the location of the pole. This Whittle likelihood is a large sample approximation to the distribution of the periodogram over a chosen grid of frequencies, and constitutes an approximation to the time-domain likelihood of the data, via the linear transformation of an inverse discrete Fourier transform combined with a demodulation. The new likelihood is straightforward to compute, and as will be demonstrated has good, yet non-standard, properties. The asymptotic behaviour of the proposed likelihood estimators is studied; in particular, $N$-consistency of the estimator of the spectral pole location is established. Large finite sample and asymptotic distributions of the score and observed Fisher information are given, and the corresponding distributions of the maximum likelihood estimators are deduced. A study of the small sample properties of the likelihood approximation is provided, and its superior performance to previously suggested methods is shown, as well as agreement with the developed distributional approximations.