Using Subspace Algorithms for the Estimation of Linear State Space Models for Over-Differenced Processes

TL;DR

This paper investigates the use of subspace algorithms like CVA for estimating linear state space models in over-differenced processes, showing that over-differencing can impair estimator consistency and suggesting the use of unadjusted data.

Contribution

It extends the consistency results of subspace methods to over-differenced processes with spectral zeros, highlighting the negative impact of over-differencing on estimator properties.

Findings

Consistency of CVA estimators holds for spectral zeros in over-differenced processes.

Over-differencing can significantly reduce the convergence rate of estimators.

Using unadjusted data may preserve estimator accuracy in the presence of spectral zeros.

Abstract

Subspace methods like canonical variate analysis (CVA) are regression based methods for the estimation of linear dynamic state space models. They have been shown to deliver accurate (consistent and asymptotically equivalent to quasi maximum likelihood estimation using the Gaussian likelihood) estimators for invertible stationary autoregressive moving average (ARMA) processes. These results use the assumption that the spectral density of the stationary process does not have zeros on the unit circle. This assumption is violated, for example, for over-differenced series that may arise in the setting of co-integrated processes made stationary by differencing. A second source of spectral zeros is inappropriate seasonal differencing to obtain seasonally adjusted data. This occurs, for example, by investigating yearly differences of processes that do not contain unit roots at all seasonal frequencies. In this paper we show consistency for the CVA estimators for vector processes containing spectral zeros. The derived rates of convergence demonstrate that over-differencing can severely harm the asymptotic properties of the estimators making a case for working with unadjusted data.