Sequentially decoupling estimators for Box-Jenkins model estimation

TL;DR

This paper introduces a two-stage estimation method for Box-Jenkins models that is consistent, efficient, and applicable in various data conditions, offering an alternative to existing approaches.

Contribution

The paper proposes a novel sequentially decoupling estimator combined with Gauss-Newton refinement for improved Box-Jenkins model estimation.

Findings

The SD estimator is consistent under standard conditions.

One-step GN refinement achieves asymptotic efficiency.

Simulation results confirm theoretical properties.

Abstract

In this paper, we propose a consistent and asymptotically efficient estimation method for Box-Jenkins (BJ) models that is applicable under both open-loop and closed-loop data conditions, serving as a possible alternative to the weighted null-space fitting approach. The method comprises two stages: an initial sequentially decoupling (SD) estimator, followed by Gauss-Newton (GN) refinement step. The SD estimator is constructed from three sequential least squares (LS) estimators: (i) estimation of a high-order autoregressive model with exogenous inputs (ARX) model; (ii) estimation of the BJ model's dynamic model via an auxiliary output-error (OE) model; and (iii) estimation of the noise model of the BJ model using another auxiliary OE model. We establish the consistency of the SD estimator under standard regularity conditions, leveraging the consistency of the underlying LS estimators for both the ARX and OE models. Moreover, we show that one-step GN iteration starting from the SD estimator yields an estimator that is asymptotically equivalent to the prediction error method, provided the ARX model order satisfies a mild growth condition. Simulation studies confirm the theoretical properties of the proposed method.