A Projection-Based ARIMA Framework for Nonlinear Dynamics in Macroeconomic and Financial Time Series: Closed-Form Estimation and Rolling-Window Inference

TL;DR

This paper proposes Galerkin-ARIMA and Galerkin-SARIMA, projection-based extensions of classical ARIMA models, offering improved forecast accuracy and efficient rolling-window estimation for nonlinear macroeconomic and financial time series.

Contribution

It introduces a novel Galerkin-based framework for ARIMA models with closed-form estimation and inference, enhancing forecasting and analysis of nonlinear dynamics.

Findings

Galerkin-SARIMA matches or outperforms classical models in forecasts.

Closed-form two-stage estimator enables efficient rolling-window re-estimation.

Provides theoretical guarantees and bootstrap inference methods.

Abstract

We introduce Galerkin-ARIMA and Galerkin-SARIMA, a projection-based extension of classical ARIMA/SARIMA that replaces rigid linear lag operators with low-dimensional Galerkin basis expansions while preserving the familiar AR-MA decomposition. Experiments on synthetic series and on quarterly GDP and daily S&P 500 returns show that Galerkin-SARIMA matches or improves forecast accuracy relative to classical ARIMA/SARIMA. Estimation is closed-form via a two-stage least-squares procedure, and the closed-form two-stage estimator enables efficient rolling-window re-estimation while preserving the familiar AR-MA operator structure, facilitating applications in central bank forecasting and portfolio risk management. We establish approximation-estimation trade-offs under weak dependence, provide consistency and asymptotic distributional results for the unpenalized estimator, compare prediction risk to classical SARIMA, and propose information-criterion selection of basis size. We further develop bootstrap-based inference for exogenous factor blocks and block-bootstrap prediction intervals that account for serial dependence and the two-stage generated-regressor structure.