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Optimal Forecast under Structural Breaks

Tae Hwy Lee, Shahnaz Parsaeian and Aman Ullah

No 202207, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: This paper develops an optimal combined estimator to forecast out-of-sample under structural breaks. When it comes to forecasting, using only the post-break observations after the most recent break point may not be optimal. In this paper we propose a new estimation method that exploits the pre-break information. In particular, we show how to combine the estimator using the full-sample (i.e., both the pre-break and post-break data) and the estimator using only the post-break sample. The full-sample estimator is inconsistent when there is a break while it is efficient. The post-break estimator is consistent but inefficient. Hence, depending on the severity of the breaks, the full-sample estimator and the post-break estimator can be combined to balance the consistency and efficiency. We derive the Stein-like combined estimator of the full-sample and the post-break estimators, to balance the bias-variance trade-o . The combination weight depends on the break severity, which we measure by the Wu-Hausman statistic. We examine the properties of the proposed method, analytically in theory, numerically in simulation, and also empirically in forecasting real output growth across nine industrial economies.

Keywords: Forecasting; Structural breaks; Stein-like combined estimator; Output growth (search for similar items in EconPapers)
JEL-codes: C13 C32 C53 (search for similar items in EconPapers)
Date: 2022-01
New Economics Papers: this item is included in nep-for and nep-ore
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Journal Article: Optimal forecast under structural breaks (2022) Downloads
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